Vehicle navigation method and device, electronic device and medium

Abstract

The present disclosure provides a vehicle navigation method and device, electronic equipment, computer readable storage medium and computer program product, relates to the computer field, and particularly relates to the intelligent traffic, automatic driving, auxiliary driving, high-precision map technical field. The implementation scheme is as follows: a green light end time corresponding to a target intersection to be passed by a target vehicle is acquired; a plurality of optional lanes corresponding to the target vehicle for passing the target intersection are determined; for each optional lane, a target time required for the target vehicle to pass the target intersection based on the optional lane is determined; based on the green light end time and the target time corresponding to each of the plurality of optional lanes, a score corresponding to each of the plurality of optional lanes is determined, and the score is used to represent at least one of a passing efficiency and a risk of the target vehicle passing the target intersection based on the corresponding optional lane; a target lane is determined in the plurality of optional lanes based on the score to generate a navigation instruction, and the navigation instruction is used to guide the target vehicle to pass the target intersection along the target lane.

Description

Technical Field

[0001] This disclosure relates to the field of computers, and more particularly to the fields of intelligent transportation, autonomous driving, driver assistance, and high-precision map technology, specifically to a vehicle navigation method, device, electronic device, computer-readable storage medium, and computer program product. Background Technology

[0002] Cloud computing refers to a technology system that provides access to a shared pool of physical or virtual resources via a network. These resources can include servers, operating systems, networks, software, applications, and storage devices, and can be deployed and managed on demand and in a self-service manner. Cloud computing technology can provide efficient and powerful data processing capabilities for applications such as artificial intelligence and blockchain, as well as for model training.

[0003] Urban intersections are the most complex traffic scenarios in urban roads, involving the most participants and experiencing the most frequent problems. They are nodes and hubs in the road traffic system, handling a large volume of traffic flow, and the smoothness of intersections directly affects traffic capacity. To meet the needs of efficient urban travel, vehicle-to-infrastructure (V2I) technology is typically used in urban transportation networks to transmit real-time traffic light status information to vehicles, assisting drivers in making safer and more efficient driving decisions.

[0004] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0005] This disclosure provides a vehicle navigation method, apparatus, electronic device, computer-readable storage medium, and computer program product.

[0006] According to one aspect of this disclosure, a vehicle navigation method is provided, comprising: obtaining the green light end time corresponding to a target intersection through which a target vehicle is to pass; determining multiple optional lanes for the target vehicle to pass through the target intersection, wherein the multiple optional lanes include the lane currently occupied by the target vehicle and adjacent lanes; for each of the multiple optional lanes, determining a target time required for the target vehicle to pass through the target intersection based on that optional lane; determining a score corresponding to each of the multiple optional lanes based on the green light end time and the target time corresponding to each of the multiple optional lanes, wherein the score is used to characterize at least one of the traffic efficiency and risk of the target vehicle passing through the target intersection based on the corresponding optional lane; and determining a target lane among the multiple optional lanes based on the score to generate a navigation instruction, wherein the navigation instruction is used to guide the target vehicle to pass through the target intersection along the target lane.

[0007] According to another aspect of this disclosure, a vehicle navigation device is provided, comprising: an acquisition unit configured to acquire the green light end time corresponding to a target intersection through which a target vehicle is to pass; a first determination unit configured to determine multiple optional lanes for the target vehicle to pass through the target intersection, wherein the multiple optional lanes include the lane currently occupied by the target vehicle and adjacent lanes; a second determination unit configured to determine, for each of the multiple optional lanes, a target time required for the target vehicle to pass through the target intersection based on that optional lane; a scoring unit configured to determine a score corresponding to each of the multiple optional lanes based on the green light end time and the target time corresponding to each of the multiple optional lanes, wherein the score is used to characterize at least one of the traffic efficiency and risk of the target vehicle passing through the target intersection based on the corresponding optional lane; and a navigation unit configured to determine a target lane among the multiple optional lanes based on the score to generate a navigation instruction, wherein the navigation instruction is used to guide the target vehicle to pass through the target intersection along the target lane.

[0008] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor to enable the at least one processor to perform the methods described in this disclosure.

[0009] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing a computer to perform the methods described in this disclosure.

[0010] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the methods described in this disclosure.

[0011] According to another aspect of this disclosure, a vehicle is provided that includes the electronic equipment described in this disclosure.

[0012] According to another aspect of this disclosure, an edge computing device is provided, including the electronic device described in this disclosure.

[0013] According to one or more embodiments of this disclosure, by quantifying and comparing multiple selectable lanes, low-load, high-efficiency traffic paths can be accurately identified, thereby optimizing intersection resource allocation, significantly reducing the passing delay and energy consumption of target vehicles, and improving the driving experience.

[0014] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0015] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0016] Figure 1 A schematic diagram of an exemplary system in which the various methods described herein may be implemented according to embodiments of the present disclosure is shown; Figure 2 A flowchart of a vehicle navigation method according to an embodiment of the present disclosure is shown; Figure 3 A schematic diagram of a vehicle waiting to pass through an intersection according to an embodiment of the present disclosure is shown; Figure 4 A schematic diagram of a traffic scenario including vehicles and multiple selectable lanes according to an embodiment of the present disclosure is shown; Figure 5 A structural block diagram of a vehicle navigation device according to an embodiment of the present disclosure is shown; Figure 6 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0017] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0018] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0019] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0020] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0021] Figure 1 A schematic diagram of an exemplary system 100 in which the various methods and apparatus described herein can be implemented according to embodiments of this disclosure is shown. Reference Figure 1 The system 100 includes a motor vehicle 110, a server 120, and one or more communication networks 130 that couple the motor vehicle 110 to the server 120.

[0022] In embodiments of this disclosure, the motor vehicle 110 may include a computing device according to embodiments of this disclosure and / or be configured to perform a method according to embodiments of this disclosure.

[0023] Server 120 may run one or more services or software applications that enable methods for implementing vehicle navigation. In some embodiments, server 120 may also provide other services or software applications that may include both non-virtual and virtual environments. Figure 1In the configuration shown, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or combinations thereof that can be executed by one or more processors. A user of motor vehicle 110 may sequentially interact with server 120 using one or more client applications to utilize the services provided by these components. It should be understood that various different system configurations are possible and may differ from system 100. Therefore, Figure 1 This is an example of a system used to implement the various methods described herein, and is not intended to be limiting.

[0024] Server 120 may include one or more general-purpose computers, special-purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for servers). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0025] The computing unit in server 120 can run one or more operating systems, including any of the aforementioned operating systems and any commercially available server operating system. Server 120 can also run any of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0026] In some implementations, server 120 may include one or more applications to analyze and merge data feeds and / or event updates received from vehicle 110. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of vehicle 110.

[0027] Network 130 can be any type of network well known to those skilled in the art, and can use any of a variety of available protocols (including, but not limited to, TCP / IP, SNA, IPX, etc.) to support data communication. By way of example only, one or more networks 130 can be satellite communication networks, local area networks (LANs), Ethernet-based networks, token ring networks, wide area networks (WANs), the Internet, virtual networks, virtual private networks (VPNs), intranets, extranets, public switched telephone networks (PSTNs), infrared networks, wireless networks (including, for example, Bluetooth, WiFi), and / or any combination of these with other networks.

[0028] System 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 150 may be used to store information such as audio files and video files. Databases 150 may reside in various locations. For example, a data repository used by server 120 may be local to server 120, or it may be located away from server 120 and may communicate with server 120 via a network-based or dedicated connection. Databases 150 may be of different types. In some embodiments, a data repository used by server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data from and from the databases in response to commands.

[0029] In some embodiments, one or more of the databases 150 may also be used by an application to store application data. The databases used by the application may be of different types, such as key-value stores, object stores, or regular stores supported by a file system.

[0030] Motor vehicle 110 may include sensors 111 for sensing the surrounding environment. Sensors 111 may include one or more of the following sensors: a visual camera, an infrared camera, an ultrasonic sensor, a millimeter-wave radar, and a lidar (LiDAR). Different sensors can provide different detection accuracy and range. Cameras may be mounted in front of, behind, or at other locations on the vehicle. Visual cameras can capture the situation inside and outside the vehicle in real time and present it to the driver and / or passengers. In addition, by analyzing the images captured by the visual cameras, information such as traffic light indications, intersection conditions, and the operating status of other vehicles can be obtained. Infrared cameras can capture objects in night vision conditions. Ultrasonic sensors may be mounted around the vehicle to measure the distance of objects outside the vehicle using the strong directionality of ultrasound. Millimeter-wave radar may be mounted in front of, behind, or at other locations on the vehicle to measure the distance of objects outside the vehicle using the characteristics of electromagnetic waves. LiDAR may be mounted in front of, behind, or at other locations on the vehicle to detect the edges and shape information of objects, thereby performing object recognition and tracking. Due to the Doppler effect, the radar device can also measure the speed changes of the vehicle and moving objects.

[0031] The motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., BeiDou, GPS, GLONASS, and GALILEO) from satellite 141 and generating coordinates based on these signals. The communication device 112 may also include a module for communicating with a mobile communication base station 142. The mobile communication network can implement any suitable communication technology, such as current or emerging wireless communication technologies (e.g., 5G technology) like GSM / GPRS, CDMA, and LTE. The communication device 112 may also have a vehicle-to-everything (V2X) module, configured to enable vehicle-to-the-world communication, for example, vehicle-to-vehicle (V2V) communication with other vehicles 143 and vehicle-to-infrastructure (V2I) communication with infrastructure 144. Furthermore, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to smartphones, tablets, or wearable devices such as watches) via, for example, a wireless local area network conforming to the IEEE 802.11 standard or Bluetooth. Using the communication device 112, the motor vehicle 110 can also access the server 120 via the network 130.

[0032] The motor vehicle 110 may also include a control unit 113. The control unit 113 may include a processor, such as a central processing unit (CPU) or a graphics processing unit (GPU), or other dedicated processors, that communicates with various types of computer-readable storage devices or media. The control unit 113 may include an autonomous driving system for automatically controlling various actuators in the vehicle. The autonomous driving system is configured to control the powertrain, steering system, and braking system of the motor vehicle 110 (not shown) via multiple actuators in response to inputs from multiple sensors 111 or other input devices to control acceleration, steering, and braking respectively, without human intervention or with limited human intervention. Some processing functions of the control unit 113 can be implemented via cloud computing. For example, some processing can be performed using an onboard processor while other processing can be performed using cloud computing resources. The control unit 113 may be configured to perform methods according to this disclosure. Furthermore, the control unit 113 may be implemented as an example of a computing device on the motor vehicle side (client) according to this disclosure. Figure 1 The system 100 can be configured and operated in various ways to enable the application of the various methods and apparatus described in this disclosure.

[0033] In the current field of assisted driving, vehicles mainly rely on two technical solutions to navigate signalized intersections. One is lane-level navigation based on high-precision maps. While it can provide accurate static lane topology relationships and prevent drivers from taking the wrong route, its decision-making logic is mainly based on static attributes such as the number of lanes and speed limits, completely lacking the ability to perceive real-time traffic light phases and lane-level queue lengths. This can easily lead to vehicles being guided to heavily congested lanes. The other is green wave speed guidance technology based on V2X or the cloud. It suggests a vehicle speed by calculating the ratio of distance to remaining time. However, in practical applications, this solution is usually based on an ideal free-flow model, without considering the actual queue congestion at the intersection and the start-up losses when the convoy starts. This often results in suggested speeds exceeding actual traffic capacity, poor robustness in saturated traffic flow scenarios, and may even increase the risk of rear-end collisions due to improper guidance.

[0034] Therefore, an embodiment of the present disclosure provides a vehicle navigation method. Figure 2 A flowchart of a vehicle navigation method according to an embodiment of the present disclosure is shown, such as... Figure 2 As shown, method 200 includes: obtaining the green light end time corresponding to the target intersection through which the target vehicle is to pass (step 210); determining multiple optional lanes for the target vehicle to pass through the target intersection, wherein the multiple optional lanes include the lane currently occupied by the target vehicle and adjacent lanes (step 220); for each of the multiple optional lanes, determining the target time required for the target vehicle to pass through the target intersection based on that optional lane (step 230); determining a score corresponding to each of the multiple optional lanes based on the green light end time and the target time corresponding to each of the multiple optional lanes, wherein the score is used to characterize at least one of the traffic efficiency and risk of the target vehicle passing through the target intersection based on the corresponding optional lane (step 240); and determining a target lane among the multiple optional lanes based on the score to generate a navigation instruction, wherein the navigation instruction is used to guide the target vehicle to pass through the target intersection along the target lane (step 250).

[0035] According to embodiments of this disclosure, by quantifying and comparing multiple selectable lanes, low-load, high-efficiency traffic paths can be accurately identified, thereby optimizing intersection resource allocation, significantly reducing the passing delay and energy consumption of target vehicles, and improving the driving experience.

[0036] In some embodiments, the green light end time corresponding to the target intersection is the basic time reference for assessing the feasibility of passage. Specifically, the timestamp can be flexibly defined according to the real-time status of the current traffic light: if it is currently in the green light phase, the time can be the most recent green light end time; if it is currently in the red light phase or the yellow light phase where the green light is about to end, the time can point to the end time of the next green light cycle.

[0037] In embodiments of this disclosure, a green light status indicates the status of a traffic light used to indicate that a vehicle may pass. In some embodiments, when a yellow light can also indicate that a vehicle may pass, the green light status and the yellow light status can be considered together as a broad green light status, and this is not a limitation.

[0038] In the actual acquisition process, parameters such as the type of the current traffic light, the remaining time of the current phase, and the preset duration of the next traffic light can be obtained. By parsing and accumulating these time-series data, the available passage time window for the target vehicle can be accurately locked.

[0039] In embodiments of this disclosure, the target intersection can refer to the stop line at which the target vehicle passes through the target intersection, such as a stop line used to mark entry into the target intersection. For example, when determining the target time in step 230, it can be the time it takes for the vehicle to reach the stop line; or, further, it can be combined with the vehicle's own geometry and the width of the intersection, the time it takes for the rear of the target vehicle to safely leave the stop line, thereby avoiding stagnation or traffic violations in the center area of ​​the intersection.

[0040] Figure 3 A schematic diagram of an intersection where a vehicle is about to pass, according to one embodiment of the present disclosure, is shown, wherein the intersection is a crossroads 310. It is understood, of course, that other types of intersections are possible and are not limited thereto. Figure 3 As shown, vehicle 330 can proceed in sequence through stop line 320 according to traffic light 350 to cross intersection 310.

[0041] In the embodiments of this disclosure, multiple selectable lanes represent all possible lane resources for a target vehicle to physically pass through under its current geographical location and navigation path. Specifically, the multiple selectable lanes first include the lane the target vehicle is currently in; secondly, they also include other lanes sequentially adjacent to the current lane that meet the navigation flow requirements, such as the left-hand or right-hand adjacent lanes in the same direction, or the left-hand adjacent lane of the left-hand adjacent lane in the same direction. These selectable lanes typically share a common directional attribute, i.e., they all point to the same target intersection exit.

[0042] For example, if there are three straight lanes ahead at an intersection, and the target vehicle is in the rightmost straight lane, multiple lane options will simultaneously cover that rightmost lane and the two straight lanes to its left. By scanning and collecting data on these competing lanes in parallel, lane-changing decision support can be provided for the target vehicle in complex dynamic traffic flow, ensuring that it always travels in the lane with the least traffic pressure and the highest probability of passing.

[0043] For example, Figure 3 The available lanes for vehicle 330 include lane 340. Both the lane where vehicle 330 is currently located and lane 340 are used to guide vehicles to make right turns based on the intersection traffic light 350.

[0044] In step 230, the target estimated time required for the target vehicle to pass through the target intersection based on the available lane is determined. For example, this target time can be determined by dividing the distance by the speed.

[0045] For example, if there are no vehicles queuing in the current lane, the target time can be determined based on the distance of the target vehicle from the target intersection, such as the distance from the stop line of the lane, and the lane speed in the corresponding lane.

[0046] According to some embodiments, the method according to this disclosure may further include: for each of the plurality of selectable lanes, in response to determining that there are queued vehicles ahead of the selectable lane, obtaining the queue information and lane travel speed corresponding to the selectable lane.

[0047] Therefore, according to some embodiments, determining the target time required for the target vehicle to pass through the target intersection based on the optional lane may include: determining the target time required for the target vehicle to pass through the target intersection based on the queuing information and the lane travel speed. The target time is used to characterize the time required for both the queued vehicles and the target vehicle to pass through the target intersection based on the optional lane.

[0048] Specifically, queuing information and lane speeds for each of the multiple selectable lanes can be obtained. The queuing information and lane speeds constitute a real-time snapshot of the intersection's traffic saturation. In some embodiments, queuing information may include queue length, such as the length of the vehicle queue extending from the stop line; while lane speeds can reflect the activity level of traffic flow within the current lane, such as the current speed of the target vehicle and / or the current lane flow rate for that selectable lane. By combining these two sets of data, the differences in traffic resistance caused by queue overflow in different lanes, even with the same green light duration, can be identified, providing accurate input parameters for subsequent estimated time calculations.

[0049] According to some embodiments, the queuing information includes the queue length and the target remaining distance between the target vehicle and the end of the queue. Determining the target time required for the target vehicle to pass through the target intersection based on the available lane may include: determining the target time required for the target vehicle to pass through the target intersection based on the queue length, the target remaining distance, and the lane travel speed.

[0050] Specifically, in some embodiments, the target time may consist of two parts: the first part is the free-flow time for the target vehicle to reach the end of the current queue; the second part is the "dissipation time" constrained by the queue. These two parts can be determined separately. For example, when calculating the dissipation time, it can be based on the current average flow speed of the road segment in the map data included in the acquired lane travel speed; while when calculating the free-flow time, it can be based on the current travel speed of the target vehicle included in the lane travel speed, without any limitation.

[0051] In some embodiments, the remaining target distance can be determined based on a uniform lateral coordinate of the target vehicle. That is, based on the current lateral coordinate of the target vehicle, the longitudinal coordinate corresponding to that lateral coordinate can be determined in the Frenet coordinate system for each available lane, thereby determining the longitudinal distance between the current target vehicle and the tail of the queue. Then, for each available lane, this distance is added to the queue length and divided by the lane travel speed to determine the target time required for the target vehicle to pass through the target intersection using that available lane.

[0052] The Frenet coordinate system is a widely used coordinate system in autonomous driving and path planning. It uses the road's centerline as a reference and establishes the coordinate system using tangent and normal vectors. In the Frenet coordinate system, the road's centerline is used as a reference line, and a coordinate system is established using the reference line's tangent and normal vectors. In other embodiments, such as... Figure 4 As shown, it uses vehicle 410 itself as the origin 440, with the coordinate axes perpendicular to each other, divided into the s direction (i.e., the direction along the reference line, usually called longitudinal) and the d direction (i.e., the current normal to the reference line, called lateral). Figure 4 In this scenario, vehicle 410 is located in lane 420. If it is determined that if vehicle 410 enters lane 430, its remaining target distance can be calculated based on its current position (origin 440) and its lateral and longitudinal coordinates in the Frenet coordinate system corresponding to the adjacent lane 430, i.e., coordinate point 450. This allows for the determination of its remaining target distance in that lane. Alternatively, the lateral position difference between the two lanes can be ignored, considering only the longitudinal position difference. This allows for the prediction of the traffic scenario the vehicle will face after changing lanes before the actual lane change operation.

[0053] According to some embodiments, the method according to this disclosure may further include: obtaining queuing information and lane driving speed corresponding to each of multiple selectable lanes, wherein the queuing information includes the target remaining distance of the target vehicle from the end of the queue and the number of vehicles in the queue.

[0054] According to some embodiments, the queuing information includes the target remaining distance of the target vehicle from the end of the queue and the number of vehicles in the queue. Therefore, determining the target time required for the target vehicle to pass through the target intersection based on the optional lane may include: determining a first estimated time based on the target remaining distance and the lane travel speed; determining a second estimated time based on the number of vehicles and a preset saturation headway; and determining the target time required for the target vehicle to pass through the target intersection based on the first estimated time and the second estimated time.

[0055] Similarly, as mentioned above, the first estimated time can be determined by dividing the remaining distance to the target by the lane speed. This lane speed could be, for example, the current average flow rate of that road segment in the map data, or the current speed of the target vehicle, which will not be elaborated further here.

[0056] In the above embodiments, the queue dissipation time of the corresponding lane (i.e., the time required for the queued vehicles to pass through the target intersection based on the two key parameters of the number of vehicles and the saturation headway) can be quantified, i.e., the second estimated time.

[0057] The second estimated time focuses on describing the "dissipation process." Based on the number of vehicles in the queue and a preset saturation headway, it calculates the cumulative time required from the start of the first vehicle in the queue to the last vehicle in front of the target vehicle crossing the stop line. By superimposing or logically combining these two time dimensions, the target time for the target vehicle to cross the stop line can be finally determined.

[0058] In the above embodiments, the introduction of saturated headway allows for a more objective quantification of the intersection's traffic capacity potential. Saturated headway refers to the average time interval between two vehicles passing the same reference point (e.g., the stop line) during the green light period at an intersection, when a queuing vehicle line crosses the stop line at a stable maximum flow rate. This parameter reflects the dispersal capacity of a specific lane under ideal saturation conditions and is a core indicator for measuring intersection efficiency in traffic engineering. By combining the preset saturated headway with the real-time sensed number of vehicles, it is possible to estimate the spatiotemporal occupancy required for the entire queuing line to dissipate, thereby achieving a refined measurement of the traffic time window.

[0059] In the above embodiments, the micro-dynamic characteristics of traffic flow are considered. By introducing saturated headway to quantify the second estimated time, lane differences such as "longer physical length but dissipates very quickly" or "shorter physical length but dissipates very slowly" can be predicted more accurately, which significantly improves the prediction accuracy of the estimated time. This provides the most efficient target lane selection and greatly avoids the time loss caused by blind queuing.

[0060] In embodiments according to this disclosure, the system can utilize multiple sensing dimensions to perceive queue information: on the one hand, it can use sensing devices such as roadside cameras or lidar installed above the target intersection to statistically analyze the absolute number of vehicles queuing behind the stop line in each lane and the physical length they occupy in real time using computer vision and point cloud analysis algorithms; on the other hand, it can directly obtain BBS (Basic Safety Messages) sent by surrounding vehicles through V2X wireless communication technology, or combine high-precision maps with vehicle-mounted sensing sensors (such as forward-facing cameras and millimeter-wave radar) to scan the distribution of the queuing vehicles ahead, thereby obtaining real-time and accurate queue length and total number of vehicles.

[0061] According to some embodiments, the queuing information further includes: information on key vehicles in the queue. Determining the second estimated time based on the number of vehicles and a preset saturation headway may include: in response to determining that the queue for the selectable lane includes a key vehicle, determining a correction factor corresponding to the key vehicle, wherein the correction factor is used to correct the start time of the key vehicle in the queue; and determining the second estimated time based on the correction factor, the number of vehicles, and the preset saturation headway.

[0062] In real-world traffic scenarios, convoy compositions are often heterogeneous, meaning that not all vehicles in the queue have uniform dynamic performance. By introducing the identification of key vehicles through the above embodiments, the accuracy of estimated travel time calculations can be further improved, significantly enhancing the robustness of the navigation system under complex and heterogeneous traffic flows. Through refined correction of estimated travel time based on correction factors, it is ensured that target vehicles, when selecting target lanes, can effectively avoid lanes that appear to have short queues but are actually occupied by large vehicles and have extremely low traffic efficiency, thereby achieving more reliable traffic decisions and reducing unexpected waiting times at stop lines.

[0063] In some embodiments, a critical vehicle may refer to a heterogeneous vehicle whose physical size, power performance, or driving behavior differs significantly from that of a regular passenger car, such as a large truck, bus, trailer, or special-purpose vehicle. Therefore, critical vehicle information may include, for example, the specific type label (e.g., identifying the vehicle as a heavy truck) and number of these vehicles in the queue, as well as the critical vehicle's position in the queue (e.g., the kth vehicle in the queue). Because these vehicles have physical characteristics such as slow start-up, delayed acceleration, and long braking distances, simply equating them to cars for calculations will lead to serious deviations in the estimation of intersection passage windows.

[0064] Therefore, to eliminate the calculation errors caused by differences in vehicle dynamics models, a correction factor is introduced in the above embodiments. For example, the correction factor can be a preset value or function specifically used to compensate for the start-up delay and acceleration loss of key vehicles in the queuing sequence. For instance, when a large bus is identified in the queue of a selectable lane, the correction factor corresponding to that vehicle type is invoked to correct the start-up time at that location, such as by adding a preset time constant to the basic start-up loss time. This correction factor reflects the special characteristics of large vehicles in actual traffic flow: not only are their own starts slow, but they may also cause the acceleration curves of subsequent following vehicles to shift backward.

[0065] According to some embodiments, determining the second estimated time based on the number of vehicles and a preset saturation headway includes: obtaining a preset start-up loss time, wherein the start-up loss time is used to characterize the delay time required for the convoy to start; and determining the second estimated time based on the start-up loss time, the number of vehicles, and the preset saturation headway.

[0066] In real-world traffic environments, the transition of a convoy from a stationary state to a moving state is not instantaneous but involves a significant physical lag. Therefore, by introducing a start-up loss time, the accuracy of the estimated travel time calculation can be further improved.

[0067] In some examples, start-up loss time can be used to refer to the delay that typically occurs when the first vehicle in a queue is in motion after the green light signal turns on, due to the driver's visual perception reaction, intention decision-making, and the mechanical coordination of the vehicle's powertrain (such as clutch engagement and engine speed increase). This value can usually be preset between 2 and 3 seconds.

[0068] When executing the calculation logic, this startup loss time can be treated as an independent time increment and added to the base travel time calculated based on queuing conditions and lane speeds. By including this loss term, the complete dynamic process from the first vehicle crossing the stop line to the subsequent traffic flow starting in sequence can be simulated more accurately. This avoids the problem of underestimating travel time due to idealized modeling and achieves accurate compensation for platoon startup time.

[0069] In one exemplary embodiment according to this disclosure, for each selectable lane, the target time corresponding to that selectable lane can be determined by the following formula. :

[0070]

[0071] in, This is used to indicate the first estimated time mentioned above, which is the physical travel time for the target vehicle to travel from its current position to the end of the queue of vehicles. At this time, the current position is the position corresponding to the target vehicle after it enters the selectable lane. Please refer to the description of the remaining distance of the target vehicle mentioned above, which will not be repeated here. Indicates the startup time loss; This represents the preset saturation headway for the kth key vehicle in the queue, such as a saturation headway of 2.5 seconds for each vehicle. Indicates the number of vehicles in the queue; This represents the correction factor corresponding to the i-th critical vehicle, where m is the number of critical vehicles in the queue, for example, all of which are preset to 1.5 seconds.

[0072] In embodiments according to this disclosure, the score corresponding to the selectable lane determined in step 240 can be a comprehensive indicator used to measure the quality of a target vehicle's passage through the intersection. For example, this score can use specific algorithmic logic to correlate and compare the estimated travel time (i.e., target time) of each lane with the green light end time to quantify at least one of traffic efficiency and risk. For example, when the estimated travel time of a lane is much shorter than the green light end time, its score is higher, indicating that the lane has high traffic redundancy and high efficiency; conversely, if the estimated travel time is close to or exceeds the green light end time, the score is lower, indicating a risk of not being able to pass through in the current green light cycle. Finally, the score is used to select the optimal lane from other lanes, ensuring that navigation instructions are fast and safe.

[0073] According to some embodiments, determining the scores corresponding to the multiple optional lanes based on the green light end time and the target time corresponding to each of the multiple optional lanes includes: determining a first score corresponding to each of the multiple optional lanes based on the difference between the green light end time and the corresponding target time, wherein the first score is used to characterize the traffic efficiency of the target vehicle passing through the target intersection based on the corresponding optional lane; and determining the scores corresponding to each of the multiple optional lanes based on the first score.

[0074] When determining the efficiency of each lane, the system standardizes decision-making by transforming the predicted time dimension into an intuitive scoring dimension. The first score can be determined based on the difference between the green light end time and the corresponding target time, such as the green light end time minus the target time. This difference physically represents the "time redundancy" of the intersection for target vehicles. When the difference is positive and large, it indicates that target vehicles have sufficient time to cross the stop line before the green light ends, resulting in a high first score and indicating excellent traffic efficiency for that lane. If the difference is negative or close to zero, it indicates that vehicles are unlikely to pass within the current cycle, resulting in a low first score. Through this difference mapping, the system can quantify the traffic pressure of different lanes in real time into comparable efficiency indicators.

[0075] In practical applications, the scores corresponding to multiple selectable lanes can be determined based on the first score. For example, the first score can be equated with the score corresponding to the selectable lane, or a comprehensive score generated based on the first score and combined with other potential dimensions can be used as the score corresponding to the selectable lane.

[0076] In the above embodiments, the scores corresponding to the multiple selectable lanes determined by the first score can be used to prioritize guiding vehicles into the lane with the highest redundancy (i.e., the highest first score) to cope with possible emergencies (such as a sudden breakdown of the vehicle in front or temporary avoidance). This scoring mechanism based on time surplus effectively transforms the uncertain traffic environment into a quantifiable traffic efficiency model, greatly improving the success rate and driving smoothness of autonomous driving or assisted driving systems at signalized intersections.

[0077] According to some embodiments, determining the scores corresponding to the multiple optional lanes based on the green light end time and the target time corresponding to each of the multiple optional lanes includes: determining a second score corresponding to each of the multiple optional lanes, wherein the second score is determined based on whether the corresponding optional lane is the lane where the target vehicle is currently located or another lane other than the current lane, wherein the second score is used to characterize the risk of the target vehicle passing through the target intersection based on the corresponding optional lane; and determining the scores corresponding to each of the multiple optional lanes based on the second score.

[0078] In actual driving decisions, traffic efficiency is not the only metric; safety and stability are equally crucial. By introducing a second rating, a quantitative assessment mechanism for driving risk is established. The determination logic of the second rating is primarily based on whether the target lane is the vehicle's current lane or a different lane that requires a lane change. Understandably, staying in the current lane generally carries the lowest risk, while lane changes inherently involve potential risks such as blind spot obstruction, rear-end collisions, and operational errors. Therefore, the second rating guides driving intentions by assigning differentiated risk weights to lanes with different attributes.

[0079] Specifically, in some embodiments, the lane where the target vehicle is currently located can be assigned a high positive value or a value of 0 (indicating low risk) to reflect the principle of "original lane priority"; while for other optional lanes that need to perform lane changing actions, a relatively low positive value or a negative value (indicating high risk) can be assigned, for example, based on the difficulty of lane changing, the speed difference between adjacent lanes, and the distance between the target vehicle and the lane changing point, etc.

[0080] In the above embodiments, by introducing a second score, frequent and meaningless lane-changing behavior by vehicles before intersections can be effectively suppressed, greatly improving driving smoothness and safety. That is, lane changes will only be chosen when the adjacent lane is significantly more spacious and has a greater advantage in terms of traffic flow; otherwise, the vehicle tends to maintain its original lane. This not only reduces the probability of traffic accidents but also avoids traffic flow disruptions caused by frequent lane changes, making the navigation system's performance more in line with human driving preferences and providing more robust and predictable vehicle guidance services.

[0081] According to some embodiments, for lanes other than the currently occupied lane, the second score is determined based on at least one of a third score characterizing safety risk and a fourth score characterizing regulatory risk.

[0082] When assessing risks in lanes other than the current lane (i.e., other available lanes), the introduction of third and fourth ratings can break down the abstract concept of risk into quantifiable physical safety and legal compliance indicators. The third rating focuses on characterizing the physical safety risks during lane changes. Its assessment criteria may include, but are not limited to, the relative speed between the target vehicle and vehicles approaching from behind in the target lane, the physical length of the current lane-change window, and the stability of traffic flow in adjacent lanes. For example, if the queue in an adjacent lane is short, but vehicles are approaching from behind with high initial speeds, the third rating will be lowered accordingly to reflect the potential risk of side collisions or rear-end collisions, thereby deterring dangerous overtaking behavior.

[0083] The fourth rating focuses on characterizing the legal and road constraint risks during lane changes. In real-world intersection scenarios, lane-change instructions must strictly adhere to traffic regulations and road markings. For example, the fourth rating can be determined by combining high-precision map information and visual perception results to assess in real time whether the target vehicle is currently within a solid line area prohibiting lane changes, or whether it is too close to the intersection stop line (i.e., outside the lane-change buffer zone). If the lane-change action involves crossing a solid line or may cause the vehicle to miss the intersection due to insufficient distance, the fourth rating can be assigned a punitive score.

[0084] Through the above embodiments, by refining the risks into safety risks (third rating) and regulatory risks (fourth rating), refined control over lane-changing decisions is achieved, effectively avoiding dangerous driving or traffic violations caused by "blindly pursuing efficiency." In complex traffic environments, this solution enables the navigation system to find the optimal balance between efficiency, safety, and compliance among multiple lanes.

[0085] According to some embodiments, the third score is determined based on obtaining a first distance between the target vehicle and the vehicles behind it in the selectable lane, the speed of the target vehicle, and the speed of the vehicles behind it.

[0086] Specifically, when quantifying the third score, not only is the initial distance between the target vehicle and the vehicle behind it in the target lane considered, but their speeds are also coupled in real time. This dynamic assessment based on both displacement and velocity dimensions greatly improves the safety of lane change guidance instructions and effectively avoids the risk of side collisions.

[0087] According to some embodiments, the third rating Determined based on the following formula:

[0088] in, This indicates that the first distance between the determined target vehicle and the vehicles behind it in the selectable lane has been obtained. This indicates the speed of the vehicle behind. This indicates the speed of the target vehicle.

[0089] In the above embodiments, a physics model based on the reciprocal of the Time to Collision (TTC) is introduced. (Third rating) The collision time is inversely proportional to the collision time with vehicles behind in the target lane. When the speed of the following vehicle is significantly higher than that of this vehicle, and the distance between the two vehicles is continuously decreasing, The value will increase rapidly.

[0090] In this way, it is possible to predict whether a lane change will force vehicles behind to brake suddenly, thereby avoiding collisions caused by uneven traffic flow from the source. This ensures that while pursuing traffic efficiency, the navigation system always prioritizes lane change safety.

[0091] According to some embodiments, the fourth score is determined based on a second distance between the target vehicle and the target intersection, and the length of the marked area on the corresponding optional lane for identifying non-changeable lanes.

[0092] Specifically, when quantifying the fourth score, a spatial constraint model is introduced, focusing on the second distance between the target vehicle and the target intersection (i.e., the real-time distance from the stop line), and the physical length of the marked area on the corresponding optional lane (e.g., the solid line area before the intersection) used to indicate non-changeable lanes. For example, the fourth score can be negatively correlated with the second distance (or the second distance itself). As the second distance decreases, the space window for the vehicle to perform lane-changing operations also compresses synchronously; once this distance approaches or enters the length of the solid line area for non-changeable lanes, the regulatory risk can increase exponentially.

[0093] In the above embodiments, by comparing the remaining lane change distance with the boundary of the solid line area, the "lane change cut-off point" can be predicted and locked in advance, thereby ensuring that navigation instructions will not be issued when approaching the solid line or when already entering the solid line area, thus avoiding violations or dangerous last-minute lane changes caused by improper guidance.

[0094] According to some embodiments, the fourth score Determined based on the following formula:

[0095] in, This indicates the length of the marked area on the corresponding optional lane used to indicate non-change lanes. This represents the second distance between the target vehicle and the target intersection. This indicates the preset adjustment coefficient.

[0096] In the above embodiments, the fourth score It can be used to characterize the proximity of a target vehicle to a designated non-changeable lane area (such as a solid line area at an intersection), preventing unauthorized lane changes by crossing solid lines. Preset adjustment coefficient. Used to control the sensitivity of risk values ​​to changes with distance.

[0097] Therefore, in some embodiments, when both a third and a fourth score are included, the second score can be determined based on the following formula. This is used to characterize the risk of a target vehicle changing lanes.

[0098] in, This is a weighting coefficient used to adjust the system's emphasis on physical collision risk versus traffic regulation compliance risk.

[0099] When higher values ​​for the third and fourth scores indicate higher risk, the second score determined based on the third and fourth scores (as described above) is used. The higher the value of the second score, the higher the risk level. If there is also a first score that represents the traffic efficiency of a target vehicle passing through the target intersection based on the corresponding optional lane, and the higher the first score, the higher the traffic efficiency, the first score can be subtracted from the second score to determine the scores corresponding to the multiple optional lanes.

[0100] For example, the number can be determined by the following formula. The scores corresponding to each selectable lane :

[0101] in, Indicates the end time of the green light. Indicates the first The target time corresponding to each selectable lane; therefore, The difference can be used as the first score.

[0102] A negative score indicates that the target vehicle cannot pass through the target intersection using that lane before the green light ends; a positive score indicates a greater safety margin for passing through the target intersection using that lane. Weighting coefficients are used to balance decision preferences between traffic efficiency and lane change risk.

[0103] As mentioned above, when the first When the available lane is the lane where the target vehicle is currently located, the second score is given. The item can be 0.

[0104] According to some embodiments, determining a target lane among multiple selectable lanes based on the score to generate navigation instructions includes: in response to determining that the target vehicle can pass through the target intersection before the green light ends, and determining that the scores corresponding to the other selectable lanes among the multiple selectable lanes (excluding the current lane) are all less than a preset threshold value greater than the score corresponding to the current lane of the target vehicle; determining a suggested speed for guiding the target vehicle to pass through the target intersection before the green light ends; and generating a first navigation instruction based on the suggested speed and the determined target lane, wherein the first navigation instruction is used to guide the target vehicle to continue driving along the target lane at the suggested speed to pass through the target intersection.

[0105] Since the score is used to characterize at least one of the efficiency and risk of a target vehicle passing through a target intersection based on the corresponding optional lane, the determination of whether a target vehicle can pass through the target intersection before the green light ends, based on the score, includes the dual constraints of efficiency and risk.

[0106] For example, the score determined through the above embodiments. Determine that the target vehicle is able to pass through the target intersection before the green light ends. If this score is... A negative value indicates either insufficient green light time margin for the target vehicle to pass through the intersection, or although there is green light time margin in other lanes that could allow the target vehicle to pass through the intersection, changing lanes would be risky, making lane changing impossible, thus preventing the target vehicle from passing through the target intersection before the green light ends. If this score... A positive value indicates that there is sufficient green light time margin in the target lane for the target vehicle to pass through the intersection.

[0107] Once traffic conditions are deemed suitable, a lane-change threshold can be introduced. Even if other lanes are calculated to have slightly higher scores than the current lane, the current lane will still be designated as the target lane as long as this score advantage does not reach a preset difference threshold. It's understandable that lane changing is a risky and energy-intensive operation; if the efficiency improvement from adjacent lanes is not significant, the current lane will tend to be maintained to avoid unnecessary frequent lane changes.

[0108] Once the target lane is selected, a suggested speed can be generated. For example, the suggested speed can be the optimal speed that guides the target vehicle to pass smoothly without stopping, taking into account the remaining distance ahead, the current convoy speed, and the remaining green light window.

[0109] According to some embodiments, the recommended vehicle speed is determined based on the following formula. :

[0110] in, This represents the third distance between the target vehicle and the target intersection. This indicates the end time of the green light. This indicates the preset safety buffer time. This safety buffer time can be a fixed preset value. It is a safety time that is manually deducted to prevent drivers from crossing the line while "on the yellow light" and causing danger. It is usually set to 2 seconds as a safety redundancy.

[0111] Finally, the target lane and recommended speed information are integrated to generate the first navigation command. This command can be issued via an in-vehicle display terminal (such as the instrument panel or HUD) or a voice broadcast system to guide the driver to continue driving along the current lane at the recommended speed.

[0112] Thus, in the above embodiments, by introducing a difference threshold, the interference of invalid lane changes on traffic flow is reduced; at the same time, with precise suggested speed guidance, the target vehicle can pass through the intersection with minimal acceleration and deceleration fluctuations, which not only improves fuel economy or energy utilization, but also greatly alleviates the decision-making pressure and traffic anxiety of drivers at complex signalized intersections.

[0113] According to some embodiments, determining a target lane from among the multiple selectable lanes based on the score to generate navigation instructions includes: in response to determining that the target vehicle can pass through the target intersection before the green light ends, and determining that there are other selectable lanes among the multiple selectable lanes whose scores are higher than the score of the target vehicle's current lane by a preset threshold, determining the other selectable lane whose score is higher than the score of the current lane by a preset threshold as the target lane; generating a second navigation instruction based on the determined target lane, wherein the second navigation instruction is used to guide the target vehicle to change lanes to the target lane and continue to travel along the target lane to pass through the target intersection.

[0114] As described above, after determining that passage conditions are met, a lane-change threshold determination can be further introduced. Even if the calculated score of another lane is higher than the score of the current lane and exceeds a preset threshold, that other lane will be determined as the target lane. Therefore, based on the determined target lane, a second navigation instruction is generated to guide the target vehicle to change lanes and pass through the target intersection along the target lane.

[0115] According to some embodiments, determining a target lane among the plurality of selectable lanes based on the score to generate navigation instructions includes: in response to determining based on the score that the target vehicle cannot pass through the target intersection before the green light ends, generating a third navigation instruction, wherein the third navigation instruction is used to guide the target vehicle to queue up to wait to pass through the target intersection.

[0116] As described above, the score is used to characterize at least one of the traffic efficiency and risk of a target vehicle passing through a target intersection based on the corresponding available lanes. In other words, if the score indicates that a target vehicle cannot pass through the target intersection before the green light ends, it means that, based on traffic efficiency and / or risk, the target vehicle is unlikely to pass through the target intersection before the current green light ends using any lane.

[0117] For example, the score determined through the above embodiments. Determine that the target vehicle is able to pass through the target intersection before the green light ends. If this score is... A negative value indicates that either there is not enough green light time margin for the target vehicle to pass through the intersection, or although there is green light time margin in other lanes that can allow the target vehicle to pass through the intersection, it requires changing lanes, and the risk of changing lanes is too high, meaning that it is impossible to change lanes, resulting in the target vehicle also being unable to pass through the target intersection before the green light ends.

[0118] At this point, the economy driving mode can be activated. Instead of recommending a specific speed, it will prompt "relax the accelerator and coast" and guide the vehicle to enter the lane with the shortest current queue to wait for the next green light to end before passing through the target intersection.

[0119] According to embodiments of this disclosure, such as Figure 5As shown, a vehicle navigation device 500 is also provided, comprising: an acquisition unit 510 configured to acquire the green light end time corresponding to a target intersection through which a target vehicle is to pass; a first determination unit 520 configured to determine multiple optional lanes for the target vehicle to pass through the target intersection, wherein the multiple optional lanes include the lane currently occupied by the target vehicle and adjacent lanes; a second determination unit 530 configured to determine, for each of the multiple optional lanes, a target time required for the target vehicle to pass through the target intersection based on that optional lane; a scoring unit 540 configured to determine a score corresponding to each of the multiple optional lanes based on the green light end time and the target time corresponding to each of the multiple optional lanes, wherein the score is used to characterize at least one of the traffic efficiency and risk of the target vehicle passing through the target intersection based on the corresponding optional lane; and a navigation unit 550 configured to determine a target lane from the multiple optional lanes based on the score to generate a navigation instruction, wherein the navigation instruction is used to guide the target vehicle to pass through the target intersection along the target lane.

[0120] Here, the operation of each of the above-mentioned units 510 to 550 of the vehicle navigation device 500 is similar to the operation of steps 210 to 250 described above, and will not be repeated here.

[0121] The collection, storage, use, processing, transmission, provision, and disclosure of any type of information, such as user personal information, in this technical solution comply with relevant laws and regulations and do not violate public order and good morals.

[0122] According to embodiments of this disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0123] According to another aspect of this disclosure, an edge computing device is also provided. Optionally, in addition to electronic devices, the edge computing device may also include communication components, etc. The electronic devices and communication components can be integrated or separately configured. The electronic devices can acquire data from roadside sensing devices (such as roadside cameras), such as images and videos, thereby performing image and video processing and data calculations, and then transmitting the processing and calculation results to the cloud control platform via the communication components.

[0124] Optionally, the edge computing device can also be a Road Side Computing Unit (RSCU). Alternatively, the electronic device itself can also have the functions of acquiring and communicating sensing data, such as an AI camera. The electronic device can directly perform image and video processing and data calculation based on the acquired sensing data, and then transmit the processing and calculation results to the cloud control platform.

[0125] Optionally, the cloud control platform performs processing in the cloud, including image and video processing and data calculation. The cloud control platform can also be called a vehicle-road cooperative management platform, V2X platform, cloud computing platform, central system, cloud server, etc.

[0126] refer to Figure 6 The present invention describes a structural block diagram of an electronic device 600 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0127] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the electronic device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0128] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, output unit 607, storage unit 608, and communication unit 609. Input unit 606 can be any type of device capable of inputting information to electronic device 600. Input unit 606 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device, and can include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 607 can be any type of device capable of presenting information, and can include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 608 can include, but is not limited to, disk and optical disk. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and can include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0129] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform method 200 by any other suitable means (e.g., by means of firmware).

[0130] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0134] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0135] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0136] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0137] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the invention is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as the technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A vehicle navigation method, comprising: Obtain the green light end time corresponding to the target intersection through which the target vehicle is to pass; Determine multiple selectable lanes for the target vehicle to pass through the target intersection, wherein the multiple selectable lanes include the lane where the target vehicle is currently located and the adjacent lanes; For each of the multiple selectable lanes, determine the target time required for the target vehicle to pass through the target intersection based on that selectable lane; Based on the green light end time and the target time corresponding to each of the multiple optional lanes, a score is determined for each of the multiple optional lanes, wherein the score is used to characterize at least one of the traffic efficiency and risk of the target vehicle passing through the target intersection based on the corresponding optional lane; and Based on the score, a target lane is determined from the multiple selectable lanes to generate navigation instructions, wherein the navigation instructions are used to guide the target vehicle along the target lane through the target intersection.

2. The method of claim 1, further comprising: For each of the multiple selectable lanes, in response to determining that there are queued vehicles ahead of that selectable lane, the queue information and lane travel speed corresponding to that selectable lane are obtained. Determining the target time required for the target vehicle to pass through the target intersection based on the optional lane includes: determining the target time required for the target vehicle to pass through the target intersection based on the queuing information and the lane travel speed, wherein the target time is used to characterize the time required for both the queuing vehicles and the target vehicle to pass through the target intersection based on the optional lane.

3. The method as described in claim 2, wherein, The queuing information includes the queue length and the target vehicle's remaining distance from the end of the queue, wherein determining the target time required for the target vehicle to pass through the target intersection based on the available lane includes: Based on the convoy length, the remaining target distance, and the lane speed, the target time required for the target vehicle to pass through the target intersection using the available lane is determined.

4. The method of claim 2, wherein, The queuing information includes the target remaining distance between the target vehicle and the end of the queue, and the number of vehicles in the queue. Determining the target time required for the target vehicle to pass through the target intersection based on the available lane includes: Based on the remaining distance to the target and the lane travel speed, a first estimated time is determined; Based on the number of vehicles and the preset saturation headway, a second estimated time is determined; and Based on the first estimated time and the second estimated time, the target time required for the target vehicle to pass through the target intersection using the optional lane is determined.

5. The method of claim 4, wherein, The queuing information also includes: key vehicle information in the queue, wherein determining the second estimated time based on the number of vehicles and the preset saturation headway includes: In response to determining that the queue for the selectable lane includes a critical vehicle, a correction factor corresponding to the critical vehicle is determined, wherein the correction factor is used to correct the start time of the critical vehicle in the queue; and The second estimated time is determined based on the correction factor, the number of vehicles, and the preset saturation headway.

6. The method of claim 4 or 5, wherein, The second estimated time is determined based on the number of vehicles and the preset saturation headway, including: Obtain a preset start-up loss time, wherein the start-up loss time characterizes the delay time required for the convoy to start; and The second estimated time is determined based on the start-up loss time, the number of vehicles, and the preset saturation headway.

7. The method of claim 1, wherein, Based on the green light end time and the target time corresponding to each of the multiple optional lanes, the scores corresponding to each of the multiple optional lanes are determined as follows: Based on the difference between the green light end time and the corresponding target time, a first score is determined for each of the multiple selectable lanes, wherein the first score characterizes the traffic efficiency of the target vehicle passing through the target intersection based on the corresponding selectable lane; and Based on the first score, the scores corresponding to the multiple selectable lanes are determined respectively.

8. The method of claim 1 or 7, wherein, Based on the green light end time and the target time corresponding to each of the multiple optional lanes, the scores corresponding to each of the multiple optional lanes are determined as follows: Determine a second score corresponding to each of the multiple selectable lanes, wherein the second score is determined based on whether the corresponding selectable lane is the lane the target vehicle is currently in, or a lane other than the current lane, and wherein the second score is used to characterize the risk of the target vehicle passing through the target intersection based on the corresponding selectable lane; and Based on the second score, the scores corresponding to the multiple selectable lanes are determined respectively.

9. The method of claim 8, wherein, For lanes other than the current lane, the second score is determined based on at least one of a third score characterizing safety risk and a fourth score characterizing regulatory risk.

10. The method of claim 9, wherein, The third score is determined based on the first distance between the target vehicle and the vehicles behind it in the selectable lane, the speed of the target vehicle, and the speed of the vehicles behind it.

11. The method of claim 10, wherein, The third rating Determined based on the following formula: in, This indicates that the first distance between the determined target vehicle and the vehicles behind it in the selectable lane has been obtained. This indicates the speed of the vehicle behind. This indicates the speed of the target vehicle.

12. The method of claim 9, wherein, The fourth score is determined based on the second distance between the target vehicle and the target intersection, and the length of the marked area on the corresponding optional lane for identifying non-changeable lanes.

13. The method of claim 12, wherein, The fourth rating Determined based on the following formula: in, This indicates the length of the marked area on the corresponding optional lane used to indicate non-change lanes. This represents the second distance between the target vehicle and the target intersection. This indicates the preset adjustment coefficient.

14. The method of claim 1, wherein, Based on the rating, a target lane is determined from the multiple selectable lanes to generate navigation instructions, including: In response to determining, based on the score, that the target vehicle can pass through the target intersection before the green light ends, and determining that the scores corresponding to the other selectable lanes among the multiple selectable lanes, excluding the current lane, are all less than a preset threshold corresponding to the score of the target vehicle's current lane, the current lane is determined to be the target lane. Determine a recommended speed for guiding the target vehicle through the target intersection before the green light ends; and A first navigation instruction is generated based on the suggested vehicle speed and the determined target lane, wherein the first navigation instruction is used to guide the target vehicle to continue traveling along the target lane at the suggested vehicle speed to pass the target intersection.

15. The method of claim 14, wherein, The recommended vehicle speed is determined based on the following formula. : in, This represents the third distance between the target vehicle and the target intersection. This indicates the end time of the green light. This indicates the preset safety buffer time.

16. The method of claim 1 or 14, wherein, Based on the rating, a target lane is determined from the multiple selectable lanes to generate navigation instructions, including: In response to determining, based on the score, that the target vehicle can pass through the target intersection before the green light ends, and determining that among the multiple selectable lanes there exists another selectable lane (excluding the current lane) with a score greater than the score of the target vehicle's current lane by a preset threshold, the other selectable lane with a score greater than the score of the current lane by a preset threshold is determined as the target lane; A second navigation instruction is generated based on the determined target lane, wherein the second navigation instruction is used to guide the target vehicle to change lanes to the target lane and continue to travel along the target lane to pass through the target intersection.

17. The method of claim 1 or 14, wherein, Based on the rating, a target lane is determined from the multiple selectable lanes to generate navigation instructions, including: In response to determining, based on the score, that the target vehicle cannot pass through the target intersection before the green light ends, a third navigation instruction is generated, wherein the third navigation instruction is used to guide the target vehicle to queue up and wait to pass through the target intersection.

18. A vehicle navigation device, comprising: The acquisition unit is configured to acquire the green light end time corresponding to the target intersection through which the target vehicle is to pass. The first determining unit is configured to determine multiple selectable lanes for the target vehicle to pass through the target intersection, wherein the multiple selectable lanes include the lane where the target vehicle is currently located and the adjacent lanes; The second determining unit is configured to, for each of the plurality of selectable lanes, determine the target time required for the target vehicle to pass through the target intersection based on that selectable lane; The scoring unit is configured to determine a score for each of the multiple optional lanes based on the green light end time and the target time corresponding to each of the multiple optional lanes. The score characterizes at least one of the traffic efficiency and risk of the target vehicle passing through the target intersection via the corresponding optional lane. A navigation unit is configured to determine a target lane from among a plurality of selectable lanes based on the rating, in order to generate navigation instructions, wherein the navigation instructions are used to guide the target vehicle through the target intersection along the target lane.

19. An electronic device comprising: At least one processor; as well as A memory that is communicatively connected to the at least one processor; in The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-17.

20. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-17.

21. A computer program product comprising a computer program, wherein, When the computer program is executed by a processor, it implements the method of any one of claims 1-17.

22. A vehicle comprising: The electronic device as claimed in claim 19.

23. An edge computing device, comprising the electronic device of claim 19.

Images