Method of handling busy traffic intersection in autonomous vehicle

Abstract

In one aspect, a method is described. The method comprises the steps that a control module determines the traffic condition around a vehicle in real time; the control module detects the movement of the vehicle towards the traffic light and the intersection area; the control module determines the time used by the traffic light cycle; determining, by the control module, an average distance traveled by the vehicle during the traffic light period; and calculating, by the control module, a probability that the vehicle passes through the intersection region before the red signal of the traffic light cycle based on the average distance traveled by the vehicle, the intersection region information, and the length of the vehicle. The vehicle may be an autonomous vehicle (autonomous vehicle), a semi-autonomous vehicle, or a manually driven vehicle.

Description

Technical Field

[0001] The present disclosure relates generally to the field of transportation. More specifically, the present disclosure relates to systems and methods for improving mobility and safety between vehicles at intersection areas. Background Art

[0002] Traffic congestion issues are the most pressing problem in many urban areas of Surat city. These issues are the main bottlenecks for the free movement of vehicles on roads and intersections. The traffic delay issues are mainly due to the increase in population and inefficient use of road sections. Road intersections are the main parts of road sections that play an important role in directing traffic movement. The various traffic congestion issues associated with the road network are traffic delays, environmental issues, increased costs due to increased fuel consumption, traffic safety issues, etc. Therefore, traffic congestion is the result of the gap between the supply and demand of traffic. Urban road intersections are the main bottlenecks and the main strike sites for traffic delays. This is because at road intersections vehicles perform different maneuvering movements like left turn, right turn, U-turn, and vehicles and pedestrians seek the same space at the same time. Therefore, urban road intersections are the main critical points from the perspective of safe capacity and delay and efficiency. " [Source: "A CRITICAL STUDY OF ROADINTERSECTIONS IN THE SOUTH EAST PART OF SURAT CITY", published in April 2018]

[0003] Therefore, there has long been a need for a system and method to monitor traffic conditions and provide driving assistance to a vehicle and nearby vehicles to improve traffic conditions in intersection areas. Summary of the invention

[0004] The following provides a basic understanding of one or more embodiments described herein. This overview is not intended to identify important or critical elements or to limit any scope of the different embodiments and / or any scope of the claims. The sole purpose of this overview is to present some concepts in a simplified form as a prelude to a more detailed description presented herein.

[0005] In one or more embodiments described herein, systems, devices, computer-implemented methods, methods, apparatus, and / or computer program products are provided that facilitate handling of heavy traffic intersections in an autonomous vehicle.

[0006] In one aspect, a method is described. The method includes: determining the traffic conditions around a vehicle in real time by a control module; detecting the movement of the vehicle toward a traffic (signal) light and an intersection area by the control module; determining the time taken for a traffic light cycle by the control module; determining the average distance traveled by the vehicle during the traffic light cycle by the control module; and calculating the probability of the vehicle crossing the intersection area before the red signal of the traffic light cycle by the control module through a calculation engine based on the average distance traveled by the vehicle, the intersection area information and the length of the vehicle. When it is calculated that the vehicle crosses the intersection area before the red signal of the traffic light cycle, the probability of the vehicle crossing the intersection area is set to true. When it is calculated that the vehicle blocks the intersection area, the probability of the vehicle crossing the intersection area is set to false.

[0007] In one embodiment, the calculated probability that the vehicle passes through the intersection area is set to true further includes: calculating that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle, and the rear vehicle behind the vehicle blocks the intersection area.

[0008] In one embodiment, the method further comprises: transmitting, by the control module, a first message to nearby vehicles when it is calculated that the vehicle is the last vehicle to pass through the intersection area before a red signal of a traffic light cycle.

[0009] In one embodiment, the first message communicates a command to the nearby vehicle before the traffic light provides a signal to the nearby vehicle.

[0010] In one embodiment, the commands to the nearby vehicles enable continuous mobility and improve the traffic conditions.

[0011] In one embodiment, the method further comprises: when the probability that the vehicle passes through the intersection area is set to true, providing, by the control module, a first command to pass through the intersection area to an electric drive unit of the vehicle.

[0012] In one embodiment, the method further comprises: providing, by the control module, a second command to the electric drive unit of the vehicle to stop before the intersection area when the probability that the vehicle passes through the intersection area is set to false.

[0013] In one embodiment, the first command includes the following details: speed, direction, lane, a first distance between the vehicle and a vehicle in front, a second distance between the vehicle and a vehicle behind, and lane change information.

[0014] In one embodiment, the second command includes the following details: a lane, a first distance between the vehicle and an intersection area, a second distance between the vehicle and a rear vehicle, and lane change information.

[0015] In one embodiment, determining the traffic conditions around the vehicle in real time includes: monitoring the speed of the vehicle through a sensor; comparing the speed of the vehicle with a threshold speed; and when the speed of the vehicle is less than the threshold speed, determining the traffic conditions around the vehicle in real time.

[0016] In one embodiment, determining traffic conditions around a vehicle in real time includes: capturing an image within a threshold space around the vehicle; performing image processing on the image, wherein the image processing includes object detection; determining the number of vehicles around the vehicle from the image; and when the number of vehicles is greater than a threshold number of vehicles, determining traffic conditions around the vehicle in real time.

[0017] In one embodiment, determining the traffic conditions around the vehicle in real time includes: monitoring the speed of nearby vehicles through sensors; comparing the speed of the nearby vehicles with a threshold speed; and when the speed of the vehicle is less than the threshold speed, determining the traffic conditions around the vehicle in real time.

[0018] In one embodiment, determining the traffic conditions around the vehicle in real time includes: acquiring the traffic conditions around the vehicle from a third-party service in real time.

[0019] In one embodiment, determining the traffic conditions around a vehicle in real time includes: retrieving pre-stored traffic conditions from a traffic information database based on one of a current time and a real-time positioning of the vehicle; determining through a computing engine whether the current time is one of rush hour and working hours; and dynamically updating the pre-stored traffic conditions through the computing engine based on the determination of whether the current time is one of rush hour and working hours.

[0020] In one embodiment, determining traffic conditions around the vehicle includes calculating traffic conditions using statistical data analysis of a history of pre-stored traffic conditions from a traffic information database.

[0021] In one embodiment, detecting, by the control module, movement of the vehicle toward a traffic light and intersection area includes: detecting, by the computing engine, movement of the vehicle toward a traffic light and intersection area using video analysis.

[0022] In one embodiment, detecting, by the control module, the movement of the vehicle toward the traffic light and intersection area includes: detecting, by the computing engine, the movement of the vehicle toward the traffic light and intersection area by performing video analysis of real-time video.

[0023] In one embodiment, detecting the movement of a vehicle toward a traffic light and an intersection area by the control module includes: performing image processing on an image by the computing engine; scanning the image and identifying one of a traffic light, an intersection area, a red signal, a green signal, and a yellow signal from the image; and detecting the movement of the vehicle toward the traffic light and the intersection area based on the scanning and identification, wherein the image includes an area of ​​interest around the vehicle, the area of ​​interest covering the traffic light and the intersection area; and wherein the image includes the vehicle and nearby vehicles.

[0024] In one embodiment, determining the traffic light cycle time includes retrieving the traffic light cycle time from a traffic information database based on the current time and the real-time location of the vehicle.

[0025] In one embodiment, determining the time taken for a traffic light cycle includes: performing video analysis on a video by the computing engine; scanning the video and detecting intervals between a green signal and a red signal of a traffic light, and vice versa; and determining the time taken for the traffic light cycle based on the intervals, wherein the video includes a frame sequence of an area of ​​interest around the vehicle within a predetermined time period; and wherein the video includes a frame sequence including the vehicle and the traffic light.

[0026] In one embodiment, the traffic light cycle includes indications of a green signal, a yellow signal and a red signal.

[0027] In one embodiment, determining by the control module an average distance traveled by a vehicle during a traffic light cycle includes: determining a first length moved by the vehicle during a first traffic light cycle; determining a second length moved by the vehicle during a second traffic light cycle; determining a third length moved by the vehicle during a third traffic light cycle; and calculating the average distance traveled by the vehicle during the traffic light cycle based on the first length, the second length, and the third length.

[0028] In one embodiment, the control module calculates the probability of a vehicle crossing an intersection area before the red signal of a traffic light cycle based on the average distance traveled by the vehicle, the intersection area information and the length of the vehicle through a computing engine, including: obtaining intersection area information from a traffic information database and a third-party service; identifying the current signal of the traffic light cycle in real time; and estimating the probability of the vehicle crossing the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle during the traffic light cycle, the intersection area information, the current signal of the traffic light cycle and the length of the vehicle.

[0029] In one embodiment, the control module calculates the probability of the vehicle crossing the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle, the intersection area information and the length of the vehicle through a computing engine, including: obtaining the intersection area information, the length of the pedestrian crossing and the length of the bicycle box from a traffic information database and a third-party service; identifying the current signal of the traffic light cycle in real time; and estimating the probability of the vehicle crossing the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle during the traffic light cycle, the intersection area information, the current signal of the traffic light cycle, the length of the pedestrian crossing, the length of the bicycle box and the length of the vehicle.

[0030] In one embodiment, calculating that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle further comprises: transmitting a notification from the vehicle to the following vehicle, wherein the notification comprises a message indicating a countdown for the traffic light to switch to the red signal.

[0031] In one embodiment, calculating that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle also includes: transmitting a notification from the vehicle to the vehicle in front, wherein the notification includes a message instructing maneuver guidance of the vehicle in front to improve traffic conditions around the intersection area.

[0032] In one embodiment, the method further comprises: based on the first command, maneuvering the vehicle by the electric drive unit to cross the intersection area before a red signal of a traffic light.

[0033] In one embodiment, the method further includes: capturing a video of an area of ​​interest in front of the vehicle in real time, the area of ​​interest covering the vehicle and the intersection area; identifying an end point of the intersection area from the video in real time; and determining that the vehicle has passed through the intersection area when the end point of the intersection area passes through the video of the area of ​​interest.

[0034] In one embodiment, the method further includes: measuring the distance traveled by the vehicle before the end point of the intersection area; comparing the length of the vehicle and the distance traveled by the vehicle; and when the distance traveled by the vehicle is greater than the length of the vehicle, determining that the vehicle has passed the intersection area.

[0035] In one embodiment, the method further includes: measuring the distance traveled by the vehicle before the end point of the intersection area; comparing the length of the vehicle and the distance traveled by the vehicle; and when the distance traveled by the vehicle is less than the length of the vehicle, determining that the vehicle is blocking the intersection area.

[0036] In one embodiment, the method further includes: when it is determined that the vehicle is blocking an intersection area, measuring the distance between the vehicle and a preceding vehicle in the lane of the vehicle and an adjacent lane of the vehicle; comparing the distance to the length of the vehicle; and transmitting a third command to the electric drive unit to cause it to perform one of the following: moving forward in the lane to a predetermined distance, and performing a lane change when the measured distance is x times the length of the vehicle.

[0037] In one embodiment, the method further includes: obtaining the distance traveled by the vehicle after passing through the intersection area; detecting whether there is a rear vehicle behind the vehicle; detecting that the rear vehicle is in the intersection area; obtaining the model and length of the rear vehicle; and determining the scope of the intersection area where the rear vehicle is blocking the intersection area based on the distance traveled by the vehicle after passing through the intersection area, the length of the rear vehicle, the length of the vehicle, and the distance between the vehicle and the rear vehicle.

[0038] In one embodiment, determining the scope of the intersection area where the rear vehicle is blocking the intersection area further includes: when the rear vehicle is blocking the intersection area, transmitting one of a first signal and a first notification to the front vehicle to make it move forward; monitoring the movement of the front vehicle; in response to the movement of the front vehicle, controlling the vehicle through an electric drive unit; and transmitting one of a second signal and a second notification to the rear vehicle to make it move forward to a predetermined position to minimize the obstruction of the intersection area.

[0039] In one embodiment, determining the traffic conditions around the vehicle in real time by the control module includes one of the following: receiving real-time traffic data from a source; determining at least one of the speed, distance and direction of nearby vehicles in a route; using a video analysis unit to detect around the vehicle: a count of nearby vehicles and corresponding directions; determining that the nearby vehicles have difficulty in navigation; and receiving traffic conditions from the nearby vehicles.

[0040] In one embodiment, the source includes at least one of a closed circuit television (CCTV) system, a third party service, a traffic flow sensor, a toll payment device, mobile phone data, and connected car data.

[0041] In one embodiment, the first message includes maneuvering guidance for the nearby vehicles.

[0042] In one embodiment, the communication between the vehicle, the leading vehicle, and the trailing vehicle is via vehicle-to-vehicle (V2V) communication.

[0043] In one embodiment, the vehicle comprises one of an autonomous vehicle, a semi-autonomous vehicle, and a manually driven vehicle.

[0044] In one embodiment, the V2V communication is based on a wireless communication protocol using at least one of Dedicated Short Range Communication (DSRC) and Cellular Vehicle-to-Everything (C-V2X) technology.

[0045] In one embodiment, the communication between the vehicle, the leading vehicle and the following vehicle is via an Internet connection.

[0046] In one embodiment, the computing engine comprises an artificial intelligence engine.

[0047] In one embodiment, the artificial intelligence engine includes a machine learning algorithm.

[0048] In another aspect, a system is described. The system includes a communication module and a control module. The control module is operable to determine traffic conditions around a vehicle in real time; detect movement of the vehicle toward a traffic light and an intersection area; determine the time taken for a traffic light cycle; determine the average distance traveled by the vehicle during the traffic light cycle; and calculate, by a calculation engine, the probability that the vehicle passes through the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle, the intersection area information, and the length of the vehicle. When it is calculated that the vehicle passes through the intersection area before the red signal of the traffic light cycle, the probability that the vehicle passes through the intersection area is set to true. When it is calculated that the vehicle blocks the intersection area, the probability that the vehicle passes through the intersection area is set to false.

[0049] In one embodiment, the system is configured as a component of the vehicle.

[0050] In one embodiment, the control module is operable to calculate that the vehicle is the last vehicle to pass through the intersection area before a red signal of a traffic light cycle.

[0051] In one embodiment, the control module is operable to transmit a first message to nearby vehicles upon calculating that the vehicle is the last vehicle to pass through the intersection area before a red signal of a traffic light cycle.

[0052] In one embodiment, the first message communicates a command to the nearby vehicle before the traffic light provides a signal to the nearby vehicle.

[0053] In one embodiment, the command to the nearby vehicles enables continuous mobility of at least one of the vehicle and the nearby vehicles and improves traffic conditions.

[0054] In one embodiment, the control module is operable to provide a first command to an electric drive unit of the vehicle to pass through an intersection area when the probability that the vehicle passes through the intersection area is set to true.

[0055] In one embodiment, the control module is operable to provide a second command to an electric drive unit of the vehicle to stop before the intersection area when the probability of the vehicle crossing the intersection area is set to true.

[0056] In one embodiment, the communication module is enabled for at least one of vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, vehicle-to-everything (V2X) communication, and vehicle-to-network communication.

[0057] In one embodiment, the vehicle-to-vehicle (V2V) communication is based on a wireless communication protocol using at least one of dedicated short range communication (DSRC) and cellular vehicle-to-everything (C-V2X) technology.

[0058] In one embodiment, the V2X communication includes at least one of vehicle to network (V2N), vehicle to grid (V2G), vehicle to device (V2D) and vehicle to pedestrian (V2P).

[0059] In one embodiment, communicating includes transmitting at least one of data, instructions, messages, and information.

[0060] In one embodiment, the communication module supports communication protocols, wherein the communication protocols include at least one of the following: Advanced Message Queuing Protocol (AMQP), Message Queuing Telemetry Transport (MQTT) protocol, Simple (or Streaming) Text-Oriented Message Protocol (STOMP), Zigbee protocol, Unified Diagnostic Service (UDS) protocol, Open Diagnostic Exchange Format (ODX) protocol, Internet Protocol Diagnostics (DoIP), On-Board Diagnostics (OBD) protocol, and predefined protocol standards.

[0061] In one embodiment, the system includes a computation engine.

[0062] In one embodiment, the computational engine includes a machine learning model.

[0063] In one embodiment, the machine learning model is configured to learn using labeled data using a supervised learning method, wherein the supervised learning method includes using decision trees, logistic regression, support vector machines, k-nearest neighbors, A logic of at least one of Bayes, random forest, linear regression, polynomial regression, and support vector machine for regression.

[0064] In one embodiment, the machine learning model is configured to learn from real-time data using an unsupervised learning method, wherein the unsupervised learning method includes logic using at least one of k-means clustering, hierarchical clustering, hidden Markov models, and a priori algorithms.

[0065] In one embodiment, the machine learning model has a feedback loop where the output from the previous step is fed back to the machine learning model in real time to improve the performance and accuracy of the output of the next step.

[0066] In one embodiment, the machine learning model comprises a recursive neural network model.

[0067] In one embodiment, the machine learning model has a feedback loop where for each true output of the system, the learning is further reinforced.

[0068] In yet another aspect, a non-transitory computer-readable medium. The non-transitory computer-readable medium stores a sequence of instructions that, when executed by a processor, results in: determining traffic conditions around a vehicle in real time; detecting movement of the vehicle toward a traffic light and an intersection area; determining the time taken for a traffic light cycle; determining an average distance traveled by the vehicle during the traffic light cycle; and calculating, by a calculation engine, the probability that the vehicle crosses the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle, the intersection area information, and the length of the vehicle. When it is calculated that the vehicle crosses the intersection area before the red signal of the traffic light cycle, the probability that the vehicle crosses the intersection area is set to true. When it is calculated that the vehicle blocks the intersection area, the probability that the vehicle crosses the intersection area is set to false.

[0069] In one embodiment, the non-transitory computer readable medium further causes: calculating that the vehicle is the last vehicle to pass through the intersection area before a red signal of a traffic light cycle.

[0070] In one embodiment, the non-transitory computer readable medium further causes: transmitting a first message to nearby vehicles when it is calculated that the vehicle is the last vehicle to pass through the intersection area before a red signal of a traffic light cycle.

[0071] In one embodiment, the non-transitory computer readable medium further causes: when the probability that the vehicle crosses the intersection area is set to true, providing a first command to an electric drive unit of the vehicle to pass through the intersection area.

[0072] In one embodiment, the non-transitory computer readable medium further causes providing a second command to an electric drive unit of the vehicle to stop before the intersection area when the probability of the vehicle crossing the intersection area is set to false.

[0073] The methods and systems disclosed herein can be implemented in any means for implementing various aspects, and can be executed in the form of a non-transitory machine-readable medium containing a set of instructions that, when executed by a machine, cause the machine to perform any operations disclosed herein. Other features will be apparent from the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] These and other aspects of the present disclosure will now be described in more detail with reference to the accompanying drawings showing exemplary embodiments, in which:

[0075] Figure 1A-1B The traffic conditions in the intersection area are shown.

[0076] Figure 2 A method of processing a heavily trafficked intersection area according to one or more embodiments is shown.

[0077] Figure 3 Detection of traffic conditions according to one or more embodiments is shown.

[0078] Figure 4 Determination of the elapsed time for a traffic light cycle is shown in accordance with one or more embodiments.

[0079] Figure 5 Determination of an average distance traveled by a vehicle during a traffic light cycle is shown in accordance with one or more embodiments.

[0080] Figures 6A-6D Determination of the probability of a vehicle crossing an intersection area prior to a red signal in a traffic light cycle is shown in accordance with one or more embodiments.

[0081] Figure 7 Communications between vehicle 702 and a following vehicle are shown according to one or more embodiments.

[0082] Figures 8A-8B Determination of a vehicle passing through an intersection area according to one or more embodiments is shown.

[0083] Figures 9A-9B Lane detection according to one or more embodiments is shown.

[0084] Fig.10 Determination of a probability of a rear vehicle behind a vehicle crossing an intersection area is shown in accordance with one or more embodiments.

[0085] Figures 11A-11C Communication between a vehicle and nearby vehicles to improve traffic conditions is shown according to one or more embodiments.

[0086] Fig.12 A system according to one or more embodiments is shown.

[0087] Fig.13 A non-transitory computer readable medium is shown in accordance with one or more embodiments.

[0088] Fig.14 A block diagram of electronic components of a vehicle according to an embodiment is shown.

[0089] Fig.15 An example message of a dedicated short range communication (DSRC) message format is shown according to an embodiment.

[0090] Fig.16A Shown is the structure of a neural network / machine learning model with a feedback loop.

[0091] Fig. 16B The structure of a neural network / machine learning model with reinforcement learning is shown.

[0092] Fig.17A A block diagram of the network security module from the perspective of the system and the server is shown.

[0093] Fig. 17B An embodiment of a network security module is shown.

[0094] Fig. 17C Another embodiment of a network security module is shown.

[0095] Other features of the embodiments will be apparent from the accompanying drawings and the detailed description that follows. DETAILED DESCRIPTION

[0096] For simplicity and clarity of explanation, these drawings show the general construction mode. The description and drawings may omit the description and details of known features and technologies to avoid unnecessary confusion of the present disclosure. The dimensions of some elements in these drawings are exaggerated relative to other elements to help improve the understanding of the embodiments of the present disclosure. The same reference numerals in different drawings represent the same elements.

[0097] Although the detailed description herein contains many specifics for the purposes of illustration, one of ordinary skill in the art will appreciate that many variations and modifications to those details are considered to be included herein.

[0098] Therefore, the embodiments herein do not lose generality or generality to any claims presented, nor do they impose any limitations. The terms used herein are only used to describe specific embodiments and are not restrictive. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs.

[0099] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0100] As used herein, the absence of a quantifier modifier means that the grammatical object has one or more than one or more than one (i.e., at least one). For example, "element" means one element or more than one element. In addition, unless otherwise specified or the context clearly indicates a singular form, the absence of a quantifier in this specification and the drawings is interpreted as meaning "one or more".

[0101] As used herein, the terms "example" and / or "exemplary" refer to serving as an example, instance, or illustration. For the avoidance of doubt, such examples do not limit the subject matter described herein. In addition, any aspect or design described herein as "example" and / or "exemplary" is not necessarily preferred or advantageous over other aspects or designs, nor does it exclude equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0102] As used herein, the terms "first", "second" and "third" etc. (if any) in the specification and claims distinguish similar elements and do not necessarily describe a specific order or chronological order. These terms are interchangeable where appropriate, so that the embodiments of this article can, for example, operate in an order different from that shown or otherwise described herein. In addition, the terms "including", "having" and any variants thereof cover non-exclusive inclusion, and thus the process, method, system, article, equipment or device including a list of elements must not be limited to those elements, but may include other elements that are not explicitly listed or inherent to such processes, methods, systems, articles, equipment or devices.

[0103] As used herein, the terms "left", "right", "front", "back", "top", "bottom", "upper", "lower", etc., if any, in the specification and claims are used for descriptive purposes and not necessarily for describing permanent relative positions. The terms so used are interchangeable where appropriate, such that the embodiments of the apparatus, methods, and / or articles of manufacture described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein.

[0104] Unless explicitly stated, any element action or instruction used in this article is not critical or necessary. In addition, the term "set" includes (plural) items (e.g., related items, unrelated items, combinations of related items and unrelated items, etc.), and can be interchanged with "one or more". If it is intended to express only one item, the term "one" or similar language is used. In addition, the term "having" is an open term. In addition, unless otherwise explicitly stated, "based on" means "based at least in part on".

[0105] As used herein, the terms "system", "device", "unit" and / or "module" refer to different components, component parts or components at various levels in the order. However, other expressions that achieve the same purpose can replace these terms.

[0106] As used herein, the terms "coupled" and the like refer to connecting two or more elements mechanically, electrically and / or otherwise. Two or more electrical elements may be electrically coupled together, rather than mechanically or otherwise coupled together. The coupling may have any length of time, for example, permanent, or semi-permanent, or only momentary. "Electrically coupled" includes all types of electrical couplings. The absence of the words "removably," "removable," etc. near the words "coupled" and the like does not mean that the coupling in question is removable or not removable.

[0107] As used herein, the term "or" refers to an inclusive "or" rather than an exclusive "or". That is, unless specified otherwise or clear from the context, "X employs A or B" refers to any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then "X employs A or B" is satisfied in any of the above instances.

[0108] As used herein, two or more elements or modules are "integral" or "integrated" if they work together functionally. Two or more elements are "non-integral" if each element is capable of operating independently functionally.

[0109] As used herein, the term "real time" refers to operations that are performed as quickly as possible after a triggering event occurs. A triggering event may include receiving data necessary to perform a task or otherwise process information. Due to delays inherent in transmission and / or computing speeds, the term "real time" includes operations that occur "close to" real time or operations that are slightly delayed from a triggering event. In many embodiments, "real time" may refer to real time minus the time delay for processing (e.g., determining) and / or transmitting data. The specific time delay may vary depending on the type and / or amount of data, the processing speed of the hardware, the transmission capabilities of the communication hardware, the transmission distance, etc. However, in many embodiments, the time delay may be less than approximately one second, two seconds, five seconds, or ten seconds.

[0110] As used herein, the term "approximately" may refer to being within a specified or unspecified range of a specified or unspecified claimed value. In some embodiments, "approximately" may refer to being within plus or minus ten percent of a claimed value. In other embodiments, "approximately" may refer to being within plus or minus five percent of a claimed value. In further embodiments, "approximately" may refer to being within plus or minus three percent of a claimed value. In still further embodiments, "approximately" may refer to being within plus or minus one percent of a claimed value.

[0111] Digital electronic circuits, or computer software, firmware or hardware, including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them, can implement the embodiments and all functional operations described in this specification. The embodiments may be one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer-readable medium, for execution by a data processing device or for controlling the operation of a data processing device. The computer-readable medium may be a machine-readable storage device, a machine-readable storage substrate, a storage device, a composition of matter that affects a machine-readable propagation signal, or a combination of one or more of them. The term "computing system" includes all devices, devices and machines for processing data, for example, including a programmable processor, a computer or multiple processors or computers. In addition to hardware, the device may also include code that creates an execution environment for the computer program in question, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal (e.g., a machine-generated electrical, optical or electromagnetic signal) that encodes information for transmission to a suitable receiver device.

[0112] The actual dedicated control hardware or software code used to implement these systems and / or methods is not limited to these embodiments. Therefore, any software and any hardware can implement the system and / or method based on the description of this article without reference to specific software codes.

[0113] A computer program (also referred to as a program, software, software application, script, or code) is written in any suitable form of a programming language, including compiled or interpreted languages. It may be deployed in any suitable form, including a stand-alone program or a module, component, subroutine, or other unit suitable for a computing environment. A computer program does not necessarily correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or portions of code). A computer program may be executed on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communications network.

[0114] One or more programmable processors, executing one or more computer programs to perform functions by operating on input data and generating output, perform the processes and logic flows described in this specification. The processes and logic flows may also be performed by special purpose logic circuits, and the device may also be implemented as special purpose logic circuits, such as, but not limited to, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on chip (SOC) systems, complex programmable logic devices (CPLDs), etc.

[0115] For example, processors suitable for executing computer programs include both general and special microprocessors, and any one or more processors of digital computers of any appropriate type. The processor receives instructions and data from a read-only memory or a random access memory or both. The elements of a computer may include a processor for executing instructions and one or more memory devices for storing instructions and data. The computer also includes one or more large-capacity storage devices for storing data, such as a magnetic disk, a magneto-optical disk, an optical disk or a solid-state disk, or is operatively coupled to receive data, transfer data or both to / from the above-mentioned one or more large-capacity storage devices. However, the computer does not need to have such a device. In addition, another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio player, a global positioning system (GPS) receiver, etc., can be embedded in the computer. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and storage devices, including, for example, semiconductor memory devices (e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks (e.g., compact disk read-only memory (CD ROM) disks, digital versatile disk read-only memory (DVD-ROM) disks), and solid-state disks. Special purpose logic circuits may supplement or be combined with the processor and memory.

[0116] To provide interaction with a user, a computer may have a display device for displaying information to the user, such as a cathode ray tube (CRT) or liquid crystal display (LCD) monitor, and a keyboard and pointing device, such as a mouse or trackball, through which the user can provide input to the computer. Other types of devices also provide interaction with the user. For example, feedback to the user can be any suitable form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and the computer can receive any suitable form of input from the user, including acoustic, voice, or tactile input.

[0117] The embodiments described herein can be implemented by a computing system including a back-end component, such as a data server, or a middleware component, such as an application server, or a front-end component, such as a client computer with a graphical user interface or a web browser, through which a user can interact with the embodiment, or any suitable combination of one or more such back-end components, middleware components, or front-end components. Any suitable form or medium of digital data communication, such as a communication network, can interconnect the components of the system. Examples of communication networks include local area networks (LANs) and wide area networks (WANs), such as intranets and the Internet.

[0118] A computing system may include clients and servers. The clients and servers are remote from each other and typically interact through a communication network. The relationship between the client and the server arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0119] Embodiments may include or utilize a special or general purpose computer including computer hardware. Embodiments within the scope of the present invention may also include physical media and other computer readable media for carrying or storing computer executable instructions and / or data structures. Such computer readable media may be any medium accessible to a general or special purpose computer system. A computer readable medium storing computer executable instructions is a physical storage medium. A computer readable medium carrying computer executable instructions is a transmission medium. Thus, by way of example and not limitation, embodiments of the present invention may include at least two different types of computer readable media: a physical computer readable storage medium and a transmission computer readable medium.

[0120] Although the present embodiments described herein refer to specific example embodiments, it will be apparent that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the various embodiments. For example, hardware circuits (e.g., complementary metal oxide semiconductor (CMOS) based logic circuits), firmware, software (e.g., embodied in a non-transitory machine-readable medium), or any combination of hardware, firmware, and software may enable and operate the various devices, units, and modules described herein. For example, transistors, logic gates, and circuits (e.g., application specific integrated circuits (ASICs) and / or digital signal processor (DSP) circuits) may embody various electrical structures and methods.

[0121] Furthermore, non-transitory machine-readable media and / or systems may embody various operations, processes, and methods disclosed herein.Accordingly, the specification and drawings are illustrative rather than restrictive.

[0122] Physical computer-readable storage media include RAM, ROM, EEPROM, CD-ROM or other optical disk storage (such as CD, DVD, etc.), magnetic disk storage or other magnetic storage devices, solid-state disks, or any other media. They store the required program code in the form of computer-executable instructions or data structures that can be accessed by general-purpose or special-purpose computers.

[0123] As used herein, the term "network" may include the Internet, a local area network, a wide area network, or a combination thereof. The network may include one or more networks or communication systems, such as the Internet, a telephone system, a satellite network, a cable television network, and various other private and public networks. In addition, the connection may include a wired connection (such as a wire, cable, fiber optic line, etc.), a wireless connection, or a combination thereof. In addition, although not shown, other computers, systems, devices, and networks may also be connected to the network. A network refers to any group of devices or subsystems connected by links that (directly or indirectly) connect a group of terminal nodes that share resources located on or provided by network nodes. Computers communicate with each other using a common communication protocol through digital interconnection. For example, a subsystem may include a cloud. A cloud refers to servers accessed on the Internet, as well as software and databases running on these servers.

[0124] Furthermore, upon reaching various computer system components, program code in the form of computer executable instructions or data structures may be automatically transferred from a transmission computer readable medium to a physical computer readable storage medium (or vice versa). For example, computer executable instructions or data structures received over a network or data link may be buffered in RAM within a network interface module (NIC) and then ultimately transferred to computer system RAM and / or a less volatile computer readable physical storage medium at the computer system. Thus, computer system components that also (or even primarily) utilize transmission media may include computer readable physical storage media.

[0125] Computer executable instructions include, for example, instructions and data that cause a general purpose computer, a special purpose computer, or a special purpose processing device to perform a specific function or group of functions. Computer executable instructions may be, for example, instructions in binary, an intermediate format, such as assembly language, or even source code. Although the subject matter described herein is language specific to structural features and / or methodological behavior, the described features or described behaviors do not limit the subject matter defined in the claims. Rather, the features and behaviors described herein are example forms of implementing the claims.

[0126] Although this specification contains many details, these details are not to be construed as limitations on the scope of the present disclosure or the claims, but rather as descriptions of features of particular embodiments. A single embodiment may implement certain features described in this specification in the context of a separate embodiment. Conversely, multiple implementations may implement the various features described herein in the context of a single implementation, either individually or in any suitable subcombination. Furthermore, although the features described herein function in certain combinations and are even initially claimed, in some cases one or more features from a claimed combination may be deleted from the combination, and a claimed combination may refer to a subcombination or a variation of a subcombination.

[0127] Similarly, although the operations shown in the figure achieve the expected results in a specific order, this should not be understood as requiring such operations to be performed in the specific order or sequential order shown, or requiring all shown operations to be performed to achieve the expected results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system components in the embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can be integrated in a single software product or packaged in multiple software products.

[0128] Even though particular combinations of features are listed in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. Other implementations are also within the scope of the claims. For example, the actions recited in the claims can be performed in a different order and still achieve the desired results. In fact, many of these features can be combined in ways not specifically listed in the claims and / or disclosed in the specification. Although each dependent claim may be directly dependent on only one claim, the disclosure of possible implementations includes the combination of each dependent claim with every other claim of the claim group.

[0129] In addition, a computer system including one or more processors and a computer-readable medium (such as a computer memory) can practice these methods. In particular, one or more processors execute computer-executable instructions stored in the computer memory to perform various functions, such as the actions described in the embodiments.

[0130] Those skilled in the art will appreciate that the present invention can be practiced in a network computing environment with many types of computer system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, microcomputers, mainframe computers, mobile phones, PDAs, pagers, routers, switches, etc. A distributed system environment in which both local and remote computer systems that are linked by a network (by a combination of hardwired data links, wireless data links, or hardwired data links and wireless data links) perform tasks can also practice the present invention. In a distributed system environment, program modules can be located in both local and remote memory storage devices.

[0131] Unless otherwise indicated, the following terms and phrases shall have the following meanings.

[0132] As used herein, the term "cryptographic protocol" is also referred to as a security protocol or encryption protocol. It is an abstract or concrete protocol that performs security-related functions and applies cryptographic methods, usually as a sequence of cryptographic primitives. A protocol describes the use of an algorithm. A sufficiently detailed protocol includes details about data structures and representations to enable multiple interoperable versions of a program. Cryptographic protocols are widely used for secure application-level data transmission. Cryptographic protocols typically include at least some of the following aspects: key agreement or establishment, entity authentication, symmetric encryption and message authentication, secure application-level data transmission, non-repudiation methods, secret sharing methods, and secure multi-party computation. Hash algorithms can be used to verify the integrity of data. Secure Sockets Layer (SSL) and Transport Layer Security (TLS), the successor to SSL, are cryptographic protocols that network switches can use to ensure the security of data communications on a network.

[0133] Cryptographic protocols are widely used for secure application-level data transmission. Cryptographic protocols usually include at least some of the following aspects: key agreement or establishment, entity authentication, construction of symmetric encryption and message authentication material, secure application-level data transmission, non-repudiation methods, secret sharing methods, and secure multi-party computation.

[0134] Network switches use cryptographic protocols such as Secure Sockets Layer (SSL) and Transport Layer Security (TLS), SSL's successor, to protect data communications on wireless networks.

[0135] As used herein, the term "unauthorized access" means someone using another person's account or other method to gain access to a website, program, server, service, or other system. For example, if someone keeps guessing a password or username for an account that is not theirs before gaining access, that is considered unauthorized access.

[0136] As used herein, the term “IoT” stands for Internet of Things, which describes a network of physical “things” or objects embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet.

[0137] As used herein, "machine learning" refers to an algorithm that gives a computer the ability to learn without explicit programming, including algorithms that learn from data and predict data. Machine learning techniques include, but are not limited to, support vector machines, artificial neural networks (ANN) (also referred to herein as "neural networks"), deep learning neural networks, logistic regression, discriminant analysis, random forests, linear regression, rule-based machine learning, Naive Bayes, nearest neighbors, decision trees, decision tree learning, and hidden Markov, etc. For clarity, part of the machine learning process can use algorithms such as linear regression or logistic regression. However, using linear regression or another algorithm as part of the machine learning process is different from using a spreadsheet program to perform statistical analysis (such as regression). The machine learning process can continuously learn and adjust the classifier as new data becomes available, and does not rely on explicit or rule-based programming. ANN can be characterized by having a feedback loop to dynamically adjust the system output when learning from new data as new data becomes available. In machine learning, back propagation and feedback loops are used to train AI / ML models to improve the accuracy and performance of the model over time.

[0138] Statistical modeling relies on finding relationships (e.g., mathematical equations) between variables to predict outcomes.

[0139] As used herein, the term "data mining" is the process used to transform raw data into useful information.

[0140] As used herein, the term "data acquisition" is the process of sampling signals that measure real-world reality and converting the resulting samples into digital values ​​that can be manipulated by a computer. Data acquisition systems typically convert analog waveforms into digital values ​​for processing. The components of a data acquisition system include sensors that convert physical parameters into electrical signals, signal conditioning circuits that convert sensor signals into a form that can be converted into digital values, and analog-to-digital converters that convert conditioned sensor signals into digital values. Standalone data acquisition systems are often referred to as data loggers.

[0141] As used herein, the term "dashboard" is a type of interface that visualizes specific key performance indicators (KPIs) for a specific goal or process. It is based on data visualization and information graphics.

[0142] As used herein, a "database" is a collection of information organized so that it can be easily accessed, managed, and updated. A computer database typically contains an aggregation of data records or files.

[0143] As used herein, the term "dataset" (or "dataset") is a collection of data. In the case of tabular data, a dataset corresponds to one or more database tables, where each column of the table represents a particular variable and each row corresponds to a given record of the dataset in question. The dataset lists the value of each variable for each member of the dataset, such as the height and weight of an object. Each value is called a datum. A dataset may also consist of a collection of documents or files.

[0144] As used in this article, a "sensor" is a device that measures physical input from its environment and converts it into data that can be interpreted by humans or machines. Most sensors are electronic and provide electronic data, but there are some simpler ones, such as glass thermometers, that provide visual data.

[0145] In embodiments, sensors may be removably or fixedly mounted within the vehicle and may be arranged in a variety of arrangements to provide information to the autonomous operating features. The sensors may include one or more of a GPS unit, a radar unit, a LIDAR unit, an ultrasonic sensor, an infrared sensor, an inductive sensor, a camera, an accelerometer, a tachometer, or a speedometer. Some of the sensors (e.g., a radar, LIDAR, or camera unit) may actively or passively scan the vehicle environment for obstacles (e.g., other vehicles, buildings, pedestrians, etc.), roads, lane markings, signs, or signals. Other sensors (e.g., a GPS, accelerometer, or tachometer unit) may provide data for determining the location or movement of the vehicle (e.g., by GPS coordinates, dead reckoning, wireless signal triangulation, etc.).

[0146] As used herein, "vehicle" refers to an object used to transport people or goods. Automobiles, cars, trucks, buses, etc. are all examples of vehicles.

[0147] The term "electronic control unit" (ECU), also known as "electronic control module" (ECM), is generally a module that controls one or more subsystems. Here, the ECU can be installed in a car or other motor vehicle. It may refer to many ECUs, which may include but are not limited to an engine control module (ECM), a powertrain control module (PCM), a transmission control module (TCM), a brake control module (BCM) or an electronic brake control module (EBCM), a central control module (CCM), a central timing module (CTM), a general electronic module (GEM), a body control module (BCM), and a suspension control module (SCM). These ECUs are sometimes collectively referred to as vehicle computers or vehicle central computers, which may include separate computers. In an example, the electronic control unit may be an embedded system in the automotive electronics. In another example, the electronic control unit is wirelessly coupled to the automotive electronics.

[0148] The terms "non-transitory computer-readable medium" and "computer-readable medium" include a single medium or multiple media, such as a centralized or distributed database, and / or associated caches and servers that store one or more sets of instructions. In addition, the terms "non-transitory computer-readable medium" and "computer-readable medium" include any tangible medium that can store, encode, or carry a set of instructions for execution by a processor, for example, the set of instructions, when executed, causes a system to perform any one or more of the methods or operations disclosed herein. As used herein, the term "computer-readable medium" is expressly defined to include any type of computer-readable storage device and / or storage disk, and to exclude propagating signals.

[0149] The term “vehicle data bus” as used in this article refers to the interface of the vehicle data bus (such as CAN, LIN, Ethernet / IP, FlexRay and MOST) that enables communication between vehicle onboard equipment (OBE) and other vehicle systems to support connected vehicle applications.

[0150] The term "handshake" refers to the exchange of predetermined signals between intelligent agents connected by a communication channel to ensure that each is connected to the other (and not to an imposter). This can also include the use of passwords and codes by human operators. Handshake signals are transmitted back and forth on a communication network to establish a valid connection between two stations. Hardware handshaking uses dedicated lines, such as the Request to Send (RTS) and Clear to Send (CTS) lines in RS-232 serial transmissions. Software handshaking sends codes such as "synchronize" (SYN) and "acknowledge" (ACK) in TCP / IP transmissions.

[0151] The term "infotainment system" or "in-vehicle infotainment system (IVI)" as used herein refers to a combination of vehicle systems for providing entertainment and information. In one example, the information can be provided to the driver and passengers / occupants of the vehicle through audio / video interfaces, control elements such as touch screen displays, button panels, voice commands, etc. Some of the main components of the in-vehicle infotainment system are an integrated head-unit, a head-up display, a high-end digital signal processor (DSP) and a graphics processing unit (GPU) to support multiple displays, an operating system, a controller area network (CAN), low voltage differential signaling (LVDS) and other network protocol support (as required), a connectivity module, automotive sensor integration, a digital instrument cluster, etc.

[0152] The term "environment" or "surroundings" as used in this article refers to the surroundings and the space that the vehicle travels in. It refers to the dynamic environment in which the vehicle travels, including other vehicles, obstacles, pedestrians, lane boundaries, traffic signs and signals, speed limits, potholes, snow, water, etc.

[0153] The term "autonomous mode" as used herein refers to an independent and unsupervised mode of operation.

[0154] The term "autonomous communication" as used herein includes communication in a minimally supervised manner over a period of time in different scenarios, and is not solely or entirely based on precoding scenarios, precoding rules or predefined protocols. Generally speaking, autonomous communication is performed in an independent and unsupervised manner. In an embodiment, the communication module is enabled for autonomous communication.

[0155] The term "communication system" or "communication module" as used herein refers to a system that enables information exchange between two points. The process of transmitting and receiving information is called communication. The main elements of communication include but are not limited to the transmitter of information, the channel or medium of communication, and the receiver of information.

[0156] The term "connection" as used herein refers to a communication link. It refers to a communication channel that connects two or more devices for the purpose of data transmission. It can refer to a physical transmission medium such as a wire, or to a logical connection through a multiplexed medium such as a radio channel in telecommunications and computer networks. A channel is used for the transfer of information, such as a digital bit stream, from one or several transmitters to one or several receivers. A channel has a certain ability to transmit information, usually measured by a bandwidth in Hertz (Hz) or a data rate in bits per second. For example, vehicle-to-vehicle (V2V) communication can wirelessly exchange information about the speed, location, and heading of surrounding vehicles.

[0157] The term "communication" as used herein refers to the transmission of information and / or data from one point to another. Communication can be carried out with the help of electromagnetic waves. It is also the flow of information from one point, called a source, to another point, called a receiver. Communication includes one of the following: transmitting data, instructions and information, or a combination of data, instructions and information. Communication occurs between any two communication systems or communication units. The term "communication with..." may refer to any coupling, connection or interaction using electrical signals to exchange information or data using any system, hardware, software, protocol or format, whether the exchange is done wirelessly or through a wired connection. The term communication includes systems that combine other more specific types of communication, such as V2I (vehicle to infrastructure), V2I (vehicle to infrastructure), V2N (vehicle to network), V2V (vehicle to vehicle), V2P (vehicle to pedestrian), V2D (vehicle to device) and V2G (vehicle to grid) as well as vehicle to anything (V2X) communication.

[0158] In addition, the communication device is configured on a computer having a communication function and is connected for two-way communication with the vehicle-mounted emergency reporting device through a communication line of a radio station and a communication network such as a public telephone network or satellite communication through a communication satellite. The communication device is adapted to communicate with the communication terminal through the communication network.

[0159] The term "vehicle-to-vehicle (V2V) communication" refers to technology that allows vehicles to broadcast and receive messages. This information can be omnidirectional, creating a 360-degree "awareness" of other vehicles in the vicinity. Vehicles can be equipped with appropriate software (or safety applications or traffic condition detection) that can use information from surrounding vehicles to determine potential collision threats.

[0160] The term "protocol" as used in this article refers to the procedures required to initiate and maintain communications; a set of formal conventions that govern the format and relative timing of message exchanges between two communicating terminals; a set of specifications that manage the interaction of processes, devices, and other components within a system; a set of signaling rules for transmitting information or commands between boards connected to a bus; a set of signaling rules for passing information between intelligent agents; a set of semantic and syntactic rules that determine the behavior of interacting entities; a set of rules and formats (semantics and syntax) that determine the communication behavior of simulated applications; a set of conventions or rules that manage the interaction of processes or applications within a computer system or network; a set of formal conventions that manage the format and relative timing of message exchanges in a computer system; a set of semantic and syntactic rules that determine the behavior of functional units in achieving meaningful communication; a set of semantic and syntactic rules for exchanging information.

[0161] The term "V2X communication" as used in this article refers to the transmission of information from a vehicle to any entity that may affect the vehicle and vice versa. There are two types of V2X communication technologies, depending on the underlying technology used: cellular networks and other technologies that support direct device-to-device communication (such as Dedicated Short Range Communication (DSRC), Port Community System (PCS), wait).

[0162] The term "communication protocol" as used herein refers to standardized communications between any two systems. An example communication protocol is the DSRC protocol. The DSRC protocol uses a specific frequency band (e.g., 5.9 GHz) and specific message formats (e.g., basic safety messages, signal phase and timing, and roadside alerts) to enable communications between vehicles and infrastructure components (e.g., traffic signals and roadside sensors). DSRC is a standardized protocol whose specifications are maintained by various organizations including IEEE and SAE International.

[0163] The term "two-way communication" as used herein refers to the exchange of data between two components. In one example, the first component may be a vehicle and the second component may be an infrastructure enabled by a system of hardware, software, and firmware.

[0164] The term "alert" or "alert signal" refers to a communication that attracts attention. Alerts may include visual, tactile, audible alerts, and combinations of these alerts to warn the driver or occupants. These alerts enable the recipient, such as the driver or occupants, to react and respond quickly.

[0165] As used herein, the term "communicating with" refers to any coupling, connection, or interaction using signals to exchange information, messages, instructions, commands, and / or data, using any system, hardware, software, protocol, or format, whether the exchange is wireless or a wired connection.

[0166] As used herein, the term "traffic (signal) light" refers to a light used to control the movement of traffic. Traffic lights can be set up at road intersections and crossroads. Traffic lights usually consist of three signals, which convey meaningful information to drivers and cyclists through colors and symbols (including arrows and bicycles). Conventional traffic light colors are red, yellow (also called amber), and green, arranged in a vertical or horizontal sequence. Although this is standardized internationally, there are differences at the national and local level when it comes to traffic light sequences and laws.

[0167] As used herein, the term "traffic light cycle" refers to the time required for a complete sequence of indications. In an embodiment, the cycle length may be the same for all intersections in a coordinated plan to maintain a consistent time-based relationship. A traffic light cycle is a complete cycle through all indications provided. The traffic light cycle length refers to the time (in seconds) required for a signal to complete a complete indication cycle. The traffic light cycle length represents the time interval between a green start from one approach to the next green start.

[0168] As used herein, the term "intersection area" refers to an area shared by two or more roads. An intersection area is designated for vehicles to turn to different directions to reach their intended destinations. An intersection area can be controlled by traffic signs, traffic lights, or both.

[0169] As used herein, the term "intersection area information" refers to information of an intersection area, including at least one of the length of the intersection area, traffic signs and / or traffic lights at the intersection area, the number of roads intersecting in the intersection area, congestion information, the starting point and end point of the intersection area, etc.

[0170] As used herein, the term "last vehicle" refers to the vehicle at the end of any event (e.g., a group of vehicles that can pass through the intersection area). The vehicle behind the last vehicle (i.e., the rear vehicle) cannot pass through the intersection area and will block the intersection area.

[0171] As used herein, the term "rear vehicle" refers to a vehicle behind an intended vehicle. The intended vehicle may be one of an autonomous vehicle, a manually driven vehicle, or a semi-autonomous vehicle.

[0172] As used herein, the term "front vehicle" refers to a vehicle in front of an expected vehicle. The expected vehicle may be one of an autonomous vehicle, a manually driven vehicle, and a semi-autonomous vehicle.

[0173] As used herein, the term "continuous mobility" refers to the uninterrupted seamless movement of a vehicle. The term "continuous mobility" may further refer to seamless, progressive passage through an intersection area.

[0174] As used herein, the term "electric drive unit" refers to a unit that can control the operation of various devices of a vehicle, such as an autonomous vehicle, a manually driven vehicle, a semi-autonomous vehicle, etc. The electric drive unit enables mobility of the vehicle.

[0175] As used herein, the term "block" means to hinder or impede something or someone, or to prevent something from happening or progressing by causing difficulty.

[0176] As used herein, the term "continuous mobility" means that a vehicle moves on a road while not being blocked in an intersection area. A vehicle may stop before the start of an intersection or after the end of an intersection area to enable other vehicles in adjacent lanes sharing the intersection area to achieve continuous mobility.

[0177] As used herein, the term "lane change information" refers to a maneuver guide having information related to lane switching. The lane change information includes details of the lane to be switched (lane direction, lane position, lane distance, etc.).

[0178] As used herein, the term "threshold speed" refers to the minimum speed at which a vehicle moves when there is no traffic or congestion. The threshold speed may also be the minimum speed at which a vehicle moves in an intersection area when there is no congestion. Below the threshold speed, the vehicle may obstruct other vehicles.

[0179] As used herein, the term "threshold space" refers to the minimum space within an image of a region of interest that is suitable for analyzing the image and determining the number or count of vehicles. The number of vehicles or the count of vehicles may be calculated to determine traffic conditions.

[0180] As used herein, the term “Third Party Service” means any unaffiliated individual, company, or entity that provides services to a company. A third party service provider may be compensated for its services but has no stake, share, or equity in the company. The services of a third party service provider may also be free of charge. A third party service provider may also be web-based technology that is not wholly operated or controlled by a government entity or involves significant involvement of non-government entities.

[0181] As used herein, the term "pre-stored traffic conditions" refers to traffic conditions that are pre-stored based on the history of traffic conditions recorded so far. The pre-stored traffic conditions can be used to fix the opening value used to calculate the real-time traffic conditions. The pre-stored traffic conditions can be stored before they are needed.

[0182] As used herein, the term "current time" refers to a time that occurs or exists in the present. In the context of time series analysis, the current time is the timestamp of the most recent observation. The current time is always some non-zero instant in the past.

[0183] As used herein, the term "real-time location" refers to the real-time location of someone or something. The real-time location can be determined continuously or when asked.

[0184] As used herein, the term "rush hour" refers to a period of heavy traffic. In particular, rush hour may be the beginning or end of a workday. In an embodiment, rush hour refers to any time of day when most people are moving for some reason (e.g., commuting, commuting, etc.).

[0185] As used herein, the term "working hours" refers to the period of normal traffic. Working hours may be the usual time of day that most people spend at work. In an embodiment, working hours refers to any time of day when work is performed.

[0186] As used herein, the term "video analytics" refers to practical solutions for reviewing hours of video (such as surveillance video) to identify events related to what you are looking for. Video analytics is suitable for automatically generating a description of what actually happened in the video (so-called metadata), which can be used to list the people, cars and other objects detected in the video stream (e.g., intersection area, signal light, intersection, traffic, congestion, obstruction, etc.), as well as their appearance and movement.

[0187] As used herein, the term "starting point" refers to the lane markings at the beginning of an intersection area.

[0188] As used herein, the term "end point" refers to the lane marking at the end of an intersection area.

[0189] As used herein, the term "region of interest" refers to that portion of an image that you wish to filter or manipulate in some way. A region of interest (often abbreviated ROI) is a sample in a dataset that is identified for a specific purpose.

[0190] As used herein, the term "real-time traffic data" refers to data that can be used to determine current traffic conditions for map display and route guidance, including alternative routes when a traffic incident is detected on a planned route.

[0191] The term “cybersecurity” as used in this article refers to the application of technologies, processes, and controls to protect systems, networks, programs, devices, and data from cyber-attacks.

[0192] The term "cybersecurity module" as used in this article refers to a module that includes applied technologies, processes and controls to protect systems, networks, programs, devices and data from cyber attacks and threats. It is designed to reduce the risk of cyber attacks and prevent unauthorized exploitation of systems, networks and technologies. It includes but is not limited to critical infrastructure security, application security, network security, cloud security, Internet of Things (IoT) security.

[0193] The term "encryption" as used in this article refers to the protection of digital data using one or more mathematical techniques and a cipher or "key" for decrypting the information. It refers to the conversion of information or data into code, especially to prevent unauthorized access. It can also refer to the hiding of information or data by converting it into code. It can also be called a cipher, code, encryption, encoding. A simple example is to represent the alphabet with numbers, for example, "A" is "01", "B" is "02", and so on. For example, a message like "HELLO" would be encrypted as "0805121215" and this value would be transmitted over the network to the recipient.

[0194] The term "decryption" as used in this article refers to the process of converting an encrypted message back to its original format. It is usually the reverse process of encryption. It decodes the encrypted information so that only authorized users can decrypt the data, as a key or password is required for decryption. The term can be used to describe methods of decrypting data manually or using an appropriate code or key.

[0195] The term "cybersecurity threat" as used herein refers to any possible malicious attack aimed at illegally accessing data, disrupting digital operations, or destroying information. Generally speaking, malicious behavior includes, but is not limited to, destroying data, stealing data, or disrupting digital life. Cyber ​​threats include, but are not limited to, malware, spyware, phishing attacks, ransomware, zero-day exploits, Trojans, advanced persistent threats, wiper attacks, data manipulation, data destruction, rogue software, malicious advertising, unpatched software, computer viruses, man-in-the-middle attacks, data leaks, denial of service (DoS) attacks, and other attack vectors.

[0196] The term "hash value" used in this article can be thought of as a fingerprint of a file. The contents of a file are processed through a cryptographic algorithm and a unique numerical value, the hash value, is generated to identify the contents of the file. If the content has been modified in any way, the hash value will also change significantly. Example algorithms used to generate hash values: MessageDigest-5 (MD5) algorithm and Secure Hash Algorithm-1 (SHA1).

[0197] The term "integrity check" as used in this article refers to checking the accuracy and consistency of system-related files, data, etc. The check can be performed using a checking tool that can detect whether any critical system files have been altered, thereby enabling system administrators to find unauthorized system changes. For example, data integrity corresponds to the quality of data in a database and the level at which users check the quality, completeness, and reliability of the data. Data integrity checks verify that the data in the database is accurate and that it functions as expected in a given application.

[0198] The term "alarm" as used in this article refers to a trigger when a component or system in a system fails or does not operate as expected. A system may enter an alarm state when a specific event occurs. An alarm indication signal is a visual signal that indicates an alarm state. For example, when a network security threat is detected, the system administrator may be alerted through an audible alarm, a message, a glowing LED, a pop-up window, etc. The alarm indication signal can be reported downstream from the detection device to prevent adverse conditions or cascading effects.

[0199] As used herein, the term "traffic conditions" refers to conditions in traffic characterized by slower speeds, longer travel times, and increased vehicle queues. Traffic conditions may include traffic jams, traffic jams, etc. Traffic conditions may also include information about the speed of the vehicle, the direction of the vehicle, and the number of vehicles facing the traffic. In an embodiment, the traffic conditions include real-time traffic data. In another embodiment, the traffic conditions may be static and dynamic. In another embodiment, at least one of schematic, digital, or graphical data representing the current state of vehicles along a route within a predetermined interval is represented. In yet another embodiment, the traffic conditions refer to the average speed of vehicles at a fixed point on the road within a time slot.

[0200] As used herein, the term "autonomous vehicle" (also known as self-driving vehicle, driverless vehicle, robotic vehicle) refers to a vehicle that incorporates vehicle automation, i.e., a ground vehicle that is able to sense its environment and move safely with little or no human input. Autonomous vehicles incorporate a variety of sensors to sense their surroundings, such as thermal imagers, radio detection and ranging (radar), light detection and ranging (radar), sound navigation and ranging (sonar), global positioning system (GPS), odometers, and inertial measurement units. A control system designed for this purpose interprets the sensor information to identify an appropriate navigation path as well as obstacles and associated landmarks.

[0201] As used herein, the term "semi-autonomous vehicle" refers to a vehicle that can operate for extended periods of time with little to no human input. A semi-autonomous vehicle cannot drive itself all the time, but under ideal conditions (such as highway driving), it does perform some driving functions automatically. Semi-autonomous vehicles may use "autonomous driving" features. In one embodiment, semi-autonomous vehicles are able to stay in their lane, and they may also park themselves, but they are not self-driving. Semi-autonomous vehicles are independent to some extent.

[0202] As used herein, the term "connection" refers to a communication link. It refers to a communication channel that connects two or more devices for the purpose of data transmission. It can refer to a physical transmission medium such as a wire, or to a logical connection through a multiplexed medium such as a radio channel in telecommunications and computer networks. A channel is used for the transfer of information, such as a digital bit stream, from one or more transmitters to one or more receivers. A channel has a certain ability to transmit information, usually measured by a bandwidth in Hertz (Hz) or a data rate in bits per second. For example, vehicle-to-vehicle (V2V) communication can wirelessly exchange information about the speed, location, and heading of surrounding vehicles.

[0203] As used herein, the term "communication" refers to the transmission of information and / or data from one point to another. Communication can be performed with the help of electromagnetic waves. It is also the flow of information from one point, called the source, to another point, called the receiver. Communication includes one of the following: transmitting data, instructions and information or a combination of data, instructions and information. Communication occurs between any two communication systems or communication units.

[0204] The term "communicates with" may refer to any coupling, connection, or interaction using any system, hardware, software, protocol, or format for the exchange of information or data using electrical signals, whether the exchange occurs wirelessly or through a wired connection. The term communicates includes systems that combine other more specific types of communications, such as V2I (vehicle to infrastructure), V2I (vehicle to infrastructure), V2N (vehicle to network), V2V (vehicle to vehicle), V2P (vehicle to pedestrian), V2D (vehicle to device), and V2G (vehicle to grid), as well as vehicle to everything (V2X) communications. V2X communication is the transmission of information from a vehicle to any entity that may affect the vehicle and vice versa. The main motivations for the development of V2X are passenger safety, road safety, traffic efficiency, and energy efficiency. Depending on the underlying technology employed, there are two types of V2X communication technologies: cellular networks and other technologies that support direct device-to-device communication such as Dedicated Short Range Communications (DSRC), Port Community Systems (PCS), wait).

[0205] As used herein, the term "vehicle-to-vehicle (V2V) communications" refers to technology that allows vehicles to broadcast and receive messages. This information can be omnidirectional, creating a 360-degree "awareness" of other vehicles in the vicinity. Vehicles may be equipped with appropriate software (or safety applications) that can use information from surrounding vehicles to determine potential collision threats.

[0206] The term "protocol" as used in this article refers to the procedures required to initiate and maintain communications; a set of formal conventions that govern the format and relative timing of message exchanges between two communicating terminals; a set of specifications that manage the interaction of processes, devices, and other components within a system; a set of signaling rules for transmitting information or commands between boards connected to a bus; a set of signaling rules for passing information between intelligent agents; a set of semantic and syntactic rules that determine the behavior of interacting entities; a set of rules and formats (semantics and syntax) that determine the communication behavior of simulated applications; a set of conventions or rules that manage the interaction of processes or applications within a computer system or network; a set of formal conventions that manage the format and relative timing of message exchanges in a computer system; a set of semantic and syntactic rules that determine the behavior of functional units in achieving meaningful communication; a set of semantic and syntactic rules for exchanging information.

[0207] The term "communication protocol" as used in this article refers to standardized communication between any two systems. An example of a communication protocol is Health Level Seven (HL7). HL7 is a set of international standards that provide guidelines for the transfer and sharing of data between different healthcare providers. HL7 is a comprehensive framework for the exchange, integration, sharing, and retrieval of health information.

[0208] The term "two-way communication" as used herein refers to the exchange of data between two components. In an example, the first component may be a vehicle and the second component may be an infrastructure enabled by a system of hardware, software, and firmware. This communication is typically wireless.

[0209] The term "nearby vehicle" as used herein refers to vehicles surrounding the user's vehicle and within or beyond at least the communication range of the user's vehicle, where the communication range is defined as the maximum distance at which communication can exist between two antennas, one of which is the user's vehicle antenna in a wireless network. In an embodiment, "nearby vehicles" are written as surrounding vehicles, rear vehicles, or front vehicles. Nearby vehicles may also include vehicles from nearby lanes, opposite lanes, or adjacent lanes.

[0210] As used herein, the term "maneuver" means to carefully move, steer, or guide a vehicle in order to move from one point to another.

[0211] As used herein, the term "probability" refers to the likelihood of the outcome of any random event (e.g., a vehicle crossing an intersection area, a vehicle being the last vehicle to cross an intersection area, etc.). The meaning of this term is to examine the degree to which any event is likely to occur. The term probability can also be defined as the ratio of the number of favorable outcomes of an event to the total number of outcomes.

[0212] As used herein, the term "component" broadly interprets hardware, firmware, and / or a combination of hardware, firmware, and software.

[0213] The embodiments described herein may be directed to one or more of the systems, methods, devices, and / or computer program products of any possible level of integrated technical detail. A computer program product may include a computer-readable storage medium (or multiple media) having computer-readable program instructions thereon, for causing a processor to perform various aspects of one or more embodiments described herein. A computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. For example, a computer-readable storage medium may be, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a superconducting storage device, and / or any suitable combination of the above. A non-exhaustive list of more specific examples of computer-readable storage media may also include the following: a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device (e.g., a punch card or a raised structure in a groove) on which instructions are recorded, and / or any suitable combination of the above. Computer-readable storage media as used herein are not themselves constructed as transient signals, such as radio waves and / or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides and / or other transmission media (e.g., light pulses through fiber optic cables), and / or electrical signals transmitted through wires.

[0214] Computer-readable program instructions as described herein can be downloaded from computer-readable storage media to corresponding computing / processing devices and / or downloaded to external computers or external storage devices through a network (e.g., the Internet, local area network, wide area network, and / or wireless network). The network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network, and forwards computer-readable program instructions to be stored in the computer-readable storage medium in the corresponding computing / processing device. The computer-readable program instructions for performing the operation of one or more embodiments described herein can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, configuration data of integrated circuits, and / or source code and / or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and / or process programming languages, such as "C" programming languages ​​and / or similar programming languages. Computer readable program instructions can be executed completely on a computer, partially on a computer as a stand-alone software package, partially on a computer and / or partially on a remote computer or completely on a remote computer and / or server. In the latter case, the remote computer can be connected to the computer through any type of network, including a local area network (LAN) and / or a wide area network (WAN), and / or can be connected to an external computer (e.g., using an Internet service provider through the Internet). In one or more embodiments, electronic circuits including, for example, programmable logic circuits, field programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs) can execute computer readable program instructions by utilizing the state information of computer readable program instructions to personalize the electronic circuits, thereby performing various aspects of one or more embodiments described herein.

[0215] Aspects of one or more embodiments described herein are described with reference to flowchart illustrations and / or block diagrams of methods, devices (systems) and computer program products according to one or more embodiments described herein. Each block of the flowchart illustration and / or block diagram, and the combination of blocks in the flowchart illustration and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer and / or other programmable data processing device to produce a machine, so that instructions executed by a processor of a computer or other programmable data processing device can create a device for implementing the function / action specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can instruct a computer, a programmable data processing device and / or other equipment to work in a specific manner, so that a computer-readable storage medium with instructions stored therein can include a manufactured product, and the manufactured product includes instructions that can implement various aspects of the function / action specified in one or more blocks of a flowchart and / or block diagram. Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, and / or other device to cause a series of operational actions to be performed on the computer, other programmable apparatus, and / or other device, thereby producing a computer-implemented process, so that the instructions executed on the computer, other programmable apparatus, and / or other device implement the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0216] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functions and / or operations of possible implementations of the system, computer-implemented method and / or computer program product according to one or more embodiments described herein. In this regard, each block in the flowchart or block diagram may represent a module, a section and / or an instruction portion, which includes one or more executable instructions for implementing a specified logical function. In one or more alternative embodiments, the functions shown in each block may not occur in the order shown in the figure. For example, depending on the functions involved, two blocks displayed in succession may be executed substantially simultaneously, and / or these blocks may sometimes be executed in reverse order. It will also be noted that each block illustrated in the block diagram and / or flowchart and / or the combination of blocks in the block diagram and / or flowchart can be implemented by a system based on dedicated hardware, which can perform specified functions and / or actions and / or execute one or more combinations of dedicated hardware and / or computer instructions.

[0217] Although the subject matter described herein is described in the general context of computer executable instructions of a computer program product running on a computer and / or (plural) computers, those skilled in the art will recognize that one or more embodiments of the present invention may also be implemented in combination with one or more other program modules. Program modules include routines, programs, components, data structures, etc. that perform specific tasks and / or implement specific abstract data types. In addition, other computer system configurations, including single-processor and / or multi-processor computer systems, small computing devices, large computers, and multiple computers, handheld computing devices (e.g., PDAs, phones), microprocessor-based or programmable consumer and / or industrial electronic devices, etc., can practice the computer-implemented methods described herein. A distributed computing environment in which remote processing devices linked by a communication network perform tasks can also practice the illustrated aspects. However, a stand-alone computer can practice one or more aspects of one or more embodiments described herein, if not all aspects. In a distributed computing environment, program modules can be located in local and remote memory storage devices.

[0218] The terms "component", "system", "platform", "interface" and / or the like as used in this specification may refer to and / or may include computer-related entities or entities associated with an operating machine having one or more specific functions. The entities described herein may be hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program and / or a computer. For example, both an application running on a server and the server may be a component. One or more components may reside in the process and / or thread of execution, and components may be located on a computer and / or distributed between two or more computers. In another example, each component may be executed from various computer-readable media having various data structures stored thereon. Components may communicate via local and / or remote processes, such as according to a signal having one or more data packets (e.g., data from a component, the component interacts with another component in a local system, a distributed system and / or interacts with other systems via the signal through a network such as the Internet). As another example, a component may be a device having a specific functionality provided by mechanical parts operated by an electrical circuit or electronic circuit, the device being operated by a software and / or firmware application executed by a processor. In this case, the processor may be internal and / or external to the device and may execute at least a portion of the software and / or firmware application. As yet another example, a component may be a device providing a specific functionality by an electronic component without mechanical parts, wherein the electronic component may include a processor and / or other means for executing software and / or firmware that at least partially imparts the functionality of the electronic component. In one aspect, the component may simulate the electronic component via a virtual machine (e.g., within a cloud computing system).

[0219] As used in this specification, the term "processor" may refer to any computational processing unit and / or device, including but not limited to a single-core processor; a single processor with software multithreaded execution capability; a multi-core processor; a multi-core processor with software multithreaded execution capability; a multi-core processor using hardware multithreading technology; a parallel platform; and / or a parallel platform with distributed shared memory. In addition, a processor may refer to an integrated circuit, an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), discrete gate or transistor logic, discrete hardware components, and / or any combination thereof, which is designed to perform the functions described herein. In addition, a processor may utilize nanoscale architectures, such as but not limited to molecular-based transistors, switches, and / or gates, to optimize space usage and / or enhance the performance of associated devices. A combination of computational processing units may implement a processor.

[0220] As used herein, the terms "storage", "storage device", "data storage", "data storage device", "database", and any other information storage component related to the operation and function of a component refer to a "memory component", an entity contained in a "memory", or a component that includes a memory. The memory and / or memory components described herein may be volatile memory or non-volatile storage, or may include both volatile and non-volatile memory. By way of illustration and not limitation, non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, and / or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memory may include RAM, such as RAM that may be used as an external cache memory. By way of illustration and not limitation, RAM may be available in many forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and / or Rambus dynamic RAM (RDRAM). Additionally, the memory components of the systems and / or computer-implemented methods described herein include, but are not limited to, comprising these and / or any other suitable types of memory.

[0221] The embodiments described herein include only examples of systems and computer-implemented methods. Of course, for the purpose of describing one or more embodiments, it is not possible to describe every conceivable combination of components and / or computer-implemented methods, but one of ordinary skill in the art will recognize that many other combinations and / or permutations of the one or more embodiments are possible. In addition, to the extent that the terms "including," "having," "having," and the like are used in this detailed description, claims, appendices, and / or drawings, these terms are intended to be inclusive, similar to how the term "comprising" is interpreted when used as a transitional word in a claim.

[0222] The description of one or more embodiments is intended to illustrate, but not to be exhaustive or to limit the embodiments described herein. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein best explain the principles of the embodiments, practical applications, and / or technical improvements over technologies found on the market, and / or enable those of ordinary skill in the art to understand the embodiments described herein.

[0223] Figure 1A-1BThe traffic conditions in the intersection area are shown. Due to heavy traffic, the vehicle may be moving very slowly. The vehicle may wait for a signal from a traffic light when it reaches the intersection area. When the traffic light indicates a green signal, the vehicle starts to cross the intersection area, such as Figure 1A As shown in the figure, the traffic light is about to switch to red because the vehicle is moving very slowly. Figure 1B As shown in , when the traffic light indicates a red signal, the vehicle may be stuck in the intersection area. The traffic light on another route may indicate a green signal, instructing the vehicle on the other road to cross the intersection area. Figure 1B As shown in Figure 1, the vehicle on the other route is blocked by the vehicle at the intersection area. If the vehicle on the other route (stuck in the intersection area) cannot move either, the traffic situation will become worse, resulting in a traffic jam. Manually driven vehicles, semi-autonomous vehicles, and autonomous vehicles may all face the above scenario.

[0224] In one aspect, a method 200 for addressing the above-described situation is described. Figure 2 A method 200 for processing a busy traffic intersection area according to one or more embodiments is shown. The method includes the following steps. Step 202: The control module determines the traffic conditions around the vehicle in real time; Step 204: The control module detects the movement of the vehicle toward the traffic light and the intersection area; Step 206: The control module determines the time taken for the traffic light cycle; Step 208: The control module determines the average distance traveled by the vehicle during the traffic light cycle; and Step 210: The control module calculates the probability of the vehicle passing through the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle during the traffic light cycle, the intersection area information and the length of the vehicle through a calculation engine. In an embodiment, when it is calculated that the vehicle passes through the intersection area before the red signal of the traffic light cycle, the probability of the vehicle passing through the intersection area is set to true. In another embodiment, when it is calculated that the vehicle blocks the intersection area, the probability of the vehicle passing through the intersection area is set to false.

[0225] The control module uses a computing engine to calculate the probability that the vehicle passes through the intersection area before the red signal of the traffic light cycle. The computing engine may include an artificial intelligence engine. The artificial intelligence engine may include a machine learning algorithm. The computing engine may receive inputs as the average distance traveled by the vehicle during the traffic light cycle, intersection area information, and vehicle length. In an embodiment, the intersection area information includes the length of the intersection area. The control module analyzes the received inputs by the computing engine and calculates the probability that the vehicle passes through the intersection area before the red signal of the traffic light cycle. In an embodiment, the input may also include the distance between the vehicle and the starting point of the intersection area. In another embodiment, the input may also include the distance between the vehicle and the end point of the intersection area. In yet another embodiment, the input may also include the current signal of the traffic light (e.g., green signal, yellow signal, etc.).

[0226] The control module provides an output as true or false based on the comparison and analysis of the received input by the computing engine. The control module compares the average distance traveled by the vehicle with the length of the intersection area and the length of the vehicle by the computing engine. Then the control module checks the current signal (e.g., green signal) of the traffic light by the computing engine. Then the control module determines the remaining time of the traffic light indicating a red signal by the computing engine. Then the control module calculates the probability of the vehicle crossing the intersection area before the red signal of the traffic light cycle by the computing engine based on the average distance traveled by the vehicle during the traffic light cycle, the intersection area information, the length of the vehicle, the current signal, and the remaining time of the traffic light indicating a red signal. When it is calculated that the vehicle crosses the intersection area before the red signal of the traffic light cycle, the control module provides the output as true. In an embodiment, when it is calculated that the vehicle blocks the intersection area, the control module provides the output as false. In another embodiment, the control module can perform all the above calculations independently.

[0227] In an embodiment, when the calculated probability of a vehicle passing through the intersection area is detected to be set to true, the control module calculates that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle. The control module further calculates whether a rear vehicle behind the vehicle blocks the intersection area.

[0228] In another embodiment, the control module sends a first message to the nearby vehicle when it is predicted that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle. The first message can convey a command to the nearby vehicle before the traffic light provides a signal to the nearby vehicle. The command to the nearby vehicle enables it to achieve continuous mobility and improve traffic conditions. In yet another embodiment, the first message includes a steering guide for the nearby vehicle.

[0229] In one embodiment, when the probability of detecting that the vehicle passes through the intersection area is set to true, the control module provides a first command to the electric drive unit of the vehicle to pass through the intersection area. The first command includes details of speed, direction, lane, a first distance between the vehicle and the vehicle in front, a second distance between the vehicle and the vehicle behind, and lane change information. In another embodiment, when the probability of detecting that the vehicle passes through the intersection area is set to false, the control module provides a second command to the electric drive unit of the vehicle to stop before the intersection area (i.e., the starting point of the intersection area). The second command includes details of lane, a first distance between the vehicle and the intersection area, a second distance between the vehicle and the vehicle behind, and lane change information.

[0230] Figure 3 Detection of traffic conditions according to one or more embodiments is shown. The control module determines the traffic conditions around the vehicle 302 in real time including the following steps: monitoring the speed of the vehicle through a sensor; comparing the speed of the vehicle with a threshold speed limit; and determining the traffic conditions around the vehicle in real time when the speed of the vehicle is less than the threshold speed limit. In one embodiment, the vehicle passes through the control module and can also perform vehicle detection in its lane and surrounding lanes. The control module performs vehicle detection using an object detection module. In an embodiment, the control module performs vehicle detection using a computer vision module. The control module can also detect the speed of nearby vehicles 304A-N.

[0231] The control module determines the traffic conditions around the vehicle 302 in real time (such as Figure 3 The control module captures an image of the critical space around the vehicle. Then, the control module performs image processing on the image. Then, the control module determines the number of vehicles around the vehicle from the image. When the number of vehicles is greater than the threshold number of vehicles (such as Figure 3 The control module further determines the traffic conditions around the vehicle in real time. Image processing includes object detection.

[0232] In one embodiment, the control module determines the traffic conditions around the vehicle in real time, including the following technical steps: The control module monitors the speed of nearby vehicles through sensors. The control module compares the speed of nearby vehicles with a threshold speed. When the speed of the vehicle is less than the threshold speed (e.g. Figure 3 As shown), the control module determines the traffic conditions around the vehicle in real time.

[0233] In another embodiment, the control module can obtain the traffic conditions around the vehicle in real time from a third-party service. In yet another embodiment, the control module determines the traffic conditions around the vehicle in real time, including the following technical steps: the control module retrieves the pre-stored traffic conditions from the traffic information database based on one of the current time and the real-time positioning of the vehicle, the control module determines whether the current time is one of the peak hours or working hours through the calculation engine, and then the control module dynamically updates the pre-stored traffic conditions through the calculation engine based on the determination of whether the current time is one of the peak hours or working hours. In yet another embodiment, the control module determines the traffic conditions around the vehicle in real time, including: using statistical data analysis of the pre-stored traffic condition history from the traffic information database to calculate the traffic conditions.

[0234] In yet another embodiment, the control module determines traffic conditions around the vehicle in real time by: receiving real-time traffic data from a source; determining at least one of a speed, distance, and direction of nearby vehicles in a route; detecting a count and corresponding direction of nearby vehicles around the vehicle via a video analysis unit; determining that nearby vehicles are having difficulty navigating; and receiving traffic conditions from nearby vehicles. The source includes at least one of a closed circuit television (CCTV) system, a third-party service, a traffic flow sensor, a toll payment device, a mobile phone data, and a connected car data.

[0235] In one embodiment, the control module uses video analysis through a computing engine to detect the movement of vehicles toward traffic lights and intersection areas. In yet another embodiment, the control module performs video analysis of real-time video through a computing engine to detect the movement of vehicles toward traffic lights and intersection areas.

[0236] In yet another embodiment, the control module detects the movement of the vehicle toward the traffic light and intersection area, including the following technical steps. The control module performs image processing of the image through a computing engine. The image includes an area of ​​interest around the vehicle covering the traffic light and intersection area. The image also includes the vehicle and nearby vehicles. Then, the control module scans the image and recognizes one of the traffic light, intersection area, red signal, green signal, and yellow signal from the image. The control module detects the movement of the vehicle toward the traffic light and intersection area based on the recognition.

[0237] Figure 4The determination of the time taken for a traffic light cycle according to one or more embodiments is shown. The control module is configured to determine the time taken for a traffic light cycle. In one embodiment, the traffic light cycle includes indications of a green signal, a yellow signal, and a red signal. The control module can determine the time taken for a traffic light cycle by retrieving the time taken for a traffic light cycle from a traffic information database based on the current time and the real-time location of the vehicle. In one embodiment, the control module determines the time taken for a traffic light cycle including the following technical steps. The control module performs video analysis of the video through a computing engine. The video includes a sequence of frames of an area of ​​interest around the vehicle within a predetermined time period. The video includes a sequence of frames including a vehicle and a traffic light. The control module is adapted to scan the video and detect the interval between the green signal and the red signal of the traffic light, and vice versa. The control signal then determines the time taken for the traffic light cycle based on the interval (e.g., Figure 4 shown).

[0238] Figure 5 The determination of the average distance traveled by a vehicle during a traffic light cycle according to one or more embodiments is shown. The control module can be configured to determine the average distance traveled by the vehicle during the traffic light cycle. The control module determines the average distance traveled by the vehicle during the traffic light cycle including the following technical steps. The control module determines a first length moved by the vehicle during a first traffic light cycle. The control module determines a second length moved by the vehicle during a second traffic light cycle. The control module then determines a third length moved by the vehicle during a third traffic light cycle. The control module then calculates the average distance traveled by the vehicle during the traffic light cycle based on the first length, the second length, and the third length (e.g., Figure 5 shown).

[0239] For example, consider a first distance D1 of 100 meters between a vehicle and a traffic light at a first moment of a first traffic light cycle. The control module determines the distance between the traffic light and the vehicle using a sensor (e.g., a GPS device, a speed sensor, an accelerometer, a position sensor, a positioning sensor, etc.). In some embodiments, the position sensor may include a GPS receiver. These GPS receivers may determine the location and speed of the vehicle by processing signals transmitted from GPS satellites. After a predetermined period of time (e.g., 30 seconds), the control module determines the distance between the traffic light and the vehicle. At a second moment of a second traffic light cycle, a second distance D2 between the traffic light and the vehicle at that moment is approximately 80 meters. Similarly, the control module may determine the distance between the traffic light and the vehicle at different moments. At a third moment of a third traffic light cycle, a third distance D3 between the traffic light and the vehicle at that moment is approximately 60 meters. Then, the control module determines the average distance traveled during the traffic light cycle. The average distance is determined by adding D1+D2+D3 and then dividing by y. The integer "y" refers to the number of moments at which the distance is calculated. In this case, "y" is "3". The calculated average distance is equal to (100+80+60) / (3), 80. The average distance traveled by the vehicle is 80 meters.

[0240] Fig. 6A –6D shows the determination of the probability of vehicle 602 crossing the intersection area before the red signal of the traffic light cycle according to one or more embodiments. Vehicle 602 includes a control module. In an embodiment, in addition to the control module, vehicle 602 may also include a computing engine. The computing engine may include an artificial intelligence engine. The artificial intelligence engine may include a machine learning algorithm. In another embodiment, the control module may be outside the vehicle 602. In another embodiment, the computing engine may also be outside the vehicle 602. When the control module and the computing engine are outside the vehicle 602, the control module and the computing engine can determine the probability of vehicle 602 crossing the intersection area before the red signal of the traffic light cycle and communicate it to the vehicle 602. Vehicle 602 can be an autonomous vehicle (automatic driving vehicle), a semi-autonomous vehicle, and a manually driven vehicle.

[0241] The control module, through the calculation engine, calculates the probability that the vehicle 602 passes through the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle 602, the intersection area information and the length of the vehicle 602. The control module calculates the probability that the vehicle 602 passes through the intersection area before the red signal of the traffic light cycle through the calculation engine, including the following technical steps. The control module obtains the intersection area information from one of the traffic information database and the third-party service. Then, the control module recognizes the current signal of the traffic light cycle in real time. In an embodiment, the control module then recognizes the current signal of the traffic light cycle in real time through the computer vision module. In an embodiment, the control module then recognizes the current signal of the traffic light cycle in real time through the object detection module.

[0242] Next, the control module identifies the current signal of the traffic light cycle in real time by performing traffic signal detection. The control module performs traffic signal detection by performing the following technical steps. The control module performs color space conversion by converting the input image into an HSV color space image. The input image includes a real-time image of a traffic light. The control module filters the HSV color space image based on a predetermined pass range of the standard color of each traffic sign to obtain a filtered image. The control module then generates one or more connection domains based on one or more regions in the filtered image. Then, the control module removes at least one connection domain in the generated connection domain that is not a corresponding traffic signal based on the standard rule of the corresponding traffic sign. Then, the control module makes other connection domains in the generated connection domains candidate traffic sign domains. Then, the control module identifies the corresponding traffic signal based on the characteristics of each candidate traffic sign domain.

[0243] In one embodiment, the intersection area information can be obtained by accessing a traffic information database. The traffic information database may include a map database having information about intersections (crossroads), crosswalks, corners, schools, etc. located on the navigation route or near the vehicle. In another embodiment, the intersection area information can be obtained from a global positioning device. The global positioning device may be located near the intersection area.

[0244] The intersection area information may include intersection positioning data. In an embodiment, the system includes an integrated radar sensor for determining intersection positioning data. The integrated radar sensor is configured to detect multiple objects relative to the intersection. The multiple objects include multiple vehicles. The control module tracks the above-mentioned multiple detected objects to determine tracking data. Tracking includes determining the number of detected objects and determining the speed and position of the multiple detected objects. The control module then sends the tracking data to at least one of the multiple detected objects. The tracking data includes the number of detected objects and the speed and position of the above-mentioned multiple detected objects. Then, the control module sends a collision warning signal to at least one of the multiple detected objects based on the number of detected objects, the speed and position of the multiple detected objects, the predetermined average travel time through the intersection, and the predetermined number of vehicles that may enter and exit the intersection during the traffic light cycle.

[0245] The length of the vehicle 602 may be pre-stored in a database. The control module may extract the length of the vehicle 602. In one embodiment, the control module is associated with a computing engine and may perform vehicle model recognition based on an image of the vehicle. The image of the vehicle is compared with a pre-stored vehicle model. The pre-stored vehicle model may be updated periodically based on updates in the market. The control module is associated with a computing engine and performs vehicle model recognition based on the comparison and identifies the vehicle model in the image. The computing engine may then extract the specifications (including size, length, etc.) of the model vehicle and extract the length of the vehicle.

[0246] The control module then estimates the probability that the vehicle 602 will cross the intersection area before the traffic light cycle based on the average distance traveled by the vehicle 602 during the traffic light cycle, the intersection area information, the current signal of the traffic light cycle, and the length of the vehicle 602. The calculation of the average travel distance during the traffic light cycle has been discussed above. The recognition of the current signal of the traffic light has also been discussed above.

[0247] When it is calculated that the vehicle 602 crosses the intersection area before the red signal of the traffic light cycle, the probability that the vehicle 602 crosses the intersection is set to true. When it is calculated that the vehicle 602 blocks the intersection area, the probability that the vehicle 602 crosses the intersection area is set to false.

[0248] For example, consider that vehicle 602 is moving toward a traffic light. The control module determines that the vehicle has moved 15 meters (e.g., Fig. 6AAs shown), and the traffic light switches to a red signal within two seconds. The control module then compares the distance traveled by the vehicle with the average travel distance, i.e., 80 meters (the calculation has been described above). The control module also compares the remaining time to switch to the red signal (i.e., 2 seconds) with the time taken for the traffic light cycle. Since the distance traveled is less than the average travel distance, the control module calculates and concludes that the vehicle cannot pass through the intersection area before the red signal of the same traffic light, and sets the probability to false. In an embodiment, the control module also considers the remaining time until the traffic light switches to a red signal and the time taken for the traffic light cycle when determining the probability of passing through the intersection area. The control module then stops before the starting point of the intersection area (as shown). Figure 6B The first command (shown) is transmitted to the electric drive unit of the vehicle. Based on the first command, the electric drive unit manipulates the vehicle to stop (or slow down and stop) before the red signal of the traffic light and before crossing the intersection area.

[0249] In one embodiment, when the traveled distance is less than the average traveled distance, the control module calculates and concludes that the vehicle can pass through the intersection area (e.g., Figure 6C In another embodiment, the control module also considers the remaining time for the traffic light to switch to the red signal light and the time taken for the traffic light cycle when determining the probability of passing through the intersection area. Then, the control module transmits the second command of passing through the intersection area to the electric drive unit of the vehicle (as shown in FIG. Figure 6B (shown)

[0250] In one embodiment, the control module calculates the probability of a vehicle crossing an intersection area before the red signal of a traffic light cycle through a computing engine, including the following steps. The control module obtains intersection area information, pedestrian crossing length, and bicycle box length from one of the traffic information databases from a third-party service. The control module identifies the current signal of the traffic light cycle in real time. The control module estimates the probability of a vehicle crossing an intersection area before the red signal of a traffic light cycle based on the average distance traveled by the vehicle within the traffic light cycle, the intersection area information, the current signal of the traffic light cycle, the pedestrian crossing length, the bicycle box length, and the vehicle length. In an embodiment, the control module determines the bicycle box length and the pedestrian crossing by using one of the object detection module and the computer vision module. In an embodiment, as Fig.6D As shown, the control module also considers the length of the vehicle when determining whether the vehicle has passed the intersection area.

[0251] Figure 7The communication between the vehicle 702 and the rear vehicle 703 according to one or more embodiments is shown. The control module performs the following technical steps. The control module calculates that the vehicle 702 is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle. Then the control module transmits a notification from the vehicle 702 to the rear vehicle 703. The notification includes a message indicating the countdown (countdown) of the traffic light switching to the red signal. In an embodiment, the communication between the vehicle 702 and the rear vehicle 703 is via vehicle-to-vehicle (V2V) communication. V2V communication can be based on a wireless communication protocol using at least one of dedicated short-range communication (DSRC) and cellular network to everything (C-V2X, cellular vehicle to everything) technology. In another embodiment, the communication between the vehicle 702 and the rear vehicle 703 is connected via the Internet. The rear vehicle 703 can act accordingly in response to the notification. The rear vehicle 703 can also be an autonomous vehicle. In an embodiment, the control module can also consider the length of the vehicle 702 when calculating that the vehicle is the last vehicle to pass through the intersection area.

[0252] In another embodiment, the control module calculates that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle. The control module sends a notification from the vehicle to the vehicle ahead. The notification includes a message indicating the steering guidance of the vehicle ahead to improve traffic conditions around the intersection area. In an embodiment, the communication between the vehicle and the vehicle ahead is via vehicle-to-vehicle (V2V) communication. In another embodiment, the communication between the vehicle and the vehicle ahead is via an Internet connection. The control module may also consider the length of the vehicle when calculating that the vehicle is the last vehicle to pass through the intersection area.

[0253] In yet another embodiment, the control module determines that the vehicle has passed through the intersection area. The control module determines that the vehicle has passed through the intersection area including the following technical steps. The control module captures a video of the area of ​​interest in front of the vehicle in real time, the video covering the vehicle and the intersection area. The control module then identifies the end point of the intersection area from the video in real time. In an embodiment, the control module uses object detection technology to identify the end point of the intersection area from the video in real time through a computing engine. Then, when the end point of the intersection area has passed through the video of the area of ​​interest, the control module determines that the vehicle has passed through the intersection area.

[0254] Fig. 8A 8B shows the determination of a vehicle passing through an intersection area according to one or more embodiments. The control module determines that the vehicle has passed through the intersection area including the following technical steps. The control module measures the distance traveled by the vehicle in front of the end of the intersection area. The control module compares the length of the vehicle and the distance traveled by the vehicle. When the distance traveled by the vehicle is greater than the length of the vehicle (e.g. Fig. 8AAs shown), the control module determines that the vehicle has passed the intersection area. For example, assuming that the length of the vehicle is 5 meters and the distance traveled by the vehicle is 10 meters. Then, the control module determines and ensures that the vehicle has passed the intersection area.

[0255] In another embodiment, the control module determines that the vehicle passes through the intersection area, including the following technical steps: The control module measures the distance traveled by the vehicle in front of the end of the intersection area. The control module compares the length of the vehicle and the distance traveled by the vehicle. When the distance traveled by the vehicle is less than the length of the vehicle (e.g. Figure 8B As shown), the control module determines that the vehicle is blocking the intersection area. For example, assuming that the length of the vehicle is 10 meters and the distance traveled by the vehicle is 7 meters. Then, the control module determines and ensures that the vehicle blocks the intersection area.

[0256] Figures 9A-9B Lane detection according to one or more embodiments is shown. The control module first determines whether the vehicle 902 is blocking the intersection area. Then, the control module performs the following technical steps to perform lane detection. The control module measures the distance between the vehicle 902 and the preceding vehicle 905 in the lane 906 and the adjacent lane 907. The control module compares the distance with the length of the vehicle 902. The control module transmits a third command to the electric drive unit of the vehicle 902 to perform one of the following: move forward in the lane 906 to a predetermined distance (such as Fig.9A ); when the measured distance is "x" times the length of the vehicle, a lane change to the adjacent lane 907 is performed (as shown in Fig. 9B ). "x" may be predetermined. In one embodiment, "x" may be equal to 0.25. In one embodiment, "x" may be equal to 0.5. For example, consider that the distance between vehicle 902 and the preceding vehicle 905 in lane 906 is 50 meters. Consider that the distance between vehicle 902 and the potential (would-be) preceding vehicle in adjacent lane 907 is 100 meters. Because the distance in adjacent lane 907 is greater than the distance in lane 906, the control module transmits a command to the electric drive unit to execute a lane change to adjacent lane 907 (e.g., Fig. 9B shown).

[0257] Fig.10Shows the determination of the probability that a rear vehicle 1003 behind a vehicle 1002 passes through an intersection area. The control module determines the probability that the rear vehicle 1003 behind the vehicle 1002 passes through the intersection area, which includes the following technical steps. The control module obtains the distance traveled by the vehicle after passing through the intersection area. Then, the control module detects whether there is a rear vehicle 1003 behind the vehicle 1002. In an embodiment, the control module uses object detection through a computing engine (e.g., an artificial intelligence engine) to detect the rear vehicle 1003. The control module then detects that the rear vehicle 1003 is in the intersection area. The control module then obtains the model and length of the rear vehicle 1003. In an embodiment, the control module uses a car model recognition algorithm through a computing engine to determine the model and length of the rear vehicle 1003. Then, the control module determines the intersection area range where the rear vehicle 1003 is blocking the intersection area (i.e., intruding into the intersection area) based on the following: the distance traveled by the vehicle 1002 after passing through the intersection area ( Fig.10 A in), the length of the rear vehicle 1003 ( Fig.10 D in), the length of the vehicle 1002 ( Fig.10 B in), and the distance between the vehicle 1002 and the rear vehicle 1003 (as Fig.10 C in). Fig.10 As shown, V1 is the rear vehicle 1003.

[0258] The control module estimates as follows:

[0259] A = B + C + D, the rear vehicle 1003 does not block the intersection.

[0260] A > B + C + D, the rear vehicle 1003 does not block the intersection.

[0261] A < B + C + D, the rear vehicle 1003 is blocking the intersection.

[0262] Figures 11A-11C Shows the communication between a vehicle 1102 and nearby vehicles according to one or more embodiments to improve traffic conditions. The control module determines that the rear vehicle 1103 is blocking the intersection. Then, the control module honks at a front vehicle 1108 in front of the vehicle 1102. When the front vehicle 1108 moves further forward or if the front vehicle 1108 moves further forward and the vehicle 1102 also moves further forward, the rear vehicle 1103 will move forward and will block the intersection less.

[0263] In one embodiment, the control module determines the intersection area that the rear vehicle 1103 is blocking. The control module performs the following technical steps. When the rear vehicle 1103 is blocking the intersection area, the control module transmits one of a first signal and a first notification to the front vehicle 1108 to make it move forward. The control module monitors the movement of the front vehicle 1108 (e.g., Fig.11A In response to the movement of the front vehicle 1108, the control module steers the vehicle 1102 through the electric drive unit (as shown). Fig. 11B The control module then transmits one of the second signal and the second notification to the rear vehicle 1103 to move forward to a predetermined position to minimize obstruction to the intersection area (such as Fig. 11C ). In one embodiment, if the front vehicle 1108 does not move forward despite receiving instructions or signals from the vehicle 1102, the control module monitors the front vehicle 1108. After determining that the front vehicle 1108 is not moving forward, the control module instructs the electric drive unit to maneuver the vehicle 1102 to move as close to the front vehicle 1103 as possible to improve the situation of the rear vehicle 1103. The vehicle 1102 then communicates with the rear vehicle 1103 to make it block the intersection area as little as possible or completely away from the intersection area.

[0264] In another aspect, a system is described. Fig.12 A system 1200 according to one or more embodiments is shown. The system 1200 includes a control module 1212 and a communication module. The system 1200 further includes a computing engine 1214. The computing engine 1214 includes an artificial intelligence engine. The artificial intelligence engine includes a machine learning algorithm. The control module 1212 is operable to determine the traffic conditions around the vehicle in real time at step 1202. At step 1204, the control module detects the movement of the vehicle toward the traffic light and the intersection area. At step 1206, the control module determines the time taken for the traffic light cycle. At step 1208, the control module determines the average distance traveled by the vehicle during the traffic light cycle. At step 1210, the control module calculates the probability that the vehicle crosses the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle, the intersection area information and the length of the vehicle. When it is calculated that the vehicle crosses the intersection area before the red signal of the traffic light cycle, the probability that the vehicle crosses the intersection area is set to true. When it is calculated that the vehicle blocks the intersection area, the probability that the vehicle crosses the intersection area is set to false. In an embodiment, system 1200 is configured as a component of a vehicle.

[0265] In another embodiment, the control module 1212 is operable to calculate that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle. In yet another embodiment, the control module 1212 is operable to transmit a first message to a nearby vehicle when it is calculated that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle. In yet another embodiment, the first message transmits a command to a nearby vehicle before the traffic light provides a signal to the nearby vehicle. In yet another embodiment, the command to the nearby vehicle enables at least one of the vehicle and the nearby vehicle to achieve continuous mobility to improve traffic conditions. In yet another embodiment, the control module 1212 is operable to: when the probability of the vehicle passing through the intersection area is set to true, provide a first command to the electric drive unit of the vehicle to pass through the intersection area. In yet another embodiment, the control module 1212 is operable to: when the probability of the vehicle passing through the intersection area is set to false, provide a second command to the electric drive unit of the vehicle to stop before the intersection area.

[0266] The computing engine 1214 may include at least one of a computer vision module, an object detection module, a car model recognition module, etc. The computing engine 1214 is configured to detect lanes, nearby vehicles, traffic signals, etc. The computing engine 1214 is associated with the control module 1212 and may be further configured to determine traffic conditions, detect movement of the vehicle toward a traffic light, determine a traffic light cycle, determine whether the vehicle passes through an intersection area, etc. These determinations have been described in detail in the present disclosure.

[0267] In an embodiment of the system, the communication module is enabled for at least one of vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, vehicle-to-everything (V2X) communication, and vehicle-to-network communication.

[0268] In an embodiment of the system, vehicle-to-vehicle (V2V) communication is based on a wireless communication protocol using at least one of dedicated short-range communication (DSRC) technology and cellular vehicle-to-everything (C-V2X) technology.

[0269] In an embodiment of the system, the V2X communication includes at least one of vehicle to network (V2N), vehicle to grid (V2G), vehicle to device (V2D), and vehicle to pedestrian (V2P).

[0270] In an embodiment of the system, communicating includes transmitting at least one of data, instructions, messages, and information.

[0271] In an embodiment of the system, the communication module supports communication protocols, wherein the communication protocols include Advanced Message Queuing Protocol (AMQP), Message Queuing Telemetry Transport (MQTT) protocol, Simple (or Streaming) Text-Oriented Message Protocol (STOMP), Zigbee protocol, Unified Diagnostic Service (UDS) protocol, Open Diagnostic Exchange Format (ODX) protocol, Internet Diagnostic Protocol (DoIP), On-Board Diagnostics (OBD) protocol, and at least one of a predefined protocol standard.

[0272] In an embodiment, the system includes a computation engine.

[0273] In an embodiment, the computing engine comprises an artificial intelligence engine.

[0274] In an embodiment, the artificial intelligence engine comprises a machine learning model.

[0275] In an embodiment of the present system, the machine learning model is configured to learn using labeled data using a supervised learning method, wherein the supervised learning method includes using decision trees, logistic regression, support vector machines, k-nearest neighbors, A logic of at least one of Bayes (Naive Bayes), Random Forest, Linear Regression, Polynomial Regression, and Support Vector Machine for regression.

[0276] In an embodiment of the system, the machine learning model is configured to learn from real-time data using an unsupervised learning method, wherein the unsupervised learning method includes logic using at least one of k-means clustering, hierarchical clustering, a hidden Markov model, and a priori algorithms.

[0277] In an embodiment of the system, the machine learning model has a feedback loop where the output of the previous step is fed back to the model in real time to improve the performance and accuracy of the output of the next step.

[0278] In an embodiment of the system, the machine learning model comprises a recurrent neural network model.

[0279] In an embodiment of the system, the machine learning model has a feedback loop where learning is further reinforced by each real reward output by the system.

[0280] In an embodiment, the system further comprises a network security module, wherein the network security module comprises an information security management module, which provides isolation between the communication module and the server.

[0281] In an embodiment, the information security management module is operable to: receive data from the communication module, exchange security keys at the beginning of communication between the communication module and the server, receive security keys from the server, authenticate the identity of the server by verifying the security keys, analyze the security keys to find potential network security threats, negotiate encryption keys between the communication module and the server, encrypt data, and send the encrypted data to the server when no network security threats are detected.

[0282] In an embodiment, the information security management module is operable to: exchange security keys at the beginning of communication between the communication module and the server, receive the security keys from the server, authenticate the identity of the server by verifying the security keys, analyze the security keys to find potential network security threats, negotiate encryption keys between the system and the server, receive encrypted data from the server, decrypt the encrypted data, perform an integrity check on the decrypted data, and send the decrypted data to the communication module when no network security threat is detected.

[0283] In yet another aspect, a non-transitory computer-readable medium. Fig.13 A non-transitory computer-readable medium 1300 according to one or more embodiments is shown. The non-transitory computer-readable medium 1300 stores a sequence of instructions that, when executed by a processor 1301, causes: determining the traffic conditions around the vehicle in real time at 1302; detecting the movement of the vehicle toward the traffic light and the intersection area at 1304; determining the time taken for the traffic light cycle at 1306; determining the average distance traveled by the vehicle during the traffic light cycle at 1308; and calculating the probability of the vehicle crossing the intersection area before the red signal of the traffic light cycle by a computing engine at 1310 based on the average distance traveled by the vehicle, the intersection area information, and the length of the vehicle. When it is calculated that the vehicle crosses the intersection area before the red signal of the traffic light cycle, the probability of the vehicle crossing the intersection area is set to true. When it is calculated that the vehicle blocks the intersection area, the probability of the vehicle crossing the intersection area is set to false. The computing engine may include an artificial intelligence engine.

[0284] In an embodiment, the non-transitory computer readable medium 1300 further causes calculating that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle. In an embodiment, the non-transitory computer readable medium 1300 further causes: when calculating that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle, transmitting a first message to nearby vehicles. In an embodiment, the non-transitory computer readable medium 1300 further causes: when the probability of the vehicle passing through the intersection area is set to true, providing a first command to the electric drive unit of the vehicle to pass through the intersection area. In an embodiment, the non-transitory computer readable medium 1300 further causes: when the probability of the vehicle passing through the intersection area is set to false, providing a second command to the electric drive unit of the vehicle to stop before the intersection area.

[0285] Fig.14 14 is a block diagram of the electronic components of a vehicle. In the illustrated example, the electronic components include an onboard computing platform 1402 , a human machine interface (HMI) unit 1404 , a communication module 1420 , a sensor 1406 , an electronic control unit (ECU) 1408 , and a vehicle data bus 1410 .

[0286] The onboard computing platform 1402 includes a processor 1412 (also referred to as a microcontroller unit or controller) and a memory 1414. In the illustrated example, the processor 1412 of the onboard computing platform 1402 is configured to include a controller 1412-1. In other examples, the controller 1412-1 is incorporated into another ECU having its own processor and memory. The processor 1412 can be any suitable processing device or a group of processing devices, such as but not limited to a microprocessor, a microcontroller-based platform, an integrated circuit, one or more field programmable gate arrays (FPGAs), and / or one or more application-specific integrated circuits (ASICs). The memory 1414 can be a volatile memory (e.g., a RAM including non-volatile RAM, magnetic RAM, ferroelectric RAM, etc.), a non-volatile memory (e.g., a disk memory, a FLASH memory, an EPROM, an EEPROM, a non-volatile solid-state memory based on a memristor, etc.), an unchangeable memory (e.g., an EPROM), a read-only memory, and / or a high-capacity storage device (e.g., a hard disk drive, a solid-state drive, etc.). In some examples, memory 1414 includes multiple types of memory, particularly volatile memory and non-volatile storage. Memory 1414 is a computer-readable medium on which one or more sets of instructions, such as software for operating the methods of the present disclosure, can be embedded. The instructions can embody one or more of the methods or logics described herein. For example, during the execution of the instructions, the instructions reside completely or at least partially in any one or more of the memory 1414, the computer-readable medium, and / or the processor 1412.

[0287] The HMI unit 1404 provides an interface between the vehicle and the user. The HMI unit 1404 includes digital and / or analog interfaces (e.g., input devices and output devices) to receive input from the user and display information to the user. Input devices include, for example, control knobs, instrument panels, digital cameras for image capture and / or visual command recognition, touch screens, audio input devices (e.g., cabin microphones), buttons, or touch pads. Output devices may include instrument cluster outputs (e.g., dials, lighting devices), tactile devices, actuators, displays 1416 (e.g., heads-up displays, center console displays such as liquid crystal displays (LCDs), organic light emitting diode (OLED) displays, flat panel displays, solid-state displays, etc.), and / or speakers 1418. For example, the display 1416, speakers 1418, and / or other output devices of the HMI unit 1404 are configured to issue an alert to an operator of the vehicle (e.g., a driver), such as an alert requesting manual takeover. Moreover, the HMI unit 1404 of the illustrated example includes hardware (eg, a processor or controller, memory, storage, etc.) and software (eg, an operating system, etc.) for an infotainment system presented via a display 1416 .

[0288] Sensors 1406 are arranged in and / or around the vehicle to monitor the characteristics of the vehicle and / or the environment in which the vehicle is located. One or more of the sensors 1406 may be installed to measure the characteristics of the surrounding outside the vehicle. Additionally or alternatively, one or more of the sensors 1406 may be installed in the cabin or body of the vehicle (e.g., engine compartment, wheel well, etc.) to measure the characteristics of the vehicle and / or the interior of the vehicle. For example, the sensors 1406 include accelerometers, odometers, tachometers, pitch and yaw sensors, wheel speed sensors, microphones, tire pressure sensors, biometric sensors, ultrasonic sensors, infrared sensors, light detection and ranging (lidar), radio detection and ranging systems (radar), global positioning systems (GPS), cameras and / or any other suitable type of sensor. In the illustrated example, the sensor 1406 includes a long-range detection sensor that is configured to monitor objects located in an area around the vehicle.

[0289] ECU 1408 monitors and controls the subsystems of the vehicle. For example, ECU 1408 is a discrete group of electronic devices that includes their own circuits (e.g., integrated circuits, microprocessors, memory, storage devices, etc.) and firmware, sensors, actuators, and / or mounting hardware. ECU 1408 communicates and exchanges information via a vehicle data bus (e.g., vehicle data bus 1410). In addition, ECU 1408 can communicate characteristics (e.g., ECU status, sensor readings, control status, errors and diagnostic codes, etc.) and / or receive requests from each other. For example, a vehicle may have dozens of ECUs located at various locations around the vehicle and communicatively coupled to vehicle data bus 1410.

[0290] In the illustrated example, the ECU 1408 includes an autonomous unit 1408-1 and a body control module 1408-2. For example, the autonomous unit 1408-1 is configured to perform autonomous and / or semi-autonomous driving maneuvers (e.g., defensive driving maneuvers) of the vehicle based at least in part on instructions received from the controller 1412-1 and / or data collected by the sensor 1406 (e.g., a distance detection sensor). In addition, the body control module 1408-2 controls one or more subsystems throughout the vehicle, such as power windows, power locks, anti-theft systems, power mirrors, etc. For example, the body control module 1408-2 includes circuits that drive one or more relays (e.g., for controlling wiper fluid, etc.), brushed DC (DC) motors (e.g., for controlling power seats, power locks, power windows, wipers, etc.), stepper motors, LEDs, safety systems (e.g., seat belt pretensioners, airbags, etc.), etc.

[0291] The vehicle data bus 1410 communicatively couples the communication module 1420, the onboard computing platform 1402, the HMI unit 1404, the sensor 1406, and the ECU 1408. In some examples, the vehicle data bus 1410 includes one or more data buses. The vehicle data bus 1410 can be configured in accordance with the Controller Area Network (CAN) bus protocol defined by the International Standards Organization (ISO) 11898-1, the Media Oriented Systems Transport (MOST) bus protocol, the CAN Flexible Data (CAN-FD) bus protocol (ISO 11898-7), and / or the K-line bus protocol (ISO 9141 and ISO 14230-1), and / or the Ethernet bus protocol. TM It is implemented by bus protocol IEEE 802.3 (after 2002) and so on.

[0292] The communication module 1420-1 for nearby devices is configured to communicate with other nearby communication devices. In the illustrated example, the communication module 1420 includes a dedicated short-range communication (DSRC) module. The DSRC module includes an antenna, a radio, and software to communicate with nearby vehicles via vehicle-to-vehicle (V2V) communication, communicate with infrastructure-based modules via vehicle-to-infrastructure (V2I) communication, and / or more generally communicate with attached communication devices (e.g., mobile device-based modules) via vehicle-to-everything (V2X) communication.

[0293] V2V communication allows vehicles to share information such as speed, location, direction, and other relevant data, enabling these vehicles to collaborate and coordinate their actions to improve safety, efficiency, and mobility on the road. V2V communication can be used to support a variety of applications such as collision avoidance, lane change assistance, platooning, and traffic management. It can rely on dedicated short-range communications (DSRC) and other wireless protocols that enable fast and reliable data transmission between vehicles. V2V communication is a form of wireless communication between vehicles that allows vehicles to exchange information and coordinate with other vehicles on the road. V2V communication enables vehicles to share data about their location, speed, direction, acceleration, and braking with other nearby vehicles, which can help improve safety, reduce congestion, and make transportation systems more efficient.

[0294] V2V communications are typically based on wireless communication protocols such as Dedicated Short Range Communications (DSRC) or Cellular Vehicle-to-Everything (C-V2X) technology. Through V2V communications, vehicles can receive information about potential hazards, such as accidents or road closures, and adjust their behavior accordingly. V2V communications can also be used to support advanced driver assistance systems (ADAS) and autonomous driving technologies such as platooning, in which a group of vehicles driving in close proximity together use V2V communications to coordinate their movements.

[0295] For more information on DSRC networks and how they communicate with vehicle hardware and software, see the U.S. Department of Transportation's June 2011 Core System Requirements Specification (SyRS) report (available at http: / / wwwits.dot.gov / meetings / pdf / CoreSystemSESyRSRevA%20(2011-06-13) .pdf DSRC systems can be installed on vehicles as well as along roadsides on infrastructure. DSRC systems that include infrastructure information are called "roadside" systems. DSRC can be combined with other technologies, such as Global Positioning System (GPS), Visible Light Communication (VLC), cellular communications, and short-range radar, to help vehicles communicate their location, speed, heading, relative position to other objects, and exchange information with other vehicles or external computer systems. DSRC systems can be integrated with other systems such as mobile phones.

[0296] Currently, DSRC networks are identified by the DSRC abbreviation or name. However, other names are sometimes used, usually in connection with connected vehicle programs, etc. Most of these systems are either pure DSRC or variants of the IEEE 802.11 wireless standard. However, in addition to pure DSRC systems, it also means dedicated wireless communication systems covering cars and roadside infrastructure systems that are integrated with GPS and based on the IEEE 802.11 protocol for wireless LANs (such as 802.11p, etc.).

[0297] Additionally, or alternatively, the communication module 1420-2 for external networks includes a cellular vehicle-to-everything (C-V2X) module. The C-V2X module includes hardware and software to: communicate with other vehicles via V2V communication, communicate with infrastructure-based modules via V2I communication, and / or more generally, communicate with nearby communication devices (e.g., mobile device-based modules) via V2X communication. For example, the C-V2X module is configured to communicate with nearby devices (e.g., vehicles, roadside units, mobile devices, etc.) directly and / or via a cellular network. Currently, the Third Generation Partnership Project is developing standards related to C-V2X communications.

[0298] In addition, the communication module 1420-2 for external networks is configured to communicate with external networks. For example, the communication module 1420-2 for external networks includes hardware (e.g., processor, memory, storage device, antenna, etc.) and software for controlling wired or wireless network interfaces. In the illustrated example, the communication module 1420-2 for external networks includes one or more communication controllers for the following: cellular networks (e.g., Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE), Code Division Multiple Access (CDMA)), near field communication (NFC) and / or other standard-based networks (e.g., WiMAX (IEEE 802.16m), local area wireless networks (including IEEE 802.11 a / b / g / n / ac or others), wireless Gigabit (IEEE 802.11ad), etc.). In some examples, the communication module 1420-2 for external networks includes a wired or wireless interface (e.g., an auxiliary port, a universal serial bus (USB) port, The vehicle may be coupled to a mobile device (e.g., a smart phone, a wearable device, a smart watch, a tablet, etc.) to communicate with the mobile device (e.g., a smart phone, a wearable device, a smart watch, a tablet, etc.). In such an example, the vehicle may communicate with the external network via the coupled mobile device. The external network may be a public network such as the Internet, a private network such as an intranet, or a combination thereof, and may utilize various network protocols now available or later developed, including but not limited to network protocols based on TCP / IP.

[0299] In an embodiment, the communication module 1420 is used for autonomous communication, wherein autonomous communication includes communication in a minimally supervised manner in different scenarios within a period of time. The communication module includes hardware components, and the hardware components include a vehicle gateway system, the vehicle gateway system includes a microcontroller, a transceiver, a power management integrated circuit, and an Internet of Things device capable of transmitting one of an analog signal and a digital signal through one of telephone, wired or wireless communication.

[0300] The autonomous unit 1408-1 of the illustrated example is configured to perform autonomous and / or semi-autonomous driving maneuvers on the vehicle, such as defensive driving maneuvers. For example, the autonomous unit 1408-1 performs autonomous and / or semi-autonomous driving maneuvers based on data collected by the sensor 1406. In some examples, the autonomous unit 1408-1 is configured to operate a fully autonomous system, a parking assist system, an advanced driver assistance system (ADAS), and / or other autonomous systems of the vehicle.

[0301] ADAS is configured to help the driver operate the vehicle safely. For example, ADAS is configured to perform adaptive cruise control, collision avoidance, lane assistance (e.g., lane centering), blind spot detection, rear collision warning, lane departure warning, and / or any other function that helps the driver operate the vehicle. In order to perform driver assistance functions, ADAS monitors objects (such as vehicles, pedestrians, traffic lights, etc.) and develops situational awareness around the vehicle. For example, ADAS uses data collected by sensors 1406, communication modules 1420-1 (e.g., from other vehicles, roadside units, etc.), communication modules 142-2 from remote servers, and / or other sources to monitor nearby objects and develop situational awareness.

[0302] In addition, in the example shown, the controller 1412-1 is configured to monitor the surrounding environment of the vehicle. For example, in order to enable the autonomous unit 1408-1 to perform autonomous and / or semi-autonomous driving maneuvers, the controller 1412-1 collects data collected by the vehicle's sensors 1406. In some examples, the controller 1412-1 collects positioning-based data via the communication module 420-1 and / or another module (e.g., a GPS receiver) to facilitate the autonomous unit 1408-1 to perform autonomous and / or semi-autonomous driving maneuvers. In addition, the controller 1412-1 (i) collects data from neighboring vehicles via the communication module 1420-1 and V2V communication and / or (ii) collects data from the roadside unit via the communication module 1420-1 and V2I communication to further facilitate the autonomous unit 1408-1 to perform autonomous and / or semi-autonomous driving maneuvers.

[0303] In operation, according to an embodiment, the communication module 1420-1 performs V2V communication with neighboring vehicles. For example, the communication module 1420-1 collects data from neighboring vehicles that identifies (i) whether the neighboring vehicle includes an autonomous and / or semi-autonomous system (e.g., ADAS), (ii) whether the autonomous and / or semi-autonomous system is active, (iii) whether the autonomous and / or semi-autonomous system has issued a manual takeover request, (iv) lane detection information of the neighboring vehicle, (v) the speed and / or acceleration of the neighboring vehicle, (vi) the (relative) position of the neighboring vehicle, (vii) the direction of travel of the neighboring vehicle, (viii) the rate of change of the steering angle of the neighboring vehicle, (ix) the size of the neighboring vehicle, (x) whether the neighboring vehicle is using a stability control system (e.g., anti-lock braking, traction control, electronic stability control, etc.), and / or any other information that helps the controller 1412-1 monitor the neighboring vehicle.

[0304] Based at least in part on data collected by the communication module 1420-1 from the neighboring vehicle via V2V communication, the controller 1412-1 can determine the probability of collision of the neighboring vehicle. For example, the controller 1412-1 determines the probability of collision of the neighboring vehicle in response to identifying a manual takeover request within the data collected by the communication module 1420-1 from the neighboring vehicle. Additionally or alternatively, the controller 1412-1 determines the probability of collision of the neighboring vehicle in response to identifying the difference between (i) the lane marker positioning determined by the controller 1412-1 of the vehicle based on the sensor 1406 and (ii) the lane marker positioning determined by the neighboring vehicle. In addition, in some examples, the controller 1412-1 determines the probability of collision of the neighboring vehicle based on data collected from other sources, such as sensors 1406, such as distance detector sensors and / or other sensors of the vehicle, roadside units communicating with the communication module 1420-1 via V2I communication, and / or remote servers communicating with the communication module 1420-1. For example, the controller 1412 - 1 determines the collision probability of the neighboring vehicle when it is determined that the neighboring vehicle has not detected a nearby object based on data collected by sensors of the vehicle and the neighboring vehicle.

[0305] In some examples, controller 1412-1 determines the collision probability based on the takeover time of the neighboring vehicle and / or the collision time of the neighboring vehicle. For example, the takeover time corresponds to the duration between (i) the neighboring vehicle issuing a request to perform a manual takeover and (ii) the operator of the neighboring vehicle manually taking over control of the neighboring vehicle. Controller 1412-1 is configured to determine the takeover time of the neighboring vehicle based on: measured characteristics of the neighboring vehicle (e.g., speed, acceleration, size, etc.), the operator of the neighboring vehicle (e.g., measured reaction time, etc.), and / or the environment of the neighboring vehicle (e.g., road conditions, weather conditions, etc.). In addition, if the current conditions are maintained, the collision time corresponds to the time when the neighboring vehicle collides with another vehicle (e.g., a third vehicle) and / or an object (e.g., a guardrail, a highway lane divider, etc.).

[0306] Additionally, or alternatively, the controller 1412-1 is configured to determine the collision time of the neighboring vehicle based on the speed, acceleration, driving direction, distance to the object, steering angle required to avoid the object, steering angle change rate, and / or other measured characteristics of the neighboring vehicle collected by the communication module 1420-1 from the neighboring vehicle through V2V communication. In addition, the controller 1412-1 is configured to determine the collision probability of the vehicle based on the collision probability of the neighboring vehicle.

[0307] When the collision probability of the adjacent vehicle is determined and it is determined that the collision is possible, the autonomous unit 408-1 autonomously performs (e.g., for ADAS) defensive driving maneuvers to prevent the vehicle from being involved in a collision caused by the adjacent vehicle. For example, autonomous defensive driving maneuvers include deceleration, emergency braking, lane changes, position changes within the current driving lane, etc. In some examples, the autonomous unit 1408-1 is configured to initiate a defensive driving maneuver before the takeover time of the adjacent vehicle has ended. That is, the controller 1412-1 is configured to cause the autonomous unit 1408-1 to perform a defensive driving maneuver before the operator of the adjacent vehicle manually takes over the control of the adjacent vehicle. In addition, in some examples, in response to determining that the collision probability is less than a first threshold and greater than a second threshold, the controller 1412-1 (e.g., via the HMI unit 1404) issues an audio, visual, tactile and / or other alarm to enable the operator of the vehicle to request manual takeover. By issuing such an alarm, the controller 1412-1 enables the operator of the vehicle to safely control the vehicle before the adjacent vehicle may be involved in a collision. Additionally or alternatively, the controller 1412 - 1 is configured to perform other defensive measures (eg, pre-filling the brake fluid lines) in response to determining that the probability of a collision is greater than a threshold (eg, a second threshold, a third threshold).

[0308] The communication module uses vehicle-to-network (V2N), vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), vehicle-to-cloud (V2C), vehicle-to-pedestrian (V2P), vehicle-to-device (V2D), and vehicle-to-grid (V2G) communication systems to achieve in-vehicle communication, communication with other vehicles, infrastructure communication, grid communication, etc. For example, if congestion occurs during the driving of a vehicle or a car for some reason, then, first, the system detects the traffic jam. Then, the system notifies nearby or surrounding vehicles or vehicles communicating with the communication module of the vehicle. For example, the vehicle uses a message protocol, that is, a message sent to other vehicles by broadcasting.

[0309] In an embodiment, a connection is established between a vehicle and a nearby vehicle (i.e., a surrounding car). The nearby vehicle is detected by a control module. The nearby vehicle is detected by exchanging handshake signals. Handshake is an automated process of negotiating to establish a communication channel between entities. The processor sends a start signal through the communication channel to detect a nearby vehicle. If there is a vehicle nearby, the processor can receive a confirmation signal from the nearby vehicle. After receiving the confirmation signal, the processor establishes a secure connection with the nearby vehicle. The processor can receive a signal from the nearby vehicle at the communication module. The processor can further automatically determine the source of the signal. The processor communicatively connects the communication module to the nearby vehicle. Then, the processor is configured to send a message to the nearby vehicle and / or receive a message from the nearby vehicle. The signal received by the communication module can be analyzed to identify the source of the signal, thereby determining the location of the nearby vehicle.

[0310] In an embodiment, the system is capable of two-way communication. The system sends a signal and then receives a signal / communication. In an embodiment, the communication may be permission to access to control another vehicle. In another embodiment, the communication may be an incremental control communication, e.g., initial control of speed up to 10 miles per hour, then another 10 miles per hour, etc.

[0311] As a first step of the method according to the present disclosure, a data link is established between the vehicle and a nearby vehicle or any other external device to allow data to be exchanged between the vehicle and the nearby vehicle or any other external device in the form of two-way communication. For example, this can be done via a radio link or a data cable. Thus, a nearby vehicle or any other external device can receive data from the host vehicle, or the host vehicle can request data from a nearby vehicle or any other external device.

[0312] In an embodiment, the two-way communication includes data collection means and is designed to mutually exchange data bidirectionally. In addition, at least the vehicle includes logic means for collecting data and arranging it into a specific protocol according to the protocol of the receiving entity.

[0313] Initially, a data link for two-way communication is established, via which the host vehicle and nearby vehicles or any other external device can communicate with each other and thus request or exchange data, wherein the data link can be implemented, for example, as a cable link or a radio link.

[0314] As described herein, bidirectional communication has various advantages. In various embodiments, data is communicated and transferred at appropriate intervals, including, for example, 200 millisecond (ms) intervals, 100 ms intervals, 50 ms intervals, 20 ms intervals, 10 ms intervals, or even more frequently and / or in real time or near real time, so as to allow the vehicle to respond to or otherwise react to the data. Bidirectional IR communication can be used to facilitate data exchange.

[0315] The device for performing two-way communication in a vehicle according to an embodiment can be by means of a personal area network (PAN) modem. Therefore, a user can access an external device using a vehicle information terminal, and then can store, move, and delete data that the user wants.

[0316] In an embodiment, the vehicles may transmit messages, communicate. It may use any combination of vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), or vehicle-to-infrastructure (V2I) types of communication. In an embodiment, it uses vehicle-to-vehicle (V2V) communication, which enables vehicles to wirelessly exchange information (communication), such as information about their speed, location, and heading.

[0317] In an embodiment, the message transmission protocol includes at least one of the Advanced Message Queuing Protocol (AMQP), Message Queuing Telemetry Transport (MQTT), Simple (or Streaming) Text-Oriented Messaging Protocol (STOMP), MQTT-S (an extension of open publish / subscribe MQTT), which are widely used in IoT-based technologies and edge networks.

[0318] Fig.15 An example message of a dedicated short range communication (DSRC) message format is shown. Fig.15Shown: (i) Basic Safety Message (BSM) format: BSM is a basic message type in DSRC, used to transmit information about the status, positioning and movement of a vehicle to other vehicles in the area. In the example message, the header contains the message ID and protocol version, while the data part contains specific information about the vehicle, such as vehicle ID, speed, heading and position. The timestamp indicates when the message was generated and can be used by other vehicles to determine the current status of the transmitting vehicle; (ii) Signal Phase and Timing (SPaT) format: SPaT messages provide information about the current status of traffic signals (lights) at intersections, including the currently active phase and when the signal changes. Vehicles can use this information to optimize their driving behavior and avoid collisions. In the example message, the header contains the message ID and protocol version, while the data part includes the ID of the intersection, the status of the movement phase, and the remaining time until the next and subsequent phases; (iii) Roadside Alert (RSA) format: RSA messages are used to notify vehicles of dangers or other important information on the road ahead, such as construction zones, accidents, or detours. In the example message, the header contains the message ID and protocol version, while the data portion includes the alert type (structure), distance to the alert, direction of the alert, and a text message providing other details about the alert.

[0319] Depending on the embodiment, other possible elements may also be included in each message format. For example, the BSM format may also include the following information: acceleration, size and weight, vehicle safety extensions (including brakes, lights, wipers, transmission and power system), vehicle size and vehicle identification information (including identification number and vehicle type).

[0320] In addition to the three examples provided previously, there are several other message formats available in the DSRC protocol. Some of these message formats include: MAP (Path Setting Message Assistance) format: This message provides a map of the road network, including information about road layout, speed limits, and other relevant details; CAM (Collaborative Awareness Message) format: Similar to the BSM message format, this message provides information about the vehicle's current state, such as speed, location, and heading. CAM messages are typically sent at a higher frequency than BSM messages to support more accurate situational awareness; DENM (Decentralized Environmental Notification Message) format: This message provides information about environmental conditions that may affect driving, such as weather conditions, visibility, and road conditions; EV (Emergency Vehicle) format: This message provides information about emergency vehicles, such as their location and direction of travel, to help other drivers navigate safely around them; TCI (Traffic Control Information) format: This message provides information about traffic conditions, such as congestion, accidents, and construction zones, to help drivers make informed decisions about routes.

[0321] Overall, the various message formats available in the DSRC protocol support a wide range of use cases, from basic vehicle-to-vehicle communications to more complex applications such as real-time traffic management and emergency response.

[0322] Fig.16A The structure of a neural network / machine learning model with a feedback loop is shown. The artificial neural network (ANN) model includes an input layer, one or more hidden layers, and an output layer. Each node or artificial neuron is connected to another node or artificial neuron and has an associated weight and threshold. If the output of any single node is above a specified threshold, the node is activated and the data is sent to the next layer of the network. Otherwise, no data is passed to the next layer of the network. A machine learning model or ANN model can be trained on a set of data to accept a request in the form of input data, predict the input data, and then provide a response. The model can learn from the data. Learning can be supervised learning and / or unsupervised learning, and can be based on different scenarios and use different data sets. Supervised learning includes the logic of using at least one of a decision tree, logistic regression, and a support vector machine. Unsupervised learning includes the logic of using at least one of k-means clustering, hierarchical clustering, hidden Markov models, and a priori algorithms. The output layer can predict or detect traffic conditions, the probability of a vehicle crossing an intersection area, and the probability of detecting a rear vehicle crossing an intersection area based on the input data.

[0323] In an embodiment, the ANN may be a deep neural network (DNN), which is a multi-layer tandem neural network including an artificial neural network (ANN), a convolutional neural network (CNN), and a recurrent neural network (RNN), which can recognize features from inputs, conduct expert reviews, and perform actions that require prediction, creative thinking, and analysis. In an embodiment, the ANN may be a recurrent neural network (RNN), which is an artificial neural network (ANN) that uses sequential data or time series data. Deep learning algorithms are often used for sequential or temporal problems, such as language translation, natural language processing (NLP), speech recognition, and image recognition. Like feedforward and convolutional neural networks (CNNs), recurrent neural networks use training data for learning. Their difference lies in their "memory" because they obtain information from previous inputs through feedback loops to affect current inputs and outputs. The output of the output layer in the neural network model is fed back to the model through feedback. When training the model, the changes in the weights in the hidden layer will be adjusted to better fit the expected output. This will enable the model to provide results with fewer errors.

[0324] Neural networks are characterized by having feedback loops that dynamically adjust the system output as it learns from new data. In machine learning, backpropagation and feedback loops are used to train AI models and continuously improve them as they are used. As more incoming data is received by the model, the model has more opportunities to learn from the data. The feedback loop or backpropagation algorithm identifies inconsistencies and feeds the corrected information back into the model as input.

[0325] Even though AI / ML models are trained very well with large amounts of labeled data and concept groups, after a while the performance of the model may degrade when new, unlabeled inputs are added for a number of reasons including, but not limited to, concept drift, loss of recall precision due to deviations from true positives, and data drift over time. The model’s feedback loop maintains the accuracy of AI results and ensures that the model maintains its performance and improves even as new unlabeled data is ingested. A feedback loop is the process by which the prediction output of an AI model is reused to train a new version of the model.

[0326] Initially, when training an AI / ML model, some labeled samples, including positive and negative examples of concepts (e.g., the probability of crossing an intersection area), are used for model learning. Then, the model is tested using unlabeled data. By using, for example, deep learning and neural networks, the model can predict whether the desired concept (e.g., the probability of crossing an intersection area) is in an unlabeled image. Each image is assigned a probability score, where a higher score indicates a higher confidence in the model's prediction. When a model gives an image a high probability score, it is automatically labeled with the predicted concept. However, in the case where the model returns a low probability score, this input can be sent to a controller (which can be a human regulator), which verifies and corrects the result if necessary. Human regulators may only be used in special cases. The feedback loop dynamically feeds the automatically labeled or controller-verified labeled data back to the model and uses it as training data so that the system can improve its predictions in real time and dynamically.

[0327] Fig. 16BThe structure of a neural network / machine learning model with reinforcement learning is shown. The network receives feedback from an authorized networked environment. Although the system is similar to supervised learning, the feedback obtained in this case is evaluative rather than instructive, meaning there is no teacher as in supervised learning. After receiving the feedback, the network makes adjustments to the weights to obtain better predictions in the future. Machine learning techniques, such as deep learning, allow the model to take labeled training data and learn to recognize these concepts in subsequent data and images. The model can be fed new data for testing, so the training is reinforced by feeding the model with data that it has already predicted. If the machine learning model has a feedback loop, the learning is further reinforced for every true output of the system. Feedback loops ensure that AI results do not stagnate. By incorporating feedback loops, the model output keeps dynamically improving with use / time.

[0328] In an embodiment, the system may include a network security module.

[0329] In one aspect, a secure communication management (SCM) computer device for providing a secure data connection is provided. The SCM computer device includes a processor 1708 in communication with a memory. The processor 1708 is programmed to receive a first data message from a first device. The first data message has a standardized data format. The processor 1708 is also programmed to analyze the first data message for potential cybersecurity threats. If it is determined that the first data message does not contain a cybersecurity threat, the processor 1708 is further programmed to convert the first data message into a first data format associated with a vehicle environment, and send the converted first data message to a communication module using a first communication protocol associated with a negotiated protocol.

[0330] According to an embodiment, security authentication of data transmission includes providing a hardware-based security engine (HSE) set in a network security module, wherein the HSE is manufactured in a secure environment and certified as part of an approved network in the secure environment; using the HSE to perform asynchronous authentication, verification and encryption of data, storing user permission data and connection status data in an access control list for defining an allowed data communication path of the approved network, so that the network security module can communicate with other computing system entities (e.g., communication modules) in the access control list, and using the security engine to perform synchronous verification and encryption of data includes using a hardware-based module to identify a user device (UD), wherein the user device includes credentials embodied in hardware, and the hardware-based module is equipped with one or more security aspects for protecting the communication, wherein the security aspects include the hardware-based module communicating with the user of the user device and the HSE.

[0331] In an embodiment, Fig.17A A block diagram of a network security module is shown. Data communications between the system 1700 and the server 1770 via the communication module 1712 are first verified by the information security management module 1732 before being transmitted from the system to the server or from the server to the system. The information security management module is operable to analyze the data for potential network security threats, encrypt the data when no network security threats are detected, and transmit the encrypted data to the system or server.

[0332] In an embodiment, the network security module also includes an information security management module that provides isolation between the system and the server. Fig. 17B A flow chart of protecting data by a network security module 1730 is shown. At step 1740, the information security management module is operable to receive data from the communication module. At step 1741, the information security management module exchanges security keys at the beginning of communication between the communication module and the server. At step 1742, the information security management module receives the security key from the server. At step 1743, the information security management module authenticates the identity of the server by verifying the security key. At step 1744, the information security management module analyzes the security key to find potential network security threats. At step 1745, the information security management module negotiates an encryption key between the communication module and the server. At step 1746, the information security management module receives encrypted data. At step 1747, when no network security threat is detected, the information security management module sends the encrypted data to the server.

[0333] In an embodiment, Fig. 17C A flow chart of protecting data by the network security module 1730 is shown. At step 1751, the information security management module is operable to exchange security keys at the beginning of communication between the communication module and the server. At step 1752, the information security management module receives the security key from the server. At step 1753, the information security management module authenticates the identity of the server by verifying the security key. At step 1754, the information security management module analyzes the security key to find potential network security threats. At step 1755, the information security management module negotiates an encryption key between the communication module and the server. At step 1756, the information security management module receives encrypted data. At step 1757, the information security management module decrypts the encrypted data and performs an integrity check on the decrypted data. At step 1758, when no network security threat is detected, the information security management module sends the decrypted data to the communication module.

[0334] In an embodiment, the integrity check is a hash signature verification using the Secure Hash Algorithm 256 (SHA256) or similar method.

[0335] In an embodiment, the information security management module is configured to perform asynchronous authentication and verification of communications between the communication module and the server.

[0336] In an embodiment, the information security management module is configured to issue an alarm when a network security threat is detected. In an embodiment, the information security management module is configured to discard the received encrypted data if the integrity check on the encrypted data fails.

[0337] In an embodiment, the information security management module is configured to check the integrity of the decrypted data by checking for accuracy, consistency and any possible data loss during communication through the communication module.

[0338] In an embodiment, the server is physically isolated from the system by an information security management module. Fig.17A As shown, when the system communicates with the server, identity authentication is first performed on the system and the server. The system is responsible for communicating / exchanging the system's public key and the signature of the public key with the server. The system's public key and the signature of the public key are sent to the information security management module. The information security management module decrypts the signature and verifies whether the decrypted public key is consistent with the received original public key. If the decrypted public key is verified, the identity authentication is passed. Similarly, the system and the server authenticate the information security management module. After the identity authentication is passed to the information security management module, the communicating parties, namely the system and the server, negotiate the encryption key and integrity check key for data communication between the communicating parties through the authenticated asymmetric key. The session ID number is transmitted during the identity authentication process, so that the key needs to be bound to the session ID number; when the system sends data outward, the information security gateway receives the data through the communication module, performs integrity authentication on the data, and then encrypts the data through the negotiated key, and finally transmits the data to the server through the communication module. When the information security management module receives data through the communication module, it first decrypts the data and verifies the integrity of the decrypted data. If the verification passes, the data is sent through the communication module; otherwise, the data will be discarded.

[0339] In an embodiment, identity authentication is achieved by using an asymmetric key with a signature.

[0340] In an embodiment, the signature is implemented by a pair of asymmetric keys trusted by the information security management module and the system, where the private key is used to sign the identities of the two communicating parties, and the public key is used to verify whether the identities of the two communicating parties are signed. The signing identity includes a public key and a private key pair. In other words, the signing identity is called the common name of the certificate installed in the user's machine.

[0341] In an embodiment, both communicating parties need to authenticate their own identities through a pair of asymmetric keys, and the task responsible for communicating with the information security management module of the system is identified by a pair of unique asymmetric keys.

[0342] In an embodiment, the dynamic negotiated key is encrypted by using the Rivest-Shamir-Adleman (RSA) encryption algorithm. RSA is a public key cryptographic system widely used for secure data transmission. The negotiated key includes a data encryption key and a data integrity check key.

[0343] In an embodiment, the data encryption method is a triple data encryption algorithm (3DES). The integrity check algorithm is a message authentication code (HMAC-MD5-128) algorithm based on a hash function. When the data is output, an integrity check calculation is performed on the data, the calculated message authentication code (MAC) value is added to the header of the value data message, and then the 3DES algorithm is used to encrypt the data (including the MAC of the header), and the header information of the security layer is added after the data is encrypted, and then the data is sent to the next layer for processing. In an embodiment, the next layer refers to the transport layer in the Transmission Control Protocol / Internet Protocol (TCP / IP) model.

[0344] When the communication between the two parties begins data encryption and data integrity authentication, the information security management module ensures the security, reliability and confidentiality of the communication between the system and the server through identity authentication. This method is particularly suitable for embedded platforms with fewer resources and not connected to the public key infrastructure (PKI) system, and can ensure the security of data on the server under Internet conditions from being compromised by hacker attacks by ensuring the security and reliability of communication between the system and the server.

[0345] The embodiments described herein include only examples of systems and computer-implemented methods. Of course, for the purpose of describing one or more embodiments, it is not possible to describe every conceivable combination of components and / or computer-implemented methods, but one of ordinary skill in the art will recognize that many other combinations and / or permutations of the one or more embodiments are possible. In addition, to the extent that the terms "including," "having," "having," and the like are used in this detailed description, claims, appendices, and / or drawings, these terms are intended to be inclusive, similar to how the term "comprising" is interpreted when used as a transitional word in a claim.

[0346] Without departing from the spirit or features of the present invention, other specific forms may embody the present invention. The described embodiments are illustrative and non-restrictive in all respects. Therefore, the appended claims rather than the description herein indicate the scope of the present invention. All changes within the meaning and equivalent range of the claims are within their scope.

Claims

1. A method comprising: The control module determines the traffic conditions around the vehicle in real time; detecting, by the control module, movement of the vehicle toward a traffic light and an intersection area; The control module determines the time taken for a traffic light cycle; determining, by the control module, an average distance traveled by the vehicle during the traffic light cycle; and The control module uses a computing engine to calculate the probability of a vehicle crossing the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle, the intersection area information and the length of the vehicle. wherein, when it is calculated that the vehicle crosses the intersection area before the red signal of the traffic light cycle, the probability that the vehicle crosses the intersection area is set to true; and When it is calculated that a vehicle blocks an intersection area, the probability that the vehicle passes through the intersection area is set to false.

2. The method according to claim 1, wherein: The calculated probability that the vehicle crosses the intersection area is set to true also includes: It is calculated that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle, and the rear vehicles behind the vehicle block the intersection area.

3. The method according to claim 2, further comprising: The control module transmits a first message to nearby vehicles when it is calculated that the vehicle is the last vehicle to pass through the intersection area before the red signal of the traffic light cycle.

4. The method according to claim 1, further comprising: When the probability that the vehicle passes through the intersection area is set to true, a first command to pass through the intersection area is provided by the control module to an electric drive unit of the vehicle.

5. The method according to claim 1, further comprising: A second command to stop before the intersection area is provided by the control module to an electric drive unit of the vehicle when the probability that the vehicle passes through the intersection area is set to false.

6. The method according to claim 1, wherein: Determine the traffic conditions around the vehicle in real time including: Monitoring the speed of the vehicle through sensors; comparing a speed of the vehicle to a threshold speed; and When the speed of the vehicle is less than the threshold speed, traffic conditions around the vehicle are determined in real time.

7. The method according to claim 1, wherein: Determining the time taken for a traffic light cycle includes: Performing video analysis on the video by the computing engine; scanning the video and detecting intervals between a green signal and a red signal of a traffic light, and vice versa; and determining a time taken for the traffic light cycle based on the interval, wherein the video includes a frame sequence of an area of ​​interest around the vehicle within a predetermined time period; and The video includes a frame sequence including the vehicle and the traffic light.

8. The method according to claim 1, wherein: Determining, by the control module, an average distance traveled by the vehicle during a traffic light cycle includes: determining a first length traveled by the vehicle during a first traffic light cycle; determining a second length traveled by the vehicle during a second traffic light cycle; determining a third length that the vehicle moves during a third traffic light cycle; and An average distance traveled by a vehicle during a traffic light cycle is calculated based on the first length, the second length, and the third length.

9. The method according to claim 4, further comprising: Based on the first command, the vehicle is controlled by the electric drive unit to cross the intersection area before a red signal of a traffic light.

10. The method according to claim 9, further comprising: capturing a video of an area of ​​interest in front of the vehicle in real time, the area of ​​interest covering the vehicle and the intersection area; Identify the end point of the intersection area from the video in real time; and When the end point of the intersection area passes through the video of the area of ​​interest, it is determined that the vehicle has passed through the intersection area.

11. The method according to claim 10, further comprising: measuring the distance traveled by the vehicle in front of the end point of the intersection area; comparing the length of the vehicle and the distance traveled by the vehicle; as well as When the distance traveled by the vehicle is greater than the length of the vehicle, it is determined that the vehicle has passed through the intersection area.

12. The method according to claim 10, further comprising: measuring the distance traveled by the vehicle in front of the end point of the intersection area; comparing the length of the vehicle and the distance traveled by the vehicle; as well as When the distance traveled by the vehicle is less than the length of the vehicle, it is determined that the vehicle is blocking the intersection area.

13. The method according to claim 12, further comprising: Upon determining that the vehicle is blocking the intersection area, measuring a distance between the vehicle and a preceding vehicle in a lane of the vehicle and an adjacent lane of the vehicle; comparing the distance to the length of the vehicle; and A third command is transmitted to the electric drive unit to cause it to perform one of: move forward in the lane to a predetermined distance, and perform a lane change when the measured distance is x times the length of the vehicle.

14. The method according to claim 2, further comprising: Get the distance traveled by the vehicle after crossing the intersection area; Detecting whether there is a rear vehicle behind the vehicle; Detecting the rear vehicle in the intersection area; Obtaining the model and length of the rear vehicle; as well as Based on the distance traveled by the vehicle after crossing the intersection area, the length of the rear vehicle, the length of the vehicle, and the distance between the vehicle and the rear vehicle, the extent of the intersection area where the rear vehicle is blocking the intersection area is determined.

15. The method according to claim 14, wherein: The scope of the intersection area for determining that the rear vehicle is blocking the intersection area further includes: When the rear vehicle is blocking the intersection area, transmitting one of a first signal and a first notification to the front vehicle to cause it to move forward; monitoring the movement of the preceding vehicle; responsive to movement of the leading vehicle, steering the vehicle via an electric drive unit; and One of a second signal and a second notification is transmitted to the rear vehicle to move forward to a predetermined position to minimize obstruction of the intersection area.

16. A system comprising: Communication module; and A control module, the control module being operable to: Determine the traffic conditions around the vehicle in real time; Detecting vehicle movement towards traffic lights and intersection areas; Determine the time taken for a traffic light cycle; determining the average distance travelled by vehicles during a traffic light cycle; as well as The calculation engine calculates the probability of a vehicle crossing the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle, the intersection area information and the length of the vehicle. wherein, when it is calculated that the vehicle crosses the intersection area before the red signal of the traffic light cycle, the probability that the vehicle crosses the intersection area is set to true; and Among them, when it is calculated that the vehicle blocks the intersection area, the probability of the vehicle passing through the intersection area is set to false.

17. The system of claim 16, wherein: The system is configured as a component of the vehicle.

18. The system of claim 16, wherein: The computing engine includes a machine learning model.

19. A non-transitory computer readable medium storing a sequence of instructions which, when executed by a processor, result in: Determine the traffic conditions around the vehicle in real time; Detecting vehicle movement towards traffic lights and intersection areas; Determine the time taken for a traffic light cycle; determining the average distance travelled by vehicles during a traffic light cycle; and The calculation engine calculates the probability of a vehicle crossing the intersection area before the red signal of the traffic light cycle based on the average distance traveled by the vehicle, the intersection area information and the length of the vehicle. wherein, when it is calculated that the vehicle crosses the intersection area before the red signal of the traffic light cycle, the probability that the vehicle crosses the intersection area is set to true; and Among them, when it is calculated that the vehicle blocks the intersection area, the probability of the vehicle passing through the intersection area is set to false.

20. The non-transitory computer readable medium of claim 19, wherein: The probability of a vehicle crossing the intersection area before the red signal of the traffic light cycle is calculated by the calculation engine based on the average distance traveled by the vehicle, the intersection area information and the length of the vehicle, resulting in: obtain intersection area information, pedestrian crossing length, and bicycle box length from one of a traffic information database and a third-party service; Identify the current signal of the traffic light cycle in real time; as well as The probability that a vehicle crosses the intersection area before the red signal of the traffic light cycle is estimated based on the average distance traveled by the vehicle during the traffic light cycle, the intersection area information, the current signal of the traffic light cycle, the length of the pedestrian crossing, the length of the bicycle box, and the length of the vehicle.

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