Trajectory planning of vehicles using route information

Abstract

Among other things, techniques for improving trajectory estimation for an object in an environment are described. The techniques include: receiving, with at least one processor, information indicating the presence of an object operating in the environment; determining, with the at least one processor, a trajectory of the object, the trajectory including at least a position, a velocity, and a direction of travel of the object; determining, with the at least one processor, an expected route of the object, wherein the expected route is pre-planned and includes an expected future position of the object at a future time; comparing the trajectory of the object to the expected route of the object; and updating the trajectory of the object based on the expected route of the object in accordance with the comparison that the trajectory of the object is consistent with the expected route of the object.

Description

Technical Field

[0001] The present description relates to trajectory planning of a vehicle using route information. Background Art

[0002] The path taken by the autonomous vehicle depends on the path of objects (e.g., other nearby vehicles, bicycles, pedestrians). The trajectory estimates of the objects (e.g., the next 5-10 seconds and / or 50-100 meters) are based on the estimated position and velocity of the objects determined by sensors configured on the autonomous vehicle. However, these trajectory estimates do not take into account the expected behavior of the objects. Therefore, over-reliance on these trajectory estimates during the travel of the autonomous vehicle may force the autonomous vehicle to have to recalculate its path to avoid collisions and / or less than optimal driving conditions (e.g., getting stuck behind a vehicle). Summary of the invention

[0003] A method includes: receiving, using at least one processor, information indicating the presence of an object operating in an environment; determining, using the at least one processor, a trajectory of the object, the trajectory including at least a position, a speed, and a direction of travel of the object; determining, using the at least one processor, an expected route of the object, wherein the expected route is pre-planned and includes an expected future position of the object at a future time; comparing the trajectory of the object with the expected route of the object; and updating the trajectory of the object based on the expected route of the object according to a comparison result that the trajectory of the object is consistent with the expected route of the object.

[0004] A non-transitory computer-readable storage medium includes at least one program for execution by at least one processor of a first device, the at least one program including instructions, which, when executed by the at least one processor, cause the first device to perform the above method.

[0005] A vehicle comprises: at least one sensor configured to capture information of an object; at least one transceiver configured to send and receive route information of the object; and at least one processor communicatively coupled to the at least one sensor and the at least one transceiver and configured to execute computer executable instructions, which executes the above method. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 An example of an autonomous vehicle with autonomous capabilities is shown.

[0007] Figure 2 An example "cloud" computing environment is illustrated.

[0008] Figure 3 An example computer system is shown.

[0009] Figure 4 An example architecture of an autonomous vehicle is shown.

[0010] Figure 5 Block diagram showing the relationship between the inputs and outputs of the planning module.

[0011] Figure 6 An autonomous vehicle is shown approaching an intersection.

[0012] Fig. 7A and 7B is the decision tree for autonomous vehicles.

[0013] Figure 8 is a flow chart for improving trajectory estimation of objects in an environment. DETAILED DESCRIPTION

[0014] In the following description, for the purpose of explanation, many specific details are set forth in order to provide a thorough understanding of the present invention. However, it is apparent that the present invention can be implemented without these specific details. In other examples, well-known configurations and devices are shown in block diagram form to avoid unnecessarily obscuring the present invention.

[0015] In the accompanying drawings, for ease of description, the specific arrangement or order of the schematic elements is shown, such as those elements representing devices, modules, instruction blocks, and data elements. However, it should be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the accompanying drawings does not mean that a specific processing order or sequence or separation of the processing process is required. In addition, the inclusion of schematic elements in the accompanying drawings does not mean that such elements are required in all embodiments, nor does it mean that the features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0016] In addition, in the accompanying drawings, connecting elements, such as solid or dotted lines or arrows, are used to illustrate the connection, relationship or association between two or more other schematic elements, and the absence of any such connecting elements does not mean that there can be no connection, relationship or association. In other words, the connection, relationship or association between some elements is not shown in the accompanying drawings so as not to cover up the present invention. In addition, for ease of explanation, a single connecting element is used to represent multiple connections, relationships or associations between elements. For example, if a connecting element represents the communication of a signal, data or instruction, it will be understood by those skilled in the art that the element represents one or more signal paths (e.g., a bus) that may be needed to affect the communication.

[0017] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the detailed description that follows, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments described. However, it will be apparent to one of ordinary skill in the art that the various embodiments described may be implemented without these specific details. In other cases, well-known methods, procedures, components, circuits, and networks are not described in detail in order not to unnecessarily obscure aspects of the embodiments.

[0018] Several features described below may be used independently of each other or in combination with other features. However, any individual feature may not solve any of the above problems, or may only solve one of the above problems. Some of the problems discussed above may not be fully solved by any one of the features described herein. Although headings are provided, information related to a heading but not found in that heading may be found elsewhere in this description. Embodiments are described herein according to the following summary:

[0019] 1. General Overview

[0020] 2. System Overview

[0021] 3. Autonomous Vehicle Architecture

[0022] 4. Path Planning

[0023] 5. Trajectory estimation of objects in the environment

[0024] General Overview

[0025] Knowing the expected route that an object is likely to follow helps the autonomous vehicle determine its own path. For example, the autonomous vehicle can be notified that a public transportation bus (or tram) of a specific model on a fixed route is to the left of the autonomous vehicle. The autonomous vehicle can then query a database to retrieve the expected route for that specific public transportation bus. If the public transportation bus appears to be moving along the expected route and the expected route indicates a left turn at an upcoming intersection, the autonomous vehicle can conclude with an appropriate degree of certainty that the public transportation bus is about to turn left and that the probability of the public transportation bus entering the current lane of the autonomous vehicle is very low. The autonomous vehicle can then use this information to determine that it is safe to remain in the current lane.

[0026] As yet another example, if the vehicle in front of the autonomous vehicle is identified as a UPS truck that is about to stop at a destination, the autonomous vehicle is notified of this situation so that the autonomous vehicle can switch lanes to avoid the UPS truck. As another example, if the vehicle in front of the autonomous vehicle is identified as a student driver vehicle, the autonomous vehicle can maintain a greater distance from the student driver vehicle in the event of an emergency stop or sudden move.

[0027] Some of the advantages of these techniques include reducing the need to recalculate the path of the autonomous vehicle. In addition, by incorporating the route information of the object, the autonomous vehicle can remove unknown information from the path planning process. This directly results in a safer ride and a smoother ride for the occupants in the autonomous vehicle. Objects in the environment will also be safer because the autonomous vehicle will try to avoid interfering with the expected route of the object.

[0028] System Overview

[0029] Figure 1 An example of an autonomous vehicle 100 having autonomous capabilities is shown.

[0030] As used herein, the term "autonomous capability" refers to a function, feature, or facility that enables a vehicle to operate partially or fully without real-time human intervention, including but not limited to fully autonomous vehicles, highly autonomous vehicles, partially autonomous vehicles, and conditionally autonomous vehicles.

[0031] As used herein, an autonomous vehicle (AV) is a vehicle with autonomous capabilities.

[0032] As used herein, "vehicle" includes a mode of transport of goods or people. For example, a car, bus, train, airplane, drone, truck, boat, ship, submersible, spacecraft, etc. An unmanned car is an example of a vehicle.

[0033] As used herein, a "trajectory" refers to a path or route that navigates an AV from a first spatiotemporal location to a second spatiotemporal location. In an embodiment, the first spatiotemporal location is referred to as an initial location or starting location, and the second spatiotemporal location is referred to as a destination, final location, target, target location, or target location. In some examples, a trajectory consists of one or more road segments (e.g., several sections of a road), and each road segment consists of one or more blocks (e.g., a lane or a portion of an intersection). In an embodiment, a spatiotemporal location corresponds to a real-world location. For example, a spatiotemporal location is a pick-up or drop-off location to get people or cargo on or off the vehicle.

[0034] As used herein, "sensor(s)" includes one or more hardware components for detecting information related to the environment surrounding the sensor. Some hardware components may include sensing components (e.g., image sensors, biometric sensors), transmitting and / or receiving components (e.g., laser or radio frequency wave transmitters and receivers), electronic components (e.g., analog-to-digital converters), data storage devices (e.g., RAM and / or non-volatile memory), software or firmware components and data processing components (e.g., application specific integrated circuits), microprocessors and / or microcontrollers.

[0035] As used herein, a “scene description” is a data structure (e.g., a list) or data stream that includes one or more classified or labeled objects detected by one or more sensors on an AV vehicle, or one or more classified or labeled objects provided by a source external to the AV.

[0036] As used herein, a "road" is a physical area that can be traversed by a vehicle and may correspond to a named thoroughfare (e.g., a city street, an interstate highway, etc.) or may correspond to an unnamed thoroughfare (e.g., a driveway within a house or office building, a section of a parking lot, a section of a vacant parking lot, a dirt road in a rural area, etc.). Because some vehicles (e.g., four-wheel drive pickup trucks, off-road vehicles (SUVs), etc.) are able to traverse a variety of physical areas that are not particularly suitable for vehicle travel, a "road" may be any physical area that is not formally defined as a thoroughfare by a municipality or other governmental or administrative agency.

[0037] As used herein, a "lane" is a portion of a road that can be traversed by a vehicle. Lanes are sometimes identified based on lane markings. For example, a lane may correspond to most or all of the space between lane markings, or only a portion of the space between lane markings (e.g., less than 50%). For example, a road with lane markings that are far apart may accommodate two or more vehicles, so that one vehicle can pass another vehicle without crossing the lane markings, and thus may be interpreted as a lane that is narrower than the space between lane markings, or as having two lanes between lanes. Lanes may also be interpreted in the absence of lane markings. For example, lanes may be defined based on physical features of the environment (e.g., rocks in rural areas and trees along avenues, or natural obstacles that should be avoided, for example, in underdeveloped areas). Lanes may also be interpreted independently of lane markings or physical features. For example, a lane may be interpreted based on an arbitrary path without obstacles in an area that would otherwise lack features that would be interpreted as lane boundaries. In an example scenario, an AV may interpret a lane as an obstacle-free portion through a field or open space. In another example scenario, an AV may interpret a lane through a wide (e.g., wide enough for two or more lanes) road that does not have lane markings. In this scenario, the AV may communicate information about the lane to other AVs so that the other AVs may use the same lane information to coordinate path planning between AVs.

[0038] The term “Over-the-Air (OTA) Client” includes any AV, or any electronic device (e.g., computer, controller, IoT device, electronic control unit (ECU)) embedded in, coupled to, or communicating with an AV.

[0039] The term "Over-the-Air (OTA) Update" means any update, change, deletion or addition to software, firmware, data or configuration settings, or any combination thereof, delivered to an OTA client using proprietary and / or standardized wireless communication technologies including, but not limited to: cellular mobile communications (e.g., 2G, 3G, 4G, 5G), radio wireless local area networks (e.g., WiFi) and / or satellite Internet.

[0040] The term “edge node” refers to one or more edge devices coupled to a network that provide a portal for communicating with AVs and can communicate with other edge nodes and cloud-based computing platforms to schedule and deliver OTA updates to OTA clients.

[0041] The term "edge device" refers to a device that implements an edge node and provides a physical wireless access point (AP) to the core network of an enterprise or service provider (such as VERIZON, AT&T). Examples of edge devices include, but are not limited to: computers, controllers, transmitters, routers, routing switches, integrated access devices (IADs), multiplexers, metropolitan area networks (MANs), and wide area networks (WAN) access devices.

[0042] “One or more” includes a function performed by one element, a function performed by multiple elements, such as in a distributed manner, several functions performed by one element, several functions performed by several elements, or any combination of the above.

[0043] It will also be understood that although in some cases the terms "first," "second," etc. are used to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact without departing from the scope of the various described embodiments. The first contact and the second contact are both contacts, but they are not the same contact.

[0044] The terms used in the description of the various embodiments described herein are used only to describe specific embodiments and are not intended to be limiting. As used in the description of the various embodiments described and the appended claims, the singular forms "a", "an", and "the" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any and all possible combinations of one or more related list items. It should also be understood that the terms "including", "comprising", "having", and / or "having" used in this description specifically indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or the above groups.

[0045] As used herein, the term "if" may alternatively be understood as in that case, at that time, or in response to being detected, or in response to being determined, as the context requires. Similarly, the phrase "if it is determined" or "if [the condition or event] has been detected" may be understood as "when it is determined" or "in response to being determined" or "when [the condition or event] is detected" or "in response to being detected [the condition or event]", as the context requires.

[0046] As used herein, an AV system refers to an AV and the hardware, software, stored data, and data generated in real time to support the operation of the AV. In an embodiment, the AV system is incorporated into the AV. In an embodiment, the AV system is distributed across multiple locations. For example, some software of the AV system is combined in a similar manner to the following: Figure 3 The described cloud computing environment 300 is implemented in a cloud computing environment.

[0047] In general, this document describes technologies applicable to any vehicle having one or more autonomous capabilities, including fully autonomous vehicles, highly autonomous vehicles, and conditionally autonomous vehicles, such as so-called Level 5, Level 4, and Level 3 vehicles (see SAE International Standard J3016: Classification and Definitions of Terms Relating to Automated Driving Systems for Road-Motor Vehicles, which is incorporated herein by reference in its entirety for more details on vehicle autonomy levels). The technologies described in this description are also applicable to partially autonomous vehicles and driver-assisted vehicles, such as so-called Level 2 and Level 1 vehicles (see SAE International Standard J3016: Classification and Definitions of Terms Relating to Automated Driving Systems for Road-Motor Vehicles). In embodiments, one or more Level 1, Level 2, Level 3, Level 4, and Level 5 vehicle systems may automatically perform certain vehicle operations (e.g., steering, braking, and use of maps) under certain operating conditions based on processing of sensor inputs. The technologies described in this document can benefit vehicles at all levels, from fully autonomous vehicles to human-operated vehicles.

[0048] Autonomous vehicles have advantages over vehicles that require human drivers. One advantage is safety. For example, in 2016, the United States experienced 6 million automobile accidents, 2.4 million injuries, 40,000 deaths, and 13 million vehicle crashes, with an estimated social cost of more than $910 billion. From 1965 to 2015, the number of U.S. traffic fatalities has decreased from about 6 to about 1 per 100 million miles traveled, in part due to additional safety measures deployed in vehicles. For example, it is believed that an extra half-second of warning related to an impending collision mitigates 60% of front-to-rear collisions. However, passive safety features (e.g., seat belts, airbags) may have reached their limits in improving this number. Therefore, active safety measures such as automatic control of vehicles are a possible next step to improve these statistics. Because human drivers are believed to be the cause of severe pre-crash events in 95% of crashes, automated driving systems have the potential to achieve better safety outcomes by, for example: reliably identifying and avoiding emergency situations better than humans; making better decisions than humans, obeying traffic laws better than humans, and predicting future events better than humans; and reliably controlling a vehicle better than humans.

[0049] refer to Figure 1 , the AV system 120 causes the AV 100 to operate along a trajectory 198, through an environment 190 to a destination 199 (sometimes referred to as a final location), while avoiding objects (e.g., natural obstacles 191, vehicles 193, pedestrians 192, cyclists, and other obstacles) and obeying road rules (e.g., operating rules or driving preferences).

[0050] In an embodiment, the AV system 120 includes a device 101 for receiving and operating commands from a computer processor 146. The term "operating command" is used to refer to an executable instruction (or set of instructions) that causes a vehicle to perform an action (e.g., a driving maneuver). The operating command may include, but is not limited to, instructions for causing the vehicle to start moving forward, stop moving forward, start moving backward, stop moving backward, speed up, slow down, make a left turn, and make a right turn. In an embodiment, the computing processor 146 and the following reference Figure 3 The processor 304 is similarly described. Examples of devices 101 include steering controls 102, brakes 103, gears, an accelerator pedal or other acceleration control mechanism, windshield wipers, side door locks, window controls, and turn indicators.

[0051] In an embodiment, the AV system 120 includes sensors 121 for measuring or inferring attributes of the state or condition of the AV 100, such as the position, linear and angular velocity and acceleration, and heading (e.g., the direction of the front end of the AV 100) of the AV. Examples of sensors 121 are GPS, and an inertial measurement unit (IMU) that measures vehicle linear acceleration and angular rate, wheel rate sensors for measuring or estimating wheel slip, wheel brake pressure or brake torque sensors, engine torque or wheel torque sensors, and steering angle and angular rate sensors.

[0052] In an embodiment, the sensors 121 also include sensors for sensing or measuring properties of the environment of the AV, such as monocular or stereo cameras 122 in the visible, infrared, or thermal (or both) spectrum, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, velocity sensors, temperature sensors, humidity sensors, and precipitation sensors.

[0053] In an embodiment, the AV system 120 includes a data storage unit 142 and a memory 144 for storing machine instructions related to the computer processor 146 or data collected by the sensor 121. In an embodiment, the data storage unit 142 is combined with the following Figure 3190 . In an embodiment, the memory 144 is similar to the main memory 306 described below. In an embodiment, the data storage unit 142 and the memory 144 store historical, real-time, and / or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates, or weather conditions. In an embodiment, data related to the environment 190 is transmitted to the AV 100 via a communication channel from the remote database 134.

[0054] In an embodiment, the AV system 120 includes a communication device 140 for transmitting measured or inferred attributes of the state and condition of other vehicles (such as position, linear and angular velocity, linear and angular acceleration, and linear and angular heading, etc.) to the AV 100. These devices include vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication devices and devices for wireless communication via point-to-point or ad hoc networks or both. In an embodiment, the communication device 140 communicates across the electromagnetic spectrum (including radio and optical communications) or other media (e.g., air and acoustic media). The combination of vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) communications (and in some embodiments, one or more other types of communications) is sometimes referred to as vehicle-to-everything (V2X) communications. V2X communications generally comply with one or more communication standards for communication with and between autonomous vehicles.

[0055] In an embodiment, the communication device 140 includes a communication interface. For example, a wired, wireless, WiMAX, Wi-Fi, Bluetooth, satellite, cellular, optical, near field, infrared, or radio interface. The communication interface transmits data from the remote database 134 to the AV system 120. In an embodiment, the remote database 134 is embedded in the cloud computing environment 200, such as Figure 2 The communication interface 140 transmits data collected from the sensor 121 or other data related to the operation of the AV 100 to the remote database 134. In an embodiment, the communication interface 140 transmits information related to the remote operation to the AV 100. In some embodiments, the AV 100 communicates with other remote (e.g., "cloud") servers 136.

[0056] In an embodiment, the remote database 134 also stores and transmits digital data (e.g., data storing roads and street locations, etc.) Such data is stored in the memory 144 on the AV 100 or transmitted from the remote database 134 to the AV 100 via a communication channel.

[0057] In an embodiment, the remote database 134 stores and transmits historical information (e.g., speed and acceleration rate profiles) related to driving attributes of vehicles that have previously traveled along the trajectory 198 at similar times of day. In one implementation, such data may be stored in the memory 144 on the AV 100 or transmitted from the remote database 134 to the AV 100 via a communication channel.

[0058] The computing device 146 located on the AV 100 algorithmically generates control actions based on real-time sensor data and a priori information, enabling the AV system 120 to perform its autonomous driving capabilities.

[0059] In an embodiment, the AV system 120 includes a computer peripheral device 132 connected to the computing device 146 for providing information and reminders to a user of the AV 100 (e.g., an occupant or a remote user) and receiving input from the user. In an embodiment, the peripheral device 132 is similar to the one described below with reference to Figure 3 Display 312, input device 314 and cursor control 316 are discussed. The connection is wireless or wired. Any two or more interface devices may be integrated into a single device.

[0060] In an embodiment, the AV system 120 receives and enforces the privacy level of the occupant, for example, specified by the occupant or stored in a profile associated with the occupant. The privacy level of the occupant determines how to permit the use of specific information associated with the occupant (e.g., occupant comfort data, biometric data, etc.) stored in the occupant profile and / or stored on the cloud server 136 and associated with the occupant profile. In an embodiment, the privacy level specifies specific information associated with the occupant that is deleted once the ride is completed. In an embodiment, the privacy level specifies specific information associated with the occupant and identifies one or more entities authorized to access the information. Examples of designated entities authorized to access information may include other AVs, third-party AV systems, or any entity that can potentially access the information.

[0061] The privacy level of an occupant may be specified at one or more levels of granularity. In an embodiment, the privacy level identifies specific information to be stored or shared. In an embodiment, the privacy level applies to all information associated with the occupant, such that the occupant may specify that her personal information is not to be stored or shared. The designation of entities permitted to access specific information may also be specified at various levels of granularity. The various sets of entities permitted to access specific information may include, for example, other AVs, cloud servers 136, specific third-party AV systems, etc.

[0062] In an embodiment, the AV system 120 or the cloud server 136 determines whether the AV 100 or another entity may access certain information associated with the occupant. For example, a third-party AV system attempting to access occupant input related to a particular spatiotemporal location must obtain authorization, such as from the AV system 120 or the cloud server 136, to access information associated with the occupant. For example, the AV system 120 uses the occupant's specified privacy level to determine whether the occupant input related to the spatiotemporal location can be presented to a third-party AV system, the AV 100, or another AV. This enables the occupant's privacy level to specify which other entities are allowed to receive data related to the occupant's actions or other data associated with the occupant.

[0063] Figure 2 Illustrate an example "cloud" computing environment. Cloud computing is a service delivery model that provides convenient, on-demand access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) over a network. In a typical cloud computing system, one or more large cloud data centers house the machines used to deliver the services provided by the cloud. Now refer to Figure 2 , cloud computing environment 200 includes cloud data centers 204a, 204b, and 204c interconnected by cloud 202. Data centers 204a, 204b, and 204c provide cloud computing services for computer systems 206a, 206b, 206c, 206d, 206e, and 206f connected to cloud 202.

[0064] The cloud computing environment 200 includes one or more cloud data centers. Typically, a cloud data center (e.g. Figure 2 The cloud data center 204a shown in FIG. 2 refers to a cloud (eg, Figure 2 The physical arrangement of servers in a cloud 202 or a specific portion of a cloud (as shown in FIG. 202 or a specific portion of a cloud). For example, servers are physically arranged into rooms, groups, rows, and racks in a cloud data center. A cloud data center has one or more regions, which include one or more server rooms. Each room has one or more rows of servers, and each row includes one or more racks. Each rack includes one or more individual server nodes. In some implementations, servers in regions, rooms, racks, and / or rows are divided into groups based on the physical infrastructure requirements of the data center facility, including power, energy, heat, heat sources, and / or other requirements. In an embodiment, server nodes are similar to Figure 3 The data center 204a has many computing systems distributed across multiple racks.

[0065] Cloud 202 includes cloud data centers 204a, 204b, and 204c and networks and network resources (e.g., network devices, nodes, routers, switches, and network cables) used to connect cloud data centers 204a, 204b, and 204c and facilitate access to cloud computing services by computing systems 206a-f. In an embodiment, the network represents any combination of one or more local networks, wide area networks, or internetworks connected by wired or wireless links deployed using ground or satellite connections. Data exchanged through the network is transmitted using a variety of network layer protocols (e.g., Internet Protocol (IP), Multi-Protocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM), Frame Relay, etc.). In addition, in embodiments where the network represents a combination of multiple subnetworks, different network layer protocols are used on each underlying subnetwork. In some embodiments, the network represents one or more interconnected internetworks (e.g., the public Internet, etc.).

[0066] Computing systems 206a-f or cloud computing service consumers are connected to the cloud 202 through network links and network adapters. In an embodiment, computing systems 206a-f are implemented as various computing devices, such as servers, desktops, laptops, tablets, smart phones, Internet of Things (IoT) devices, autonomous vehicles (including cars, drones, shuttles, trains, buses, etc.) and consumer electronics. In an embodiment, computing systems 206a-f are implemented in other systems or as part of other systems.

[0067] Figure 3 Computer system 300 is illustrated. In implementation, computer system 300 is a special purpose computing device. The special purpose computing device is hardwired to perform these techniques, or includes a digital electronic device such as one or more application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are permanently programmed to perform the above-mentioned techniques, or may include one or more general purpose hardware processors that are programmed to perform these techniques according to program instructions in firmware, memory, other memory, or a combination. Such a special purpose computing device can also combine customized hardwired logic, ASICs or FPGAs with customized programming to complete these techniques. In various embodiments, the special purpose computing device is a desktop computer system, a portable computer system, a handheld device, a network device, or any other device that contains hardwired and / or program logic to implement these techniques.

[0068] In an embodiment, computer system 300 includes a bus 302 or other communication mechanism for communicating information, and a hardware processor 304 connected to bus 302 to process information. Hardware processor 304 is, for example, a general-purpose microprocessor. Computer system 300 also includes a main memory 306, such as a random access memory (RAM) or other dynamic storage device, connected to bus 302 to store information and instructions, which are executed by processor 304. In one implementation, main memory 306 is used to store temporary variables or other intermediate information during the execution of instructions to be executed by processor 304. When these instructions are stored in a non-transitory storage medium accessible to processor 304, computer system 300 becomes a special-purpose machine that is customized to perform the operations specified in the instructions.

[0069] In an embodiment, computer system 300 also includes a read only memory (ROM) 308 or other static storage device connected to bus 302 for storing static information and instructions for processor 304. A storage device 310, such as a magnetic disk, optical disk, solid state drive, or three-dimensional cross point memory, is provided and connected to bus 302 to store information and instructions.

[0070] In an embodiment, the computer system 300 is connected to a display 312 such as a cathode ray tube (CRT), a liquid crystal display (LCD), a plasma display, a light emitting diode (LED) display, or an organic light emitting diode (OLED) display for displaying information to a computer user via bus 302. An input device 314 including alphanumeric and other keys is connected to bus 302 for communicating information and command selections to processor 304. Another type of user input device is a cursor controller 316, such as a mouse, trackball, touch display, or cursor direction keys, for communicating direction information and command selections to processor 304 and for controlling movement of a cursor on display 312. Such input devices typically have two degrees of freedom in two axes, a first axis (e.g., an x-axis) and a second axis (e.g., a y-axis), which allow the device to specify a position on a plane.

[0071] According to one embodiment, the techniques herein are performed by computer system 300 in response to processor 304 executing one or more sequences of one or more instructions contained in main memory 306. The instructions are read into main memory 306 from another storage medium, such as storage device 310. Execution of the sequences of instructions contained in main memory 306 causes processor 304 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry is used in place of or in combination with software instructions.

[0072] The term "storage medium" as used herein refers to any non-temporary medium that stores data and / or instructions that cause a machine to operate in a particular manner. Such storage media include non-volatile media and / or volatile media. Non-volatile media include, for example, optical disks, magnetic disks, solid-state drives, or three-dimensional cross-point memories such as storage device 310. Volatile media include dynamic memories such as main memory 306. Common forms of storage media include, for example, floppy disks, floppy disks, hard disks, solid-state drives, tapes or any other magnetic data storage media, CD-ROMs, any other optical data storage media, any physical media with a hole pattern, RAM, PROM and EPROM, FLASH-EPROM, NV-RAM, or any other memory chip or storage box.

[0073] Storage media are distinct from transmission media, but can be used in conjunction with transmission media. Transmission media participate in the transmission of information between storage media. For example, transmission media include coaxial cables, copper wires, and optical fibers, including wires that provide bus 302. Transmission media can also take the form of sound waves or light waves, such as those generated during radio wave and infrared data communications.

[0074] In an embodiment, various forms of media are involved in carrying one or more sequences of instructions to the processor 304 for execution. For example, the instructions are initially executed on a disk or solid-state drive of a remote computer. The remote computer loads the instructions into its dynamic memory and sends the instructions over a telephone line using a modem. The local modem of the computer system 300 receives the data on the telephone line and uses an infrared transmitter to convert the data to an infrared signal. An infrared detector receives the data carried in the infrared signal, and appropriate circuitry places the data on the bus 302. The bus 302 carries the data to the main memory 306, from which the processor 304 retrieves and executes the instructions. The instructions received by the main memory 306 may optionally be stored on the storage device 310 before or after execution by the processor 304.

[0075] Computer system 300 also includes a communication interface 318 connected to bus 302. Communication interface 318 provides multiple bidirectional data communications coupled to network link 320 connected to local network 322. For example, communication interface 318 is an integrated services digital network (ISDN) card, a cable modem, a satellite modem, or a modem for providing a data communication connection with a corresponding type of telephone line. As another example, communication interface 318 is a local area network (LAN) card for providing a data communication connection with a compatible LAN. In some implementations, a wireless link is also implemented. In any such implementation, communication interface 318 sends and receives electrical, electromagnetic or optical signals carrying digital data streams representing various information.

[0076] The network link 320 typically provides data communication to other data devices through one or more networks. For example, the network link 320 provides a connection to a host computer 324 or to a cloud data center or device operated by an Internet Service Provider (ISP) 326 through a local network 322. The ISP 326, in turn, provides data communication services through a worldwide packet data communication network now commonly referred to as the "Internet." Both the local network 322 and the Internet 328 use electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 320 and through the communication interface 318 are example forms of transmission media, where the communication interface 318 carries the digital data into and out of the computer system 300. In an embodiment, the network 320 includes the cloud 202 or a portion of the cloud 202 described above.

[0077] Computer system 300 sends messages and receives data, including program code, through network(s), network link 320, and communication interface 318. In an embodiment, computer system 300 receives code for processing. The received code is executed by processor 304 as it is received, and / or stored in storage device 310, or other non-volatile storage for later execution.

[0078] Autonomous Vehicle Architecture

[0079] Figure 4 An example of a method for autonomous vehicles (e.g., Figure 1 100). The architecture 400 includes a sensing module 402 (sometimes referred to as sensing circuitry), a planning module 404 (sometimes referred to as planning circuitry), a control module 406 (sometimes referred to as control circuitry), a positioning module 408 (sometimes referred to as positioning circuitry), and a database module 410 (sometimes referred to as database circuitry). Each module plays a role in the operation of the AV 100. Collectively, the modules 402, 404, 406, 408, and 410 may be Figure 1 404, 406, 408, and 410 are each sometimes referred to as a processing circuit (e.g., computer hardware, computer software, or a combination of the two). Any or all of the combinations of modules 402, 404, 406, 408, and 410 are also examples of processing circuits.

[0080] In use, the planning module 404 receives data representing a destination 412 and determines data representing a trajectory 414 (sometimes referred to as a route) that the AV 100 may travel in order to reach (e.g., arrive at) the destination 412. In order for the planning module 404 to determine the data representing the trajectory 414, the planning module 404 receives data from the perception module 402, the positioning module 408, and the database module 410.

[0081] The perception module 402 uses, for example, Figure 1 The one or more sensors 121 shown are used to identify nearby physical objects. The objects are classified (e.g., grouped into types such as pedestrians, bicycles, cars, traffic signs, etc.), and a scene description including the classified objects 416 is provided to the planning module 404. For example, the one or more sensors 121 may determine that a UPS delivery vehicle is in front of the AV 100.

[0082] The planning module 404 also receives data representing the AV position 418 from the positioning module 408. The positioning module 408 determines the AV position by using data from the sensor 121 and data from the database module 410 (e.g., geographic data) to calculate the position. For example, the positioning module 408 uses data from a GNSS (Global Navigation Satellite System) sensor and geographic data to calculate the longitude and latitude of the AV. In an embodiment, the data used by the positioning module 408 includes a high-precision map with lane geometry attributes, a map describing the road network connection attributes, a map describing the physical attributes of the lane (such as traffic speed, traffic volume, the number of vehicles and bicycle lanes, lane width, lane traffic direction, or lane marking type and location, or a combination thereof), and a map describing the spatial location of road features (such as crosswalks, traffic signs, or various types of other driving signals, etc.). In an embodiment, the high-precision map is constructed by adding data to the low-precision map via automatic or manual annotation.

[0083] The control module 406 receives data representing the trajectory 414 and data representing the AV position 418, and operates control functions 420a-420c (e.g., steering, throttle, brakes, ignition) of the AV in a manner that will cause the AV 100 to travel the trajectory 414 to reach the destination 412. For example, if the trajectory 414 includes a left turn, the control module 406 will operate the control functions 420a-420c in the following manner: the steering angle of the steering function will cause the AV 100 to turn left, and the throttle and brakes will cause the AV 100 to pause and wait for a passing pedestrian or vehicle before making the turn.

[0084] Path Planning

[0085] Figure 5 Shown (for example, Figure 4 500 of the relationship between the inputs and outputs of the planning module 404 (shown in FIG. 5 ). Typically, the output of the planning module 404 is a route 502 from a starting point 504 (e.g., a source location or initial location) to an end point 506 (e.g., a destination or final location). The route 502 is typically defined by one or more road segments. For example, a road segment refers to a distance to be traveled through at least a portion of a street, road, highway, lane, or other physical area suitable for automobile travel. In some examples, for example, if the AV 100 is an off-road vehicle such as a four-wheel drive (4WD) or all-wheel drive (AWD) car, SUV, or small truck, the route 502 includes "off-road" road segments such as unpaved roads or open fields.

[0086] In addition to the route 502, the planning module also outputs lane-level route planning data 508. The lane-level route planning data 508 is used to traverse the segments of the route 502 at a specific time based on the conditions of the segments. For example, if the route 502 includes a multi-lane highway, the lane-level route planning data 508 includes trajectory planning data 510, which the AV 100 can use to select a lane from the multiple lanes based on, for example, whether an exit is approaching, whether there are other vehicles in one or more of the multiple lanes, or other factors that change over the course of a few minutes or less. For residential roads, mail delivery vehicles may frequently stop in the rightmost lane. Similarly, in some implementations, the lane-level route planning data 508 includes a rate constraint 512 that is specific to a segment of the route 502. For example, if the segment includes pedestrians or unexpected traffic, the rate constraint 512 can limit the AV 100 to a travel rate that is slower than the expected rate, such as a rate based on the speed limit data for the segment.

[0087] In an embodiment, inputs to the planning module 404 include (e.g., Figure 4 ) database data 514, current location data 516 (e.g., Figure 4 AV position 418 shown), (e.g., for Figure 4 Destination data 518 and object data 520 (e.g., such as destination 412) shown in FIG. Figure 44 (a) and (b) in some embodiments, the database data 514 includes rules used in planning. The rules are specified using a formal language (e.g., using Boolean logic). In any given situation encountered by the AV 100, at least some of these rules will apply to the situation. A rule applies to a given situation if it has a condition that is satisfied based on information available to the AV 100 (e.g., information about the surrounding environment). Rules can have priorities. For example, a rule of "if the road is a highway, move to the leftmost lane" can have a lower priority than "if the exit is within one mile, move to the rightmost lane". As another example, a rule of "if the vehicle in front of the AV 100 is a mail delivery vehicle, attempt an overtaking maneuver" can have a lower priority than "if in a no-overtaking zone, stay in the current lane".

[0088] Trajectory estimation of objects in the environment

[0089] Figure 6 The AV 100 is shown traveling along a road segment 608 within the environment 600. Various moving objects may interfere with the route or path of the AV 100 during the travel of the AV 100. For example, another vehicle, a pedestrian, or a bicycle may interfere with the route of the AV 100. Figure 6 As shown, two objects in environment 600 (vehicle "A" and vehicle "B") are within range 602 of AV 100. Range 602 is a subset of environment 600 that AV 100 "sees" objects that may interfere with the AV 100's path.

[0090] For example, vehicle "B" is 5 feet ahead (the ahead is represented by the forward direction of travel of the AV 100) and in the rightmost travel lane, and vehicle "A" is immediately to the left of the AV 100 and in the leftmost travel lane. In some embodiments, a radius of 20m around the AV 100 represents the range 602. In some embodiments, the range 602 spans an area that the AV 100 may traverse in the next 5-8 seconds. In some embodiments, the range 602 is biased to the front of the AV 100 so that more emphasis is placed on the front of the AV 100 rather than the rear of the AV 100. As such, the range 602 depends on the speed (velocity and heading) of the AV 100.

[0091] The sensors of AV 100 (e.g., sensor 121) detect the presence of both vehicle "A" and vehicle "B" (e.g., LiDAR 123 "sees" vehicle "A" as shown by the ray trace 636 of light). Figure 4As described, this information is processed by perception module 402 for object classification 416. However, other sensors of AV 100 may be used to detect the presence of vehicles "A" and "B." For example, RADAR, cameras, proximity sensors, and / or any of the sensors described above may also be used.

[0092] However, simply knowing that vehicle "A" is to the left of AV 100 is not sufficient to determine the behavior of vehicle "A." Additionally, perception module 402 may determine a trajectory of the object representing the next few seconds (e.g., 2-5 seconds) of the object's travel. The trajectory may demonstrate that the car to the left of AV 100 is traveling straight ahead at 20 mph, but does not know if the driver is about to make a sudden maneuver (e.g., switch lanes to make an impending turn).

[0093] For example, if the planning module of the AV 100 dictates that the AV 100 should turn left at the upcoming intersection 640, the AV 100 may determine that the best course of action is to slow down and enter the leftmost lane behind vehicle "A". However, if it is known that vehicle "A" is a bus that is about to stop at the bus stop 642, the AV 100 may determine that the best course of action is to remain in the current lane and enter the leftmost lane after vehicle "A" has slowed down to stop at the bus stop 642. In contrast, if the AV 100 has slowed down and moved behind vehicle "A", the AV 100 will need to wait behind vehicle "A" at the bus stop 642 or perform an overtaking maneuver. Neither of these is ideal from a time and / or occupant comfort perspective.

[0094] Knowing the expected route that vehicle "A" is expected to take can be used to determine which lane the AV 100 should be in at any given time. The expected route is defined as a fixed, pre-planned, scheduled, unchangeable or other form of route derived based on a fixed, pre-planned, scheduled or unchangeable route that an object will take. In particular, this information is used by the planning module 404 to update the route 502 (described above with reference to FIG. Figure 5 600 , the planning module 404 may also include lane-level routing data 508. Although it is unlikely that the overall route 502 of the AV 100 would change due to knowledge of the expected routes of objects in the environment 600, such changes are possible. For example, the expected route of vehicle "A" may indicate that vehicle "A" is expected to stop at bus stop 642 and then continue to travel left at intersection 640. Knowledge of this expected route by the planning module 404 will enable the planning module 404 to determine the optimal instantaneous lane position for the AV 100.

[0095] The expected route can be a one-time or temporary route (e.g., a recreational driver, a delivery service, an engineering vehicle), or a recurring route (e.g., a postal service, a bus). In addition, knowing the distance and speed along the expected route enables the perception module 402 to roughly estimate the future time when the object is expected to be at that location along the expected route. For example, the future time can represent at least 5 seconds in the future, but in some embodiments, the expected route defines the expected location of the object during the next minute. In some embodiments, the perception module 402 can estimate the expected location vs. time by knowing the current speed limit, the current trajectory of the object, and / or traffic congestion.

[0096] An important aspect of the expected route information is that it provides an estimate of the location of the object at a future time. For example, knowing the expected location of an object 10 seconds in the future is very helpful. In most embodiments, knowing the location of an object 5-8 seconds in the future provides sufficient accuracy for fusion with the object's trajectory data to generate an improved prediction of the object's future location. Future times much longer than 10 seconds may not be very helpful because the arrangement of all possible movements of objects within environment 600 is large and may be difficult to accurately predict, however, times longer than 10 seconds in the future are sometimes used.

[0097] The expected route that the object is expected to take can be determined in several ways. One method is to communicate directly with the object using vehicle-to-vehicle communication to retrieve the information. For example, in some embodiments, vehicle "A" wirelessly broadcasts identification information including its expected route (e.g., via WiFi, Bluetooth, or low-power AM / FM frequencies). This is advantageous for commercial drivers from a safety perspective because it allows cars near the commercial driver to be aware of the commercial driver's next move. In some embodiments, the route information is communicated from a transceiver of a mobile device within vehicle "A". For example, when a driver or passenger in vehicle "A" uses his or her smart phone to provide directions to a destination, the direction information is wirelessly provided to AV 100 via WiFi, etc. In this case, the smart phone's transceiver broadcasts the direction information to AV 100.

[0098] However, in some embodiments, the identification information will not include the object's expected route. To the extent that the identification information reveals other characteristics related to the object, the transceiver of AV 100 may query a remote server and / or the Internet to determine the object's expected route using any known characteristics. In some embodiments, the transceiver communicates with an external server associated with vehicle "A" and / or vehicle "B" via the public Internet.

[0099] For example, if the identification information reveals that vehicle "A" is a public transportation bus, the transceiver of AV 100 may query a public transportation server to retrieve a map of all buses having expected paths that traverse the bus' approximate current location. Here, the current location is estimated relative to the known current location of AV 100. However, in some embodiments, the current location is approximated to the current location of AV 100 itself.

[0100] In some embodiments, route information is retrieved from a server associated with the object. For example, route information for a UPS delivery vehicle is obtained from a UPS server. Likewise, route information for an Uber vehicle is obtained from an Uber server. This can be done using an API. In some embodiments, an agreement is reached to allow the transceiver of the AV 100 or a server associated with the AV 100 to access route information. In some embodiments, the expected route data is downloaded from the server and stored locally. In some embodiments, the data is processed to further classify the object. In some cases, the data is shared with nearby autonomous vehicles.

[0101] Sometimes, the query may reveal more than one unique solution. For example, two public transportation buses may cross through the current location along their paths. In these cases, other aspects of the expected route may be considered. The method may also compare the speed of the object with the expected speed along the expected route. For example, an expected route that indicates a bus stop is approaching but the vehicle does not seem to slow down may indicate that the expected route is incorrect. Another indication is lane position. For example, a vehicle that is expected to turn left but is in the rightmost lane may not be on the expected route. Other aspects include the time of day (or day of the week or season of the year) when the object is expected to follow the expected route. If the perception module 402 cannot uniquely establish the expected route, the AV 100 may not use this information in the planning module 404. In some embodiments, when multiple expected routes are identified, the planner module 404 considers the possibility that the object may follow any of these paths and assumes a combination of all possible routes.

[0102] In some embodiments, the expected route may contradict the current position and / or speed of the object. When comparing the trajectory of the object to the expected route of the object, the perception module 402 may determine that the expected route information is not meaningful. For example, if the expected route indicates that the vehicle should be traveling through town at the current time, the expected route information is considered inaccurate or unreliable. However, if the current position of the object is along the expected route, the perception module 402 may conclude that the object is traveling along the expected route.

[0103] A confidence level is assigned based on the accuracy and / or reliability of the object's expected route. The confidence level is provided as an input to the planning module 404, where the expected route of the object is weighted (or emphasized) based on its associated confidence level. In some embodiments, the planning model 404 uses a Kalman filtering method to incorporate the confidence level into the planning process. If the confidence level is low (e.g., less than 50% confidence), the information may not be used. However, if the expected route data appears reliable (e.g., the confidence level is greater than 70%), the object's trajectory information is fused with the expected route information to improve the object's expected position over the next 5-8 seconds.

[0104] In some embodiments, the perception module 402 determines how reliable the expected route information is. For example, an expected route that was last updated 10 seconds ago may be given more weight than a route that was last updated a year ago. In some embodiments, the reputation of the data source is also considered. For example, a source from a secure site may be given more weight than a source from an unsecure site. A source that has recently been in danger (e.g., from a hacking incident) may not be considered or accessed at all.

[0105] In some embodiments, information obtained from a vehicle or a mobile device associated with an object is sufficient to uniquely identify the object. For example, a car broadcasting its location and route is sufficient information to determine the expected route. In other embodiments, the classification module may determine that the car is to the left of the AV 100 (e.g., LiDAR may be used to identify the object as a car), but little is known about the car. In these embodiments, the AV 100 may instruct onboard sensors to observe the car closely to determine if a company logo or other vehicle identification is present. For example, knowledge of company logos, vehicle identification, colors, occupant detection, vehicle sounds, open / closed doors, flasher lights on, etc. represents the state of the car and may be used to filter the set of all possible matches.

[0106] For example, the classification module of AV 100 may be determined as Figure 6 The vehicle "B" in the AV 100 is a truck. The AV 100 may then instruct the classification module to perform a second classification to see if any identifying information (or additional information) can be obtained. The second classification may reveal that the truck is brown and has the letters "UPS" on the side. This information may be used to determine that the truck is likely to be a UPS delivery truck. Furthermore, when additional information about an object is received, it may be useful to store the information for later use (e.g., in a database). In some embodiments, this information may be sent to a machine learning module within the system pipeline for improved object classification. This additional information may be used to train objects with similar signatures or features.

[0107] Knowing that vehicle "B" is a UPS truck, the AV 100 can then query the UPS server to determine the planned delivery routes of UPS trucks in the area. This may result in two or more possible matches as described above. However, using additional information such as the current track of the UPS truck, these options can be filtered down to a unique route. This becomes the expected route for vehicle "B".

[0108] If the expected path of vehicle "B" indicates that vehicle "B" will proceed straight through the intersection 640, the AV 100 is warned that vehicle "B" may enter the travel lane of the AV 100, or perform an illegal maneuver to proceed straight through the intersection 640 despite being in the right turn only lane. Due to maintaining alert, the AV 100 is advised to maintain a safe distance from vehicle "B". For example, the control module 406 can decelerate the vehicle (e.g., reduce the throttle 420b and / or apply the brake 420c) in response to maintaining alert.

[0109] In some embodiments, the perception module 402 determines the uncertainty of the updated trajectory of the vehicle or object based on fusing the trajectory information with the expected route information. For example, if the vehicle appears to be moving quickly, but the expected route indicates an approaching turn, a higher uncertainty score can be assigned to indicate that the trajectory of the vehicle may not be accurate. On the other hand, if the vehicle is slowing down in the right lane of travel, and the route indicates that a right turn is approaching, a higher confidence (lower uncertainty) score can be assigned to the updated trajectory, indicating that the trajectory is accurate. Such information can be used to influence the path taken by the AV 100 as defined by the planning module 404.

[0110] In most embodiments, perception module 402 sends the updated trajectory of the object to planning module 404 to update the route and / or instantaneous lane position of AV 100. Sometimes, the updated trajectory calculation is performed in the cloud or on a remote server and sent to a controller configured on AV 100 for processing.

[0111] Reference again Figure 6 , as the AV 100 approaches the intersection 640, a bicycle 644 enters the range 602. The classification module may determine that the bicycle 644 is associated with "Uber Eats". By querying the "Uber Eats" server, the perception module 402 determines that the bicycle 644 will proceed straight through the intersection 640. In this way, the AV 100 is warned that the bicycle will likely cross the path of the AV 100. The control module 406 controls the AV 100 to slow down and / or give way to the bicycle 644.

[0112] Likewise, when vehicle "C" is in range 602, the AV 100 learns through the classification pipeline that vehicle "C" is a fire truck with a sirens activated. For example, an onboard microphone may be used to determine that a sirens is associated with vehicle "C". The transceiver of the AV 100 queries the online data for active fires nearby and determines that the fire truck is likely to turn left at intersection 640. In this case, the control module 406 of the AV 100 controls the AV 100 to slow down and give way to vehicle "C" regardless of the traffic signal. In addition, the AV 100 anticipates that vehicle "C" will enter the travel lane of the AV 100 and is alert to such maneuvers by vehicle "C".

[0113] Fig. 7A and 7B is a decision tree of process 700 of AV 100. The process receives a request from perception module 402 or planning module 404 of AV 100 to determine a trajectory of an object (e.g., a nearby vehicle) (702). The classification module queries for data related to the object (704). This may include: requesting and / or receiving an indication of the presence of the object from at least one sensor of the vehicle (e.g., camera, LiDAR, RADAR) (706). This may also include: requesting and / or receiving an indication of the presence of the object from a transceiver of the object or from a mobile device within the object (708). Process 700 determines whether the object is present (710). If the object is not present, information that the object is not present is sent (712).

[0114] However, if an object is present, two parallel calculations are performed. One calculation is to determine the trajectory of the object (e.g., current location, speed, and direction of travel) (714), and to determine the confidence of the trajectory (716). The second calculation is to determine whether the received data is sufficient to determine the expected route of the object (718). If the received data is insufficient to determine the expected route of the object, additional information of at least one state of the object (e.g., company information, vehicle id, RADAR signature, camera color, engine sound) is received (e.g., through a second classification process) (720). Using this information, the expected route of the object is determined (722). This is determined at least in part by sending and / or receiving information from a server, a transceiver of the object, and / or from a mobile device within the object (724).

[0115] The trajectory of the object is compared to the expected route (e.g., is the object where the object should be? Is the object's velocity consistent with the expected velocity along the expected route?) (726). If "yes," the reliability of the expected route information (e.g., age of the data, reputation of the data source) is determined (728). If the expected route information is reliable, the object information, including additional information of the expected route and at least one state of the object, is sent to a machine learning module of the autonomous vehicle (730). Because the expected route information is reliable, the emphasis associated with the expected route is increased, so that the expected route information plays an important role in the updated trajectory determination (732). On the other hand, if the expected route data is inconsistent or unreliable, the process reduces the emphasis on using the route information to update the trajectory of the object (734).

[0116] The trajectory of the object is updated based on the expected path of the object using the special emphasis defined in step 732 or 734. The uncertainty of the updated trajectory is determined (738). The updated trajectory of the object is sent to a planning module of the autonomous vehicle (e.g., AV 100) (740).

[0117] Figure 8 800 is a flow chart of a method 800 for improving trajectory estimation of an object in an environment. The method includes: receiving, using at least one processor, information indicating the presence of an object operating in the environment (802). The at least one processor determines a trajectory of the object, wherein the trajectory includes at least a position, a velocity, and a direction of travel of the object (804). Determining an expected route for the object, wherein the expected route is pre-planned and includes an expected future position of the object at a future time (806). Comparing the trajectory of the object with the expected route of the object (808). And, based on the comparison result that the trajectory of the object is consistent with the expected route of the object, updating the trajectory of the object based on the expected route of the object (810).

[0118] In the previous description, embodiments of the present invention have been described with reference to many specific details, which may vary depending on implementation. Therefore, the description and the accompanying drawings should be regarded as illustrative, rather than restrictive. The only and exclusive indication of the scope of the present invention, and the applicant's expectation that the content of the scope of the present invention is the literal and equivalent scope of the claims authorized from this application in the specific form of the authorized claims, including any subsequent amendments. Any definition of the terms used to be included in such claims clearly set forth herein should be based on the meaning of such terms as used in the claims. In addition, when the term "also includes" is used in the previous description or the attached claims, the following of the phrase can be an additional step or entity, or a sub-step / sub-entity of the previously described step or entity.

Claims

1. A trajectory planning method, include: Receiving, with at least one processor, information indicative of the presence of an object operating in an environment; determining, with the at least one processor, a trajectory of the object representing future travel of the object, the trajectory comprising at least a position, a velocity, and a direction of travel of the future travel of the object; determining, with the at least one processor, an expected route for the object, wherein the expected route is a pre-planned route that the object is expected to take and includes an expected future location of the object at a future time; comparing a trajectory of the object representing future travel of the object to an expected route of the object; determining, based on the results of the comparison, that a trajectory of the object representing future travel of the object is consistent with an expected route of the object, and determining a confidence level associated with the expected route of the object, and A trajectory of the object is updated based on the expected route of the object and a confidence level associated with the expected route of the object.

2. The method according to claim 1, in, Determining the expected route of the object includes receiving route information from a server.

3. The method according to claim 1 or 2, in, The determination of the expected route is based on route information received from a transceiver or mobile device associated with the object.

4. The method according to claim 1 or 2, in, The future time is at least 5 seconds in the future.

5. The method according to claim 1 or 2, in, Comparing the trajectory of the object to an expected route for the object includes determining that the position of the object is an expected position along the expected route.

6. The method according to claim 1 or 2, in, Comparing the trajectory of the object to an expected route for the object includes determining that a speed of the object is an expected speed along the expected route.

7. The method according to claim 1 or 2, in, The information received is from at least one sensor of the host vehicle.

8. The method according to claim 1 or 2, in, The at least one processor is part of a remote server.

9. The method according to claim 1 or 2, in, The received information is from a transceiver or mobile device associated with the object.

10. The method according to claim 1 or 2, further comprising: include: determining whether the received information is sufficient to determine the intended route of the object; as well as Based on determining that the received information is insufficient to determine the intended route of the object, additional information of at least one state of the object is received.

11. The method according to claim 10, in, The additional information received is from at least one sensor of the host vehicle.

12. The method according to claim 10, further comprising: include: Based on determining that the received information is insufficient to determine the expected route of the object, the received additional information is sent to a machine learning module for object classification.

13. The method according to claim 1 or 2, further comprising: include: Based on the comparison result that the trajectory of the object is consistent with the expected path of the object, the uncertainty of the updated trajectory is determined.

14. The method according to claim 1 or 2, further comprising: include: Sending updated trajectory information of the object.

15. The method according to claim 1 or 2, in, Determining a confidence level associated with the expected path of the object includes: At least one of accuracy and reliability of the expected route of the object is determined based on at least one of a time when the expected route of the object was updated and a reputation of a data source from which the expected route of the object was obtained.

16. The method according to claim 1 or 2, in, Updating the trajectory of the object based on the expected path of the object and a confidence level associated with the expected path of the object includes: determining that a confidence level associated with the expected path of the object is greater than a predetermined threshold; and The trajectory of the object is updated by increasing the emphasis associated with the expected path of the object.

17. The method according to claim 1 or 2, in, Updating the trajectory of the object based on the expected path of the object and a confidence level associated with the expected path of the object includes: determining that a confidence level associated with the object's expected path is below a predetermined threshold; and The trajectory of the object is updated by reducing an emphasis associated with the expected path of the object.

18. A non-transitory computer-readable storage medium, comprising at least one program for execution by at least one processor of a first device, the at least one program comprising instructions which, when executed by the at least one processor, cause the first device to perform the method according to any one of claims 1 to 17.

19. A vehicle, include: at least one sensor configured to capture information of the object; at least one transceiver configured to transmit and receive route information of the object; as well as At least one processor is communicatively coupled to the at least one sensor and the at least one transceiver and is configured to execute computer executable instructions, the execution resulting in the performance of the method according to any one of claims 1 to 17.

20. A computer program product comprising a computer program which, when executed by a processor, performs the method according to any one of claims 1 to 17.

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