Trajectory planning method and device for vehicle, equipment and automatic driving vehicle

Abstract

The invention provides a trajectory planning method and device for a vehicle, electronic equipment, a computer readable storage medium, a computer program product and an automatic driving vehicle, and relates to the field of artificial intelligence, in particular to the technical field of automatic driving and intelligent traffic. According to the implementation scheme, the speed and position of one or more risk obstacles around a vehicle are obtained; according to the speed and the position of the one or more risk obstacles, a lane with the low risk relative to the one or more risk obstacles is selected from adjacent lanes on the two sides of the vehicle to serve as a low-risk lane; for each lane in the current driving lane and the low-risk lane of the vehicle, respectively generating an advancing track of the lane; and selecting a travel trajectory of the vehicle from travel trajectories of the current travel lane and the low-risk lane of the vehicle according to the positions of the one or more risk obstacles.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, in particular to the field of autonomous driving and intelligent transportation technology, and specifically to a trajectory planning method, device, electronic device, computer-readable storage medium, computer program product and autonomous driving vehicle for a vehicle. Background Art

[0002] With the rapid development of computer technology and artificial intelligence, autonomous vehicles have shown broad application prospects in many fields such as transportation, military, logistics and warehousing, and daily life. As a core component of autonomous driving technology, trajectory planning requires timely adjustment of trajectory planning according to surrounding obstacles to ensure vehicle safety.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0004] The present disclosure provides a trajectory planning method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for a vehicle.

[0005] According to one aspect of the present disclosure, a trajectory planning method for a vehicle is provided, comprising: acquiring the speed and position of one or more risk obstacles around the vehicle; selecting a side lane with a lower risk relative to the one or more risk obstacles from adjacent lanes on both sides of the vehicle as a low-risk lane according to the speed and position of the one or more risk obstacles, wherein the adjacent lanes include a left lane located on the left side of the vehicle and a right lane located on the right side of the vehicle; for each lane in a current driving lane and a low-risk lane of the vehicle, generating a travel trajectory of the lane respectively, wherein the travel trajectory of the current driving lane corresponds to a path that the vehicle will travel along the current driving lane, and the travel trajectory of the low-risk lane corresponds to a path that the vehicle will change lanes from the current driving lane to the low-risk lane, and travel along the low-risk lane after completing the lane change; and selecting the vehicle's travel trajectory from the vehicle's current driving lane and the travel trajectory of the low-risk lane according to the position of the one or more risk obstacles. According to another aspect of the present disclosure, a trajectory planning device for a vehicle is provided, comprising: an obstacle determination unit, configured to: obtain the speed and position of one or more risk obstacles around the vehicle; a lane selection unit, configured to: select, according to the speed and position of the one or more risk obstacles, a side lane with a lower risk relative to the one or more risk obstacles from adjacent lanes on both sides of the vehicle as a low-risk lane, wherein the adjacent lanes include a left lane located on the left side of the vehicle and a right lane located on the right side of the vehicle; a trajectory generation unit, configured to: for each lane in a current driving lane and a low-risk lane of the vehicle, respectively generate a travel trajectory of the lane, wherein the travel trajectory of the current driving lane corresponds to a path that the vehicle will travel along the current driving lane, and the travel trajectory of the low-risk lane corresponds to a path that the vehicle will change lanes from the current driving lane to the low-risk lane, and travel along the low-risk lane after completing the lane change; and a trajectory selection unit, configured to: select the travel trajectory of the vehicle from the travel trajectories of the current driving lane and the low-risk lane of the vehicle according to the positions of the one or more risk obstacles. According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the present disclosure.

[0006] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to cause a computer to execute the method described in the present disclosure.

[0007] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the method described in the present disclosure when executed by a processor.

[0008] According to another aspect of the present disclosure, an autonomous driving vehicle is provided, comprising the electronic device according to the present disclosure.

[0009] According to another aspect of the present disclosure, an edge computing device is provided, including the electronic device according to the present disclosure.

[0010] According to one or more embodiments of the present disclosure, by screening low-risk lanes for trajectory planning, the complexity of trajectory selection is reduced, so that the vehicle can plan the trajectory more quickly and accurately.

[0011] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1 A schematic diagram showing an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0014] Figures 2A-2C A schematic diagram showing an application scenario of trajectory planning according to an embodiment of the present disclosure;

[0015] Figure 3 A flow chart of a trajectory planning method for a vehicle according to an embodiment of the present disclosure is shown;

[0016] Figure 4 A schematic diagram showing an application scenario of trajectory planning according to an embodiment of the present disclosure;

[0017] Figure 5 A flowchart showing a process of selecting a low-risk lane from adjacent lanes on both sides of a vehicle according to an embodiment of the present disclosure;

[0018] Figure 6 A schematic diagram showing an application scenario of trajectory planning according to an embodiment of the present disclosure;

[0019] Figure 7 A flowchart showing a process of selecting a vehicle's travel trajectory from a current driving lane of the vehicle and a travel trajectory of a low-risk lane according to an embodiment of the present disclosure;

[0020] Figure 8 A schematic diagram showing an application scenario of trajectory planning according to an embodiment of the present disclosure;

[0021] Fig. 9 A schematic diagram showing an application scenario of trajectory planning according to an embodiment of the present disclosure;

[0022] Fig.10 A structural block diagram of a trajectory planning device for a vehicle according to an embodiment of the present disclosure is shown; and

[0023] Fig.11 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0024] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0025] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0026] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0027] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0028] Figure 1 FIG. 1 is a schematic diagram of an exemplary system 100 in which various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes a motor vehicle 110 , a server 120 , and one or more communication networks 130 coupling the motor vehicle 110 to the server 120 .

[0029] In an embodiment of the present disclosure, the motor vehicle 110 may include a computing device according to an embodiment of the present disclosure and / or be configured to perform a method according to an embodiment of the present disclosure.

[0030] The server 120 may run one or more services or software applications for the trajectory planning method of the vehicle. In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. A user of the motor vehicle 110 may in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0031] Server 120 may include one or more general purpose computers, dedicated 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 virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0032] The computing units in the server 120 may run one or more operating systems including any of the above operating systems and any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0033] In some embodiments, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from motor 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 motor vehicle 110.

[0034] The network 130 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including, but not limited to, TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 130 may be a satellite communications network, a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infrared network, a wireless network (including, for example, Bluetooth, WiFi), and / or any combination of these and other networks.

[0035] The 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. The data repository 150 may reside in various locations. For example, the data repository used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data repository 150 may be of different types. In some embodiments, the data repository used by the server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to a command.

[0036] 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 different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0037] The motor vehicle 110 may include a sensor 111 for sensing the surrounding environment. The sensor 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 laser radar (LiDAR). Different sensors may provide different detection accuracy and range. The camera may be installed at the front, rear, or other location of the vehicle. The visual camera may 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 camera, information such as traffic light indications, intersection conditions, and the operating status of other vehicles may be obtained. The infrared camera may capture objects under night vision conditions. Ultrasonic sensors may be installed around the vehicle to measure the distance of objects outside the vehicle from the vehicle using the characteristics of strong ultrasonic directionality. Millimeter wave radars may be installed at the front, rear, or other location of the vehicle to measure the distance of objects outside the vehicle from the vehicle using the characteristics of electromagnetic waves. Laser radars may be installed at the front, rear, or other location of the vehicle to detect object edges and shape information, thereby identifying and tracking objects. Due to the Doppler effect, the radar device may also measure the speed change of the vehicle and the moving object.

[0038] 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 satellites 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, and the mobile communication network may implement any suitable communication technology, such as GSM / GPRS, CDMA, LTE, and other current or evolving wireless communication technologies (e.g., 5G technology). The communication device 112 may also have a vehicle-to-vehicle or vehicle-to-everything (V2X) module, which is configured to implement, for example, vehicle-to-vehicle (V2V) communication with other vehicles 143 and vehicle-to-infrastructure (V2I) communication with infrastructure 144. In addition, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to a smartphone, a tablet computer, or a wearable device such as a watch) by, for example, using a wireless LAN or Bluetooth of the IEEE802.11 standard. Using the communication device 112, the motor vehicle 110 may also access the server 120 via the network 130.

[0039] The motor vehicle 110 may also include a control device 113. The control device 113 may include a processor that communicates with various types of computer-readable storage devices or media, such as a central processing unit (CPU) or a graphics processing unit (GPU), or other dedicated processors. The control device 113 may include an automatic driving system for automatically controlling various actuators in the vehicle. The automatic 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 limited human intervention. Some processing functions of the control device 113 can be implemented through cloud computing. For example, some processing can be performed using an on-board processor, while other processing can be performed using computing resources in the cloud. The control device 113 can be configured to perform a method according to the present disclosure. In addition, the control device 113 can be implemented as an example of a computing device on the motor vehicle side (client) according to the present disclosure.

[0040] It is understandable that the motor vehicle does not necessarily have to include the various vehicle-side sensing devices described above. According to some embodiments of the present invention, safe and reliable autonomous driving can still be achieved even when the motor vehicle does not have or does not enable these vehicle-side sensing devices.

[0041] Autonomous vehicles are extremely complex systems. The actual driving environment in which they are located is complex and changeable, which makes the safety of autonomous vehicles uncertain. Safety of the intended functionality (SOTIF) is an important part of the autonomous driving safety system, mainly to deal with the hazards caused by insufficient autonomous driving functions and foreseeable human misuse. The intended functional safety for single-vehicle autonomous driving (AD SOTIF) mainly involves five major aspects: perception, prediction, decision-making, control and human-computer interaction. Among them, perception and prediction are the outstanding problems currently faced.

[0042] Figure 1 The system 100 may be configured and operated in various ways to enable the application of various methods and apparatuses described in the present disclosure.

[0043] In the embodiments described in the present disclosure, “obstacles” refer to various objects that exist in autonomous driving and may interact with the vehicle performing autonomous driving or potentially affect its trajectory.

[0044] In the embodiments described in the present disclosure, a "lane" may be a real lane that actually exists physically, wherein the real lane is defined by physical facilities such as road markings, curbs, and guardrails. According to other embodiments, a lane in the present disclosure may be a virtual lane marked when there is no real lane (e.g., at an intersection), for example, a virtual lane generated based on road geometry, vehicle trajectory, and road usage habits. In the embodiments described in the present disclosure, a "vehicle" refers to a vehicle that is currently executing or preparing to execute an autonomous driving task (e.g., as described in reference Figure 3 The described trajectory planning method 300 for a vehicle, and may also be referred to as a “host vehicle”.

[0045] Figures 2A-2C A schematic diagram of an application scenario 200 of trajectory planning according to an embodiment of the present disclosure is shown.

[0046] According to some embodiments, the current trajectory of the obstacle crosses the current trajectory of the vehicle (e.g., the host vehicle in the autonomous driving scenario), for example, Figure 2A As shown, the vehicle 210 is moving at a speed v adc The vehicle is traveling in the current driving lane 242, and the obstacle 220 is moving at a speed v obs The vehicle 210 moves forward, so that the obstacle 220 may interact with the vehicle 210 in the interaction area 230 at a future moment. For example, the obstacle 220 is a pedestrian crossing the road, and the interaction area 230 is an area of ​​the intersection corresponding to the zebra crossing. According to some embodiments, the vehicle 210 may choose to continue driving in the current driving lane 242, or may choose to change lanes to the left lane 241 or the right lane 243 of the current driving lane 242.

[0047] According to some embodiments, the direction of travel of the obstacle is opposite to the direction of travel of the vehicle (e.g., the main vehicle in the autonomous driving scenario), for example, Figure 2B As shown, the vehicle 210 is moving at a speed v adc and the obstacle 220 moves at a speed v obs The vehicle 210 moves forward, so that the obstacle 220 may interact with the vehicle 210 in the interaction area 230 at a future moment. For example, the obstacle 220 is another vehicle coming from the opposite direction, and the interaction area 230 is an area where the other vehicle and the vehicle 210 will meet. According to some embodiments, the vehicle 210 may choose to continue driving in the current driving lane 242, or may choose to change lanes to the left lane 241 or the right lane 243 of the current driving lane 242.

[0048] According to some embodiments, part of the trajectory of the obstacle overlaps with part of the trajectory of the vehicle (e.g., the main vehicle in the autonomous driving scenario), for example, Figure 2C As shown, the vehicle 210 is moving at a speed vadc The vehicle is traveling in the current driving lane 242, and the obstacle 220 is moving at a speed v obs The vehicle 210 moves forward, so that the obstacle 220 may interact with the vehicle 210 in the interaction area 230 at a future moment. For example, the obstacle 220 is another vehicle that converges to the main road from a fork road different from the vehicle 210, and the interaction area is the intersection area of ​​the fork road and the main road. According to some embodiments, the vehicle 210 may choose to continue driving in the current driving lane 242, or may choose to change lanes to the left lane 241 or the right lane 243 of the current driving lane 242.

[0049] It can be seen that, as analyzed above, in autonomous driving, there are scenarios where various obstacles may interact with the vehicle. Therefore, it is necessary to promptly identify risk scenarios in autonomous driving and take corresponding actions, such as adjusting the vehicle's trajectory in the current lane, or changing lanes of the vehicle to avoid collision with obstacles.

[0050] In one prior art solution, the trajectory planning can be performed for the vehicle's current lane and the lanes on both sides of the vehicle at the same time. However, this may consume more computing resources and result in low efficiency of trajectory planning, and the corresponding actions cannot be taken in time to avoid obstacles. In another prior art solution, the trajectory planning can be performed only for the vehicle's current lane by default, and the trajectory planning can be performed for other lanes only when risks are identified, in order to save computing resources. However, this may cause the problem of missing the opportunity to change lanes due to omission of risk identification.

[0051] Therefore, according to an embodiment of the present disclosure, a trajectory planning method for a vehicle is provided. Figure 3 A flow chart of a trajectory planning method 300 for a vehicle according to an embodiment of the present disclosure is shown.

[0052] like Figure 3 As shown, the method 300 includes:

[0053] Step S310: Obtain the speed and position of one or more risk obstacles around the vehicle;

[0054] Step S320: According to the speed and position of one or more risk obstacles, a lane with a lower risk relative to the one or more risk obstacles is selected from adjacent lanes on both sides of the vehicle as a low-risk lane, wherein the adjacent lanes include a left lane located on the left side of the vehicle and a right lane located on the right side of the vehicle;

[0055] Step S330: for each lane of the vehicle's current driving lane and the low-risk lane, respectively generate a travel trajectory of the lane, wherein the travel trajectory of the current driving lane corresponds to a path that the vehicle will travel along the current driving lane, and the travel trajectory of the low-risk lane corresponds to a path that the vehicle will change lanes from the current driving lane to the low-risk lane, and travel along the low-risk lane after completing the lane change; and

[0056] Step S340: Selecting a vehicle's travel trajectory from the vehicle's current driving lane and the travel trajectory of the low-risk lane according to the location of one or more risk obstacles.

[0057] According to the embodiments of the present disclosure, the trajectory planning process is optimized by comprehensively considering the risks of obstacles around the vehicle, which not only improves the planning efficiency and accuracy, but also effectively enhances the safety of vehicle driving. Specifically, by performing risk assessment and pruning on adjacent lanes, the number of lanes that need to be planned is reduced, thereby effectively reducing the computational complexity of the trajectory planning process and making the planning process more efficient; by excluding adjacent lanes with higher risks, the final selected travel trajectory is ensured to be safer and the possibility of conflict with risky obstacles is reduced; and by explicitly assessing the risk of obstacles, the possibility of missing important risk scenarios is reduced, making the planning results more comprehensive and reliable.

[0058] According to some embodiments, risk obstacles may include pedestrians, other vehicles or other obstacles, etc., which are not limited here.

[0059] According to some embodiments, in an autonomous driving system, a vehicle can detect all obstacles around the vehicle through a variety of sensors (such as lidar, radar, and camera). However, in order to improve the decision-making efficiency and accuracy of the system, the system can further screen and analyze the detected obstacles to screen out the risk obstacles that really pose a risk to the vehicle.

[0060] According to some embodiments, in an autonomous driving system, a vehicle can obtain the position and speed of obstacles in real time through sensors while detecting surrounding obstacles. For example, a lidar can use laser ranging to provide the distance of the obstacle from the vehicle and the position of the obstacle, a radar uses the Doppler effect to measure the speed of the obstacle, and a camera can assist in identifying the position and direction of travel of the obstacle.

[0061] According to some embodiments, subsequent low-risk lane selection and travel trajectory selection are performed based on the acquired position and speed of the obstacle at the current moment.

[0062] According to some embodiments, based on the current driving lane of the vehicle, an obstacle among the detected obstacles that is relatively close to the current driving lane of the vehicle is determined as a risk obstacle.

[0063] According to some embodiments, obtaining obstacle information of one or more risk obstacles around a vehicle includes: for each obstacle around the vehicle, in response to a distance between the position of the obstacle and an entry point of the obstacle into a current driving lane of the vehicle being less than or equal to an interaction threshold, determining that the obstacle is a risk obstacle.

[0064] According to some embodiments, the entry point of the obstacle into the current lane of travel of the vehicle is the entry point of the obstacle into the current lane of travel of the vehicle in the direction of the speed of the obstacle. According to some embodiments, the entry point of the obstacle into the current lane of travel of the vehicle refers to the position where the obstacle enters the current lane of travel of the vehicle if the obstacle continues to travel at the current speed of the obstacle.

[0065] The following Figure 4 Take this as an example to illustrate: Figure 4 As shown, the entry point 401 of the obstacle 220 into the current driving lane 242 of the vehicle 210 may be determined based on the speed v of the obstacle 220 detected. obs Predicted, that is, the velocity v of the obstacle 220 along the obstacle 220 from the position of the obstacle 220 obs The intersection of a straight line extending in the direction of the obstacle and a boundary line of the current driving lane 242 of the vehicle 210 close to the obstacle 220 is determined as the entry point 401 of the obstacle 220 entering the current driving lane 242 of the vehicle 210, wherein the distance d1 between the obstacle 220 and the entry point 401 is the distance between the position of the obstacle and the entry point of the obstacle entering the current driving lane of the vehicle.

[0066] According to some embodiments, the interaction threshold may be a predetermined distance threshold, for example, 5 m.

[0067] According to some other embodiments, all detected obstacles may be regarded as risk obstacles, for example, when the number of detected obstacles is small.

[0068] According to some embodiments, in addition to the speed and position of the risk obstacle, other information of the risk obstacle may also be obtained, for example, the acceleration and posture of the risk obstacle.

[0069] According to some embodiments, the lane information may be provided by a professional map provider that provides high-precision maps, or may be acquired through a sensor acquisition and / or positioning system.

[0070] According to some embodiments, the adjacent lanes on both sides of the vehicle are the left lane and the right lane of the lane where the vehicle is currently located.

[0071] Figure 5The process of selecting a low-risk lane from adjacent lanes on both sides of a vehicle according to an embodiment of the present disclosure is shown (for example, Figure 3 According to some embodiments, Figure 5 As shown, step S320 includes:

[0072] Step S501: for each adjacent lane of the vehicle, determining a risk value of each obstacle among one or more risk obstacles on a side where the adjacent lane is located;

[0073] Step S502: for each adjacent lane, determining a risk value of the adjacent lane based on the risk value of an obstacle on the side where the adjacent lane is located among one or more risk obstacles; and

[0074] Step S503: Determine a low-risk lane based on the risk value of each adjacent lane of the vehicle.

[0075] According to the embodiments described in the present disclosure, it is possible to select a low-risk lane required for subsequent trajectory selection, and discard a lane with a higher risk, thereby saving computing resources compared to retaining the lane with a higher risk and a lower probability of being selected in the prior art solution.

[0076] According to some embodiments, determining the risk value of each of the one or more risk obstacles on the side where the adjacent lane is located for each adjacent lane of the vehicle in step S501 includes: for each of the one or more risk obstacles on the side where the adjacent lane is located, determining the time when the obstacle enters the current driving lane of the vehicle based on the speed and position of the obstacle; and for each of the one or more risk obstacles on the side where the adjacent lane is located, determining the risk value of the obstacle based on the time when the obstacle enters the current driving lane of the vehicle.

[0077] According to some embodiments, one or more risk obstacles are divided into obstacles on the side where each adjacent lane is located. For example, the side where the risk obstacle is located can be determined based on the direction of the risk obstacle relative to the vehicle, and / or the side where the risk obstacle is located can be determined based on the distance between the risk obstacle and each adjacent lane.

[0078] According to some embodiments, the time for the obstacle to enter the current lane of the vehicle refers to the time required for the obstacle to enter the current lane of the vehicle if the obstacle continues to move at the current speed of the obstacle, that is, Figure 4 In the scenario shown, the time between traveling from the current position of obstacle 220 to entry point 401.

[0079] According to some embodiments, determining the time when the obstacle enters the current lane of travel of the vehicle may include: determining the entry point of the obstacle into the current lane of travel of the vehicle based on the position and direction of the speed of the obstacle (e.g., as referenced to Figure 4 Determine the time when the obstacle enters the current lane of the vehicle based on the distance between the position of the obstacle and the entry point of the obstacle into the current lane of the vehicle and the speed of the obstacle.

[0080] According to some embodiments, the risk value of the obstacle is determined according to the time when the obstacle enters the current driving lane of the vehicle. For example, when the time when the obstacle enters the current driving lane of the vehicle is less than the first time threshold, the risk value of the obstacle is set to the first preset value; when the time when the obstacle enters the current driving lane of the vehicle is greater than or equal to the first time threshold and less than the second time threshold, the risk value of the obstacle is set to the second preset value; when the time when the obstacle enters the current driving lane of the vehicle is greater than or equal to the second time threshold and less than the third time threshold, the risk value of the obstacle is set to the third preset value.

[0081] According to some embodiments, in addition to the time when the obstacle enters the current lane of the vehicle, the risk value of the obstacle is determined based on the distance between the position of the obstacle and the entry point of the obstacle into the current lane of the vehicle.

[0082] According to some embodiments, for each of the one or more risk obstacles on the side where the adjacent lane is located, determining the risk value of the obstacle based on the time when the obstacle enters the current driving lane of the vehicle includes: for each of the one or more risk obstacles on the side where the adjacent lane is located, determining a first sub-risk value of the obstacle based on the time when the obstacle enters the current driving lane of the vehicle; determining a second sub-risk value of the obstacle based on the distance between the position of the obstacle and an entry point of the obstacle entering the current driving lane of the vehicle, wherein the entry point of the obstacle entering the current driving lane of the vehicle is an entry point of the obstacle entering the current driving lane of the vehicle along the direction of the speed of the obstacle; and determining the risk value of the obstacle based on the first sub-risk value and the second sub-risk value of the obstacle.

[0083] According to some embodiments, a Frenet coordinate system is defined based on the center line of the vehicle's current lane or other lanes (e.g., any adjacent lanes) to describe the position, distance, or length in an autonomous driving scenario.

[0084] According to some embodiments, for the vehicle's current driving lane or each lane in other lanes, for the Frenet coordinate system corresponding to the lane, the center line of the lane is used as a reference curve, the direction along the center line of the lane is used as the longitudinal direction, and the direction perpendicular to the center line of the lane is used as the lateral direction.

[0085] According to some embodiments, for each lane in the vehicle's current driving lane or other lanes, for the Frenet coordinate system corresponding to the lane, the s value represents the distance along the center line of the lane, and the d value represents the lateral offset relative to the center line of the lane.

[0086] According to some embodiments, for the vehicle's current driving lane or each lane in other lanes, a projection point where the vehicle's position is projected onto the center line of the lane is used as the coordinate origin.

[0087] According to some embodiments, the centerline of a lane is a line located in the center of the lane and extending along the geometric center axis of the road. According to some embodiments, the centerline of a lane may be a physically existing marking line, for example, a line clearly delineated by road surface markings such as solid lines or dashed lines, or may be a virtual reference line, for example, a reference line inferred from road geometry, map data, or sensor information. According to some embodiments, the process of determining the first sub-risk value of the obstacle based on the time when the obstacle enters the current lane of travel of the vehicle may be the same or similar to the process of determining the risk value of the obstacle based on the time when the obstacle enters the current lane of travel of the vehicle described above. According to some embodiments, determining the second sub-risk value of the obstacle based on the distance between the position of the obstacle and the entry point of the obstacle into the current lane of travel of the vehicle includes:

[0088] Based on the position and velocity of the obstacle, determine whether the vehicle enters the vehicle-obstacle interaction area (e.g., Figures 2A-2C The displayed interactive area 230) is the estimated obstacle position of the obstacle;

[0089] Determining a vehicle lateral distance based on the determined distance between the estimated obstacle position of the obstacle and an entry point of the obstacle into the current driving lane of the vehicle; and

[0090] A second sub-risk value of the obstacle is determined based on a difference between a distance between the position of the obstacle and an entry point of the obstacle into the current driving lane of the vehicle and a lateral distance of the vehicle.

[0091] According to some embodiments, the interaction area between the vehicle and the obstacle is a spatial range where interaction, collision or interference may occur between the vehicle and the surrounding obstacles. For example, the interaction area is as follows: Figure 2A The intersection shown corresponds to the area of ​​the zebra crossing, such as Figure 2BThe intersection where two vehicles may meet is shown, and Figure 2C The intersection area of ​​the fork road shown. According to some embodiments, the interaction area between the vehicle and the obstacle is determined based on environmental information (e.g., road planning information) in the autonomous driving, the current trajectory of the vehicle and / or the current trajectory of the obstacle.

[0092] According to some embodiments, as described above, the entry point of the obstacle into the current driving lane of the vehicle is the entry point of the obstacle into the current driving lane of the vehicle along the direction of the speed of the obstacle.

[0093] It should be understood that the lateral distance of the vehicle is calculated on the assumption that the obstacle and the vehicle continue to move in their current motion states, respectively (ie, maintain a constant speed and acceleration).

[0094] The following Figure 6 Take this as an example to illustrate: Figure 6 As shown, assuming that the vehicle 210 continues to move in the current motion state, it will enter the interaction area 230 with the obstacle 220 at the estimated vehicle position 610, and assuming that the obstacle 220 keeps moving at a speed v obs The obstacle 220 will move to the estimated obstacle position 620 at this time, wherein the vehicle lateral distance d2 is the distance between the estimated obstacle position 620 and the entry point 401 of the obstacle 220, and the entry point 401 can be the same as the reference Figure 4 The entry point 401 is the same or similar to the one described above, that is, it is a point that will be moved from the position of the obstacle 220 along the speed v of the obstacle 220. obs The intersection of a straight line extending in the direction of and a boundary line of the current driving lane 242 of the vehicle 210 on the side close to the obstacle 220 .

[0095] It should be understood that in order to clearly show the lateral distance of the vehicle, Figure 6 Only the vehicle’s current lane is drawn, while the vehicle’s adjacent lanes are omitted.

[0096] For example, the second risk value may be determined according to the following formula (1):

[0097] second_cost=original_obs_cutin_s-obs_dist_when_adc_cutin (1)

[0098] Among them, second_cost is the second risk value, original_obs_cutin_s is the distance between the position of the obstacle and the entry point of the obstacle into the current lane of the vehicle, and obs_dist_when_adc_cutin is the lateral distance of the vehicle, that is, as described above, the distance between the estimated position of the obstacle when the vehicle enters the interaction area between the vehicle and the obstacle and the entry point of the obstacle into the current lane of the vehicle.

[0099] According to some embodiments, a weighted sum may be performed on the first risk value and the second risk value to obtain the risk value of the obstacle.

[0100] For example, the risk value of the obstacle can be determined according to the following formula (2):

[0101] risk_cost=w1*first_cost+w2*second_cost (2)

[0102] Among them, risk_cost is the risk value of the obstacle, first_cost is the first risk value, second_cost is the second risk value, w1 is the first weight corresponding to the first risk value, and w2 is the second weight corresponding to the second risk value.

[0103] According to some embodiments, in step S502, for each adjacent lane, the risk values ​​of obstacles on the side of the adjacent lane among one or more risk obstacles are summed to determine the risk value of the adjacent lane. According to some embodiments, in step S503, the adjacent lane with a lower risk value is selected as the low-risk lane.

[0104] According to some embodiments, in step S330, for each lane of the vehicle's current driving lane and the low-risk lane, a travel trajectory of the lane is generated, wherein the travel trajectory of the current driving lane corresponds to the path that the vehicle will travel along the current driving lane, and the travel trajectory of the low-risk lane corresponds to the path that the vehicle will change lanes from the current driving lane to the low-risk lane, and travel along the low-risk lane after completing the lane change. According to some embodiments, the path refers to a set of points in space that the vehicle passes through from a position and continues to travel. According to some embodiments, the trajectory is a travel plan for the vehicle to start from a position and continue to travel, wherein the trajectory includes not only a set of points in space that the vehicle passes through from a position and continues to travel, but also includes dynamic information such as time, speed, acceleration, etc. of the vehicle at these points to indicate the specific driving behavior of the vehicle.

[0105] In some embodiments, in autonomous driving, the generated travel trajectory does not extend infinitely, but has a finite length. For example, the travel trajectory generated by the autonomous driving algorithm each time corresponds to a path with a preset length (e.g., 80 meters).

[0106] Figure 7 The process of selecting a vehicle's travel trajectory from the vehicle's current travel lane and the travel trajectory of the low-risk lane according to an embodiment of the present disclosure is shown (for example, Figure 7 According to some embodiments, step S340 includes:

[0107] Step S701: for each of the one or more risk obstacles, determine the longitudinal distance of the risk obstacle based on the difference between the s value of the entry point of the risk obstacle into the current driving lane of the vehicle in the first Frenet coordinate system and the s value of the position of the vehicle in the first Frenet coordinate system, wherein the entry point of the risk obstacle into the current driving lane of the vehicle is the entry point of the risk obstacle into the current driving lane of the vehicle along the direction of the speed of the risk obstacle, and the first Frenet coordinate system is a Frenet coordinate system defined based on the center line of the current driving lane of the vehicle;

[0108] Step S702: setting a risk obstacle with the smallest longitudinal distance among one or more risk obstacles as a key obstacle;

[0109] Step S703: for each lane in the current driving lane of the vehicle and the low-risk lane, determine the longitudinal risk value of the lane; and

[0110] Step S704: Selecting a vehicle's travel trajectory from the vehicle's current travel lane and the low-risk lane's travel trajectories based at least in part on the longitudinal risk values ​​of the vehicle's current travel lane and the low-risk lane. For example, when it is determined that the vehicle continues to travel along the current travel lane, selecting the travel trajectory of the current travel lane (i.e., a trajectory corresponding to a path that the vehicle will travel along the current travel lane, for example, Figure 8 For another example, when determining that the vehicle is changing lanes to an adjacent lane, the trajectory of the adjacent lane is selected (i.e., the trajectory corresponding to the path that the vehicle will change from the current driving lane to the low-risk lane and travel along the low-risk lane after completing the lane change, for example, Figure 8 The travel trajectory 853) is shown as the travel trajectory of the vehicle.

[0111] According to some embodiments, for each of the one or more risk obstacles, the longitudinal distance of the risk obstacle is determined based on the difference between the s value of the entry point of the risk obstacle into the current lane of the vehicle in the first Frenet coordinate system and the s value of the position of the vehicle in the first Frenet coordinate system, wherein the entry point of the risk obstacle into the current lane of the vehicle is the entry point of the risk obstacle into the current lane of the vehicle along the direction of the speed of the risk obstacle (for example, Figure 8 The entry point 401 in the vehicle is a Frenet coordinate system defined based on the center line of the vehicle's current driving lane.

[0112] According to some embodiments, in steps S701 and S702, the key obstacles closest to the vehicle in the longitudinal direction are screened out by the shortest longitudinal distance to be used for evaluating the travel trajectory of the vehicle's current driving lane and the low-risk lane, thereby efficiently performing the travel trajectory evaluation and further reducing the amount of calculation.

[0113] In practical applications, it is expected that the vehicle travels as far as possible while avoiding obstacles to avoid sudden braking or premature braking at a position far away from the interaction area. According to some embodiments, the length of the travel trajectory of each lane can be compared with the distance from the position of the vehicle to the potential interaction area between the vehicle and the obstacle.

[0114] According to some embodiments, for each of the vehicle's current driving lane and the low-risk lane, a longitudinal risk value (eg, Figure 7 Step S703 in the method includes:

[0115] Determine a target distance of the key obstacle corresponding to the lane based on a difference between an s-value of the obstacle intersection point of the lane in the Frenet coordinate system corresponding to the lane and an s-value of the position of the vehicle in the Frenet coordinate system corresponding to the lane, wherein the obstacle intersection point of the lane is an intersection point of the key obstacle traveling along a direction of the speed of the key obstacle with a center line of the lane, and the Frenet coordinate system corresponding to the lane is a Frenet coordinate system defined based on the center line of the lane; and

[0116] The longitudinal risk value of the lane is determined based on the target distance of the key obstacle corresponding to the lane and the planned length of the travel trajectory of the lane, wherein the planned length of the travel trajectory of the lane is the distance from the position of the vehicle on the travel trajectory of the lane to the travel end point of the travel trajectory of the lane, wherein, when there is a braking point in the travel trajectory of the lane, the travel end point on the travel trajectory of the lane is the braking point in the travel trajectory of the lane, and, when there is no braking point in the travel trajectory of the lane, the travel end point on the travel trajectory of the lane is the end point of the travel trajectory of the lane.

[0117] According to some embodiments, for each lane in the vehicle's current driving lane and the low-risk lane, the obstacle intersection point of the lane is the intersection point from the position of the key obstacle along the direction of the speed of the key obstacle to the center line of the corresponding lane.

[0118] For example, in Fig. 9 In the illustrated scenario, for the current driving lane 242 of the vehicle 210, the obstacle intersection point 965 of the lane may be a velocity v of the detected obstacle 220. obs Predicted, that is, the velocity v of the obstacle 220 along the obstacle 220 from the position of the obstacle 220 obs and the center line 252 of the current driving lane 242 of the vehicle 210; and, for the low-risk lane 243 of the vehicle 210, the obstacle intersection point 967 of the lane may be determined according to the speed v of the detected obstacle 220. obs Predicted, that is, the velocity v of the obstacle 220 along the obstacle 220 from the position of the obstacle 220 obs The intersection of a straight line extending in the direction of and a center line 253 of the low-risk lane 243 of the vehicle 210 .

[0119] According to some embodiments, the target distance of the key obstacle corresponding to the lane is the difference between the s value of the obstacle intersection point of the lane in the Frenet coordinate system corresponding to the lane and the s value of the vehicle's position in the Frenet coordinate system corresponding to the lane, wherein the Frenet coordinate system corresponding to the lane is a Frenet coordinate system defined based on the centerline of the lane.

[0120] For example, in Fig. 9 In the scene shown, for the current driving lane 242, the target distance of the vehicle 210 corresponding to the lane is the distance between the position of the vehicle 210 and the intersection point 965 of the obstacle in the lane on the center line 252 of the lane, that is, the distance between the center line projection point 968 of the lane and the intersection point 965 of the obstacle in the lane, wherein the center line projection point 968 of the vehicle 210 is the point where the position of the vehicle 210 is projected onto the center line 252 of the lane.

[0121] For example, in Fig. 9 In the scenario shown, for the low-risk lane 243, the target distance of the vehicle 210 corresponding to the lane is the distance between the position of the vehicle 210 and the intersection point 967 of the obstacle in the lane on the center line 253 of the lane, that is, the distance between the center line projection point 961 of the lane and the intersection point 967 of the obstacle in the lane, wherein the center line projection point 961 of the vehicle 210 is the point where the position of the vehicle 210 is projected onto the center line 253 of the lane.

[0122] According to some embodiments, for the vehicle's current driving lane and each of the low-risk lanes, the planned length of the travel trajectory of the lane is the distance from the vehicle's position on the travel trajectory of the lane to the travel end point of the travel trajectory of the lane, wherein, when there is a braking point in the travel trajectory of the lane, the travel end point on the travel trajectory of the lane is the braking point in the travel trajectory of the lane, and, when there is no braking point in the travel trajectory of the lane, the travel end point on the travel trajectory of the lane is the end point of the travel trajectory of the lane.

[0123] For example, in Fig. 9 In the scenario shown, there is a braking point 962 on the travel trajectory 952 of the current driving lane 242, and the planned length of the travel trajectory 952 of the current driving lane 242 is the distance from the position of the vehicle 210 on the travel trajectory 952 to the braking point 962, and there is no braking point on the travel trajectory 953 of the low-risk lane 243, and the planned length of the travel trajectory 953 of the low-risk lane 243 is the distance from the position of the vehicle 210 on the travel trajectory 953 to the end point 963 of the travel trajectory 953.

[0124] According to some embodiments, for each of the vehicle's current driving lane and the low-risk lanes, a longitudinal risk value of the lane is determined based on a target distance of a key obstacle corresponding to the lane and a planned length of a travel trajectory of the lane.

[0125] For example, for each lane, the longitudinal risk value of the lane can be determined according to the following formula (3):

[0126] longitudinal_cost=max(0.0,min(end_s-adc_s,30.0)-plan_end_s) (3)

[0127] Among them, longitudinal_cost is the longitudinal risk value, end_s is the s value of the obstacle intersection of the lane in the Frenet coordinate system corresponding to the lane, adc_s is the s value of the vehicle's position in the Frenet coordinate system corresponding to the lane, and plan_end_s is the planned length of the lane's travel trajectory.

[0128] According to some embodiments, step S704 includes: determining a difference between a longitudinal risk value of a current driving lane and a longitudinal risk value of a low-risk lane; and in response to the difference between the longitudinal risk value of the current driving lane and the longitudinal risk value of the low-risk lane being less than or equal to a longitudinal threshold, selecting a travel trajectory of the vehicle's current driving lane as the vehicle's travel trajectory, that is, the vehicle does not change lanes.

[0129] According to some embodiments, in response to the difference between the longitudinal risk value of the current driving lane and the longitudinal risk value of the low-risk lane being greater than the longitudinal threshold, the travel trajectory of the lane to be selected is continued to be determined based on other parameters (for example, the lateral risk value described in detail below) to ensure safety, or the travel trajectory corresponding to the low-risk lane can be directly selected to change lanes.

[0130] According to some embodiments, selecting the vehicle's travel trajectory from the vehicle's current driving lane and the travel trajectory of the low-risk lane based on the position information of one or more risk obstacles also includes: determining the lateral distance between the position of the key obstacle and the travel trajectory of the lane based on the distance between the position of the key obstacle and the entry point of the travel trajectory of the obstacle entering the lane, wherein the entry point of the travel trajectory of the obstacle entering the lane is the entry point of the travel trajectory of the obstacle entering the lane along the direction of the speed of the obstacle; and for each lane in the vehicle's current driving lane and the low-risk lane, determining the lateral risk value of the lane, including: determining the lateral risk value of the lane based on the lateral distance between the position of the key obstacle and the travel trajectory of the lane, wherein, at least partially based on the longitudinal risk values ​​of the vehicle's current driving lane and the low-risk lane, selecting the vehicle's travel trajectory from the vehicle's current driving lane and the travel trajectory of the low-risk lane includes: selecting the vehicle's travel trajectory from the vehicle's current driving lane and the travel trajectory of the low-risk lane based on the longitudinal risk value and the lateral risk value of the vehicle's current driving lane and the low-risk lane.

[0131] For example, in Fig. 9 In the illustrated scenario, for the current driving lane 242 of the vehicle 210, the entry point 964 of the travel trajectory of the lane may be a speed v of the detected obstacle 220. obs Predicted, that is, the velocity v of the obstacle 220 along the obstacle 220 from the position of the obstacle 220 obsThe straight line extending in the direction of the lane 242 intersects with the boundary of the side close to the obstacle 220 of the travel trajectory 952 of the current driving lane 242, and the lateral distance between the position of the obstacle 220 and the travel trajectory of the lane is the distance between the position of the obstacle 220 and the entry point 964 of the travel trajectory of the lane.

[0132] For another example, for the low-risk lane 243 of the vehicle 210, the obstacle intersection point 966 of the lane may be a speed v of the detected obstacle 220. obs Predicted, that is, the velocity v of the obstacle 220 along the obstacle 220 from the position of the obstacle 220 obs The lateral distance between the position of the obstacle 220 and the travel trajectory of the lane is the distance between the position of the obstacle 220 and the entry point 966 of the travel trajectory of the lane.

[0133] According to some embodiments, selecting the vehicle's traveling trajectory from the traveling trajectories of the vehicle's current driving lane and the low-risk lane based on the longitudinal risk values ​​and lateral risk values ​​of the vehicle's current driving lane and the low-risk lane includes: in response to a difference between the lateral risk value of the current driving lane and the lateral risk value of the low-risk lane being greater than a lateral threshold, and in response to a difference between the longitudinal risk value of the current driving lane and the longitudinal risk value of the low-risk lane being greater than a longitudinal threshold, selecting the traveling trajectory of the vehicle's low-risk lane as the traveling trajectory of the vehicle, and in response to a difference between the lateral risk value of the current driving lane and the lateral risk value of the low-risk lane being less than or equal to the lateral threshold, or in response to a difference between the longitudinal risk value of the current driving lane and the longitudinal risk value of the low-risk lane being less than or equal to the longitudinal threshold, selecting the traveling trajectory of the vehicle's current driving lane as the traveling trajectory of the vehicle.

[0134] In the embodiment as described in the present disclosure, by evaluating the travel trajectory of the lane to be selected from two dimensions of lateral risk and longitudinal risk, the safety of possible lane change operations (ie, switching to the travel trajectory of a low-risk lane) is ensured.

[0135] According to some embodiments, the lateral threshold is determined based on the difference between the s-value of the position of the critical obstacle in the first Frenet coordinate system and the s-value of the position of the vehicle in the first Frenet coordinate system, wherein the first Frenet coordinate system is a Frenet coordinate system defined based on the center line of the vehicle's current driving lane. For example, the greater the distance between the position of the critical obstacle and the position of the vehicle in the direction of the center line of the current driving lane, the larger the lateral threshold is set.

[0136] According to some embodiments, the method as disclosed herein further includes: in response to determining that the selected vehicle's traveling trajectory is a traveling trajectory of a low-risk lane, and in response to determining that the vehicle has not entered the low-risk lane, determining, for each lane of the vehicle's current driving lane and the low-risk lane, a longitudinal risk value and a lateral risk value of the lane; and determining whether to change the vehicle's traveling trajectory to the traveling trajectory of the current driving lane based on the longitudinal risk value and the lateral risk value of the vehicle's current driving lane and the low-risk lane.

[0137] According to an embodiment as described in the present disclosure, by continuing to evaluate the trajectory of the lane when a lane change operation is selected (i.e., determining that the selected vehicle's travel trajectory is the travel trajectory of a low-risk lane) but the lane has not yet been changed (i.e., the vehicle has not entered the low-risk lane), the safety and reliability of the lane change operation can be improved.

[0138] According to the embodiments of the present disclosure, Fig.10 As shown, a trajectory planning device 1000 for a vehicle is also disclosed, including: an obstacle determination unit 1010, configured to: obtain the speed and position of one or more risk obstacles around the vehicle; a lane selection unit 1020, configured to: select a side lane with a lower risk relative to one or more risk obstacles from adjacent lanes on both sides of the vehicle as a low-risk lane according to the speed and position of the one or more risk obstacles, wherein the adjacent lanes include a left lane located on the left side of the vehicle and a right lane located on the right side of the vehicle; a trajectory generation unit 1030, configured to: for each lane in the current driving lane and the low-risk lane of the vehicle, respectively generate a travel trajectory of the lane, wherein the travel trajectory of the current driving lane corresponds to a path that the vehicle will travel along the current driving lane, and the travel trajectory of the low-risk lane corresponds to a path that the vehicle will change lanes from the current driving lane to the low-risk lane, and travel along the low-risk lane after completing the lane change; and a trajectory selection unit 1040, configured to: select the vehicle's travel trajectory from the current driving lane and the travel trajectory of the low-risk lane of the vehicle according to the position of one or more risk obstacles.

[0139] According to some embodiments, the lane selection unit 1020 includes: an obstacle risk determination subunit, configured to: for each adjacent lane of the vehicle, determine the risk value of each of the one or more risk obstacles on the side where the adjacent lane is located, including: for each of the one or more risk obstacles on the side where the adjacent lane is located, determining the time when the obstacle enters the current driving lane of the vehicle based on the speed and position of the obstacle; and for each of the one or more risk obstacles on the side where the adjacent lane is located, determining the risk value of the obstacle based on the time when the obstacle enters the current driving lane of the vehicle; a lane risk determination subunit, configured to: for each adjacent lane, determine the risk value of the adjacent lane based on the risk value of the one or more risk obstacles on the side where the adjacent lane is located; and a lane selection subunit, configured to: determine a low-risk lane based on the risk value of each adjacent lane of the vehicle.

[0140] Here, the operations of the above-mentioned units 1010-1040 of the device 1000 are similar to the operations of the steps S310-S340 described above, and are not repeated here.

[0141] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0142] According to an embodiment of the present disclosure, an electronic device, a readable storage medium and a computer program product are also provided.

[0143] According to another aspect of the present disclosure, an edge computing device is also provided. Optionally, the edge computing device may include not only electronic devices but also communication components, and the electronic devices and the communication components may be integrated or separately provided. The electronic devices may obtain data from roadside sensing devices (such as roadside cameras), such as pictures and videos, to perform image and video processing and data calculation, and then transmit the processing and calculation results to the cloud control platform via the communication components.

[0144] Optionally, the edge computing device may also be a road side computing unit (RSCU). Optionally, the electronic device itself may also have the perception data acquisition function and communication function, such as an AI camera. The electronic device can directly perform image and video processing and data calculation based on the acquired perception data, and then transmit the processing and calculation results to the cloud control platform.

[0145] Optionally, the cloud control platform performs processing in the cloud to perform image and video processing and data calculation. The cloud control platform can also be called a vehicle-road collaborative management platform, V2X platform, cloud computing platform, central system, cloud server, etc.

[0146] refer to Fig.11 , a block diagram of an electronic device 1100 that can be used as a server or client of the present disclosure will now be described, 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, workbenches, 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 processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0147] like Fig.11 As shown, the electronic device 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1102 or a computer program loaded from a storage unit 1108 into a random access memory (RAM) 1103. In the RAM 1103, various programs and data required for the operation of the electronic device 1100 can also be stored. The computing unit 1101, the ROM 1102, and the RAM 1103 are connected to each other via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0148] Multiple components in the electronic device 1100 are connected to the I / O interface 1105, including: an input unit 1106, an output unit 1107, a storage unit 1108, and a communication unit 1109. The input unit 1106 can be any type of device that can input information to the electronic device 1100. The input unit 1106 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 1107 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1108 can include but is not limited to a disk and an optical disk. The communication unit 1109 allows the electronic device 1100 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device and / or the like.

[0149] The computing unit 1101 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1101 performs the various methods and processes described above, such as method 300. For example, in some embodiments, the method 300 may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1108. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1100 via the ROM 1102 and / or the communication unit 1109. When the computer program is loaded into the RAM 1103 and executed by the computing unit 1101, one or more steps of the method 300 described above may be performed. Alternatively, in other embodiments, the computing unit 1101 may be configured to execute the method 300 in any other appropriate manner (eg, by means of firmware).

[0150] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0151] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0152] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0153] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0154] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0155] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0156] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0157] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A trajectory planning method for a vehicle, comprising: Obtaining the speed and position of one or more risk obstacles around the vehicle; According to the speed and position of the one or more risk obstacles, select a lane on the side with a lower risk relative to the one or more risk obstacles from the adjacent lanes on both sides of the vehicle as a low-risk lane, wherein the adjacent lanes include a left lane located on the left side of the vehicle and a right lane located on the right side of the vehicle; For each of the current driving lane and the low-risk lane of the vehicle, respectively generate a travel trajectory of the lane, wherein the travel trajectory of the current driving lane corresponds to a path that the vehicle will travel along the current driving lane, and the travel trajectory of the low-risk lane corresponds to a path that the vehicle will change lanes from the current driving lane to the low-risk lane and travel along the low-risk lane after completing the lane change; and According to the positions of the one or more risk obstacles, a travel trajectory of the vehicle is selected from the current travel lane of the vehicle and the travel trajectories of a low-risk lane.

2. The method according to claim 1, wherein: The obtaining obstacle information of one or more risk obstacles around the vehicle includes: For each obstacle around the vehicle, in response to a distance between the position of the obstacle and an entry point of the obstacle into the current lane of travel of the vehicle being less than or equal to an interaction threshold, the obstacle is determined to be a risk obstacle, wherein the entry point of the obstacle into the current lane of travel of the vehicle is an entry point of the obstacle into the current lane of travel of the vehicle along the direction of the speed of the obstacle.

3. The method according to claim 1, wherein: The selecting, according to the speed and position of the one or more risk obstacles, a lane having a lower risk than the one or more risk obstacles from adjacent lanes on both sides of the vehicle as a low-risk lane comprises: For each adjacent lane of the vehicle, determining a risk value of each obstacle among the one or more risk obstacles on a side where the adjacent lane is located, comprises: For each obstacle among the one or more risk obstacles on the side where the adjacent lane is located, determining the time when the obstacle enters the current driving lane of the vehicle based on the speed and position of the obstacle; and For each obstacle among the one or more risk obstacles on the side where the adjacent lane is located, determining a risk value of the obstacle based on a time when the obstacle enters a current driving lane of the vehicle; For each adjacent lane, determining a risk value of the adjacent lane based on the risk value of an obstacle on the side where the adjacent lane is located among the one or more risk obstacles; and The low-risk lane is determined based on the risk value of each adjacent lane of the vehicle.

4. The method according to claim 3, wherein: For each obstacle in the one or more risk obstacles on the side where the adjacent lane is located, determining the risk value of the obstacle based on the time when the obstacle enters the current driving lane of the vehicle includes: For each obstacle among the one or more risk obstacles on the side where the adjacent lane is located, determining a first sub-risk value of the obstacle based on a time when the obstacle enters a current driving lane of the vehicle; determining a second sub-risk value of the obstacle based on a distance between the position of the obstacle and an entry point of the obstacle into the current lane of travel of the vehicle, wherein the entry point of the obstacle into the current lane of travel of the vehicle is an entry point of the obstacle into the current lane of travel of the vehicle along a direction of the speed of the obstacle; and A risk value of the obstacle is determined according to the first sub-risk value and the second sub-risk value of the obstacle.

5. The method according to any one of claims 1 to 4, wherein: The selecting the traveling trajectory of the vehicle from the traveling trajectory of the current driving lane of the vehicle and the low-risk lane according to the position information of the one or more risk obstacles comprises: For each of the one or more risk obstacles, determining the longitudinal distance of the risk obstacle based on the difference between the s value of the entry point of the risk obstacle into the current driving lane of the vehicle in the first Frenet coordinate system and the s value of the position of the vehicle in the first Frenet coordinate system, wherein the entry point of the risk obstacle into the current driving lane of the vehicle is the entry point of the risk obstacle into the current driving lane of the vehicle along the direction of the speed of the risk obstacle, and the first Frenet coordinate system is a Frenet coordinate system defined based on the center line of the current driving lane of the vehicle; setting the risk obstacle with the smallest longitudinal distance among the one or more risk obstacles as a key obstacle; For each lane of the current driving lane of the vehicle and the low-risk lane, determining a longitudinal risk value of the lane includes: Determining a target distance of the key obstacle corresponding to the lane based on a difference between an s value of the obstacle intersection point of the lane in the Frenet coordinate system corresponding to the lane and an s value of the position of the vehicle in the Frenet coordinate system corresponding to the lane, wherein the obstacle intersection point of the lane is an intersection point of the key obstacle traveling along a direction of the speed of the key obstacle with a center line of the lane, and the Frenet coordinate system corresponding to the lane is a Frenet coordinate system defined based on the center line of the lane; and determining a longitudinal risk value of the lane based on a target distance of the key obstacle corresponding to the lane and a planned length of the travel trajectory of the lane, wherein the planned length of the travel trajectory of the lane is a distance from the position of the vehicle to an end point of the travel trajectory of the lane on the travel trajectory of the lane, wherein when a braking point exists in the travel trajectory of the lane, the end point of the travel trajectory of the lane is the braking point in the travel trajectory of the lane, and when the braking point does not exist in the travel trajectory of the lane, the end point of the travel trajectory of the lane is the end point of the travel trajectory of the lane; and A travel trajectory of the vehicle is selected from travel trajectories of a current travel lane of the vehicle and a low-risk lane based at least in part on the longitudinal risk values ​​of the current travel lane of the vehicle and the low-risk lane.

6. The method according to claim 5, wherein: The selecting the vehicle's travel trajectory from the vehicle's current travel lane and the travel trajectory of the low-risk lane according to the position information of the one or more risk obstacles further comprises: For each lane in the current driving lane of the vehicle and the low-risk lane, determining a lateral risk value of the lane includes: determining a lateral distance between the position of the key obstacle and the trajectory of travel of the lane based on a distance between the position of the key obstacle and an entry point of the trajectory of travel of the obstacle into the lane, wherein the entry point of the trajectory of travel of the obstacle into the lane is an entry point of the trajectory of travel of the obstacle into the lane along a direction of the speed of the obstacle; and Determine the lateral risk value of the lane based on the lateral distance between the position of the key obstacle and the travel trajectory of the lane, Wherein, selecting the travel trajectory of the vehicle from the travel trajectories of the current travel lane of the vehicle and the low-risk lane based at least in part on the longitudinal risk values ​​of the current travel lane of the vehicle and the low-risk lane comprises: A travel trajectory of the vehicle is selected from travel trajectories of the current driving lane of the vehicle and the low-risk lane based on the longitudinal risk value and the lateral risk value of the current driving lane of the vehicle and the low-risk lane.

7. The method according to claim 6, wherein: The selecting the vehicle's travel trajectory from the vehicle's current travel lane and the low-risk lane's travel trajectory based on the longitudinal risk value and the lateral risk value of the vehicle's current travel lane and the low-risk lane comprises: In response to a difference between a lateral risk value of the current driving lane and a lateral risk value of the low-risk lane being greater than a lateral threshold, and in response to a difference between a longitudinal risk value of the current driving lane and a longitudinal risk value of the low-risk lane being greater than a longitudinal threshold, selecting a travel trajectory of the low-risk lane of the vehicle as the travel trajectory of the vehicle, and In response to a difference between a lateral risk value of the current driving lane and a lateral risk value of the low-risk lane being less than or equal to the lateral threshold, or in response to a difference between a longitudinal risk value of the current driving lane and a longitudinal risk value of the low-risk lane being less than or equal to the longitudinal threshold, selecting a travel trajectory of the current driving lane of the vehicle as the travel trajectory of the vehicle.

8. The method according to claim 7, wherein: The lateral threshold is determined based on a difference between an s-value of a position of the key obstacle in a first Frenet coordinate system and an s-value of a position of the vehicle in the first Frenet coordinate system, wherein the first Frenet coordinate system is a Frenet coordinate system defined based on a centerline of a current driving lane of the vehicle.

9. The method according to claim 6, further comprising: In response to determining that the selected travel trajectory of the vehicle is the travel trajectory of the low-risk lane, and in response to determining that the vehicle has not entered the low-risk lane, for each lane of the current travel lane of the vehicle and the low-risk lane, determining a longitudinal risk value and a lateral risk value of the lane; as well as Based on the longitudinal risk value and the lateral risk value of the current driving lane and the low-risk lane of the vehicle, it is determined whether to change the traveling trajectory of the vehicle to the traveling trajectory of the current driving lane.

10. A trajectory planning device for a vehicle, comprising: An obstacle determination unit is configured to: obtain the speed and position of one or more risk obstacles around the vehicle; A lane selection unit is configured to: select, according to the speed and position of the one or more risk obstacles, a lane on one side of the adjacent lanes on both sides of the vehicle that has a lower risk than the one or more risk obstacles as a low-risk lane, wherein the adjacent lanes include a left lane on the left side of the vehicle and a right lane on the right side of the vehicle; a trajectory generating unit configured to: for each of the current driving lane and the low-risk lane of the vehicle, generate a travel trajectory of the lane respectively, wherein the travel trajectory of the current driving lane corresponds to a path that the vehicle will travel along the current driving lane, and the travel trajectory of the low-risk lane corresponds to a path that the vehicle will change lanes from the current driving lane to the low-risk lane and travel along the low-risk lane after completing the lane change; and The trajectory selection unit is configured to select a travel trajectory of the vehicle from the travel trajectories of the current driving lane of the vehicle and the low-risk lane according to the positions of the one or more risk obstacles.

11. The device according to claim 10, wherein: The lane selection unit comprises: The obstacle risk determination subunit is configured as follows: For each adjacent lane of the vehicle, determining a risk value of each obstacle among the one or more risk obstacles on a side where the adjacent lane is located, comprises: For each obstacle among the one or more risk obstacles on the side where the adjacent lane is located, determining the time when the obstacle enters the current driving lane of the vehicle based on the speed and position of the obstacle; and For each obstacle among the one or more risk obstacles on the side where the adjacent lane is located, determining a risk value of the obstacle based on a time when the obstacle enters a current driving lane of the vehicle; a lane risk determination subunit configured to: for each adjacent lane, determine a risk value of the adjacent lane based on a risk value of an obstacle on a side of the adjacent lane among the one or more risk obstacles; and The lane selection subunit is configured to determine the low-risk lane based on the risk value of each adjacent lane of the vehicle.

12. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 9.

13. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-9.

14. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

15. An autonomous driving vehicle comprising the electronic device according to claim 12.

16. An edge computing device, comprising the electronic device according to claim 12.

Images