Control method, device, equipment, vehicle and medium for autonomous driving vehicle

Abstract

The present disclosure provides a control method, device, electronic device, autonomous driving vehicle, storage medium and program product for an autonomous driving vehicle, and relates to the field of artificial intelligence technology, and in particular to technical fields such as autonomous driving, intelligent transportation, high-precision maps, cloud services, and vehicle networking. A specific implementation scheme is as follows: in response to receiving an obstacle avoidance instruction, determining a lane change obstacle avoidance strategy based on first obstacle data of a target obstacle, the obstacle avoidance instruction being generated in response to determining that a first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition, the first relationship between the target obstacle and the vehicle being generated based on the first obstacle data of the target obstacle and the first vehicle data of the vehicle; controlling the vehicle to travel according to the lane change obstacle avoidance strategy; and in response to receiving a risk elimination instruction, updating a lane change planning path so that the vehicle travels according to the updated lane change planning path.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, particularly to autonomous driving, intelligent transportation, high-precision mapping, cloud services, and the Internet of Vehicles. Specifically, it relates to control methods, devices, electronic devices, autonomous driving vehicles, storage media, and program products. Background Art

[0002] Vehicles operating in autonomous driving mode can free occupants, particularly the driver, from some driving-related responsibilities. When operating in autonomous driving mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or, in some cases, without any passengers.

[0003] Lane changes typically involve changing lanes to the corresponding turning lane in response to a turn command, or in response to a command to bypass road construction. However, complex lane change scenarios can involve many dynamic obstacles suddenly moving, making it impossible for the vehicle to safely change lanes according to the planned path. Summary of the Invention

[0004] The present disclosure provides a control method and device for an autonomous driving vehicle, an electronic device, an autonomous driving vehicle, a storage medium, and a program product.

[0005] According to one aspect of the present disclosure, a control method for an autonomous driving vehicle is provided, comprising: in response to receiving an obstacle avoidance instruction, determining a lane change obstacle avoidance strategy based on first obstacle data of a target obstacle, wherein the obstacle avoidance instruction is generated in response to determining that a first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition, and the first relationship between the target obstacle and the vehicle is generated based on the first obstacle data of the target obstacle and the first vehicle data of the vehicle; controlling the vehicle to travel according to the lane change obstacle avoidance strategy; and in response to receiving a risk relief instruction, updating the lane change planning path so that the vehicle travels according to the updated lane change planning path, wherein the risk relief instruction is generated in response to determining that a second relationship between the target obstacle and the vehicle does not satisfy the predetermined collision condition, and the second relationship between the target obstacle and the vehicle is generated based on second obstacle data of the target obstacle and the second vehicle data of the vehicle.

[0006] According to another aspect of the present disclosure, a control device for an autonomous driving vehicle is provided, comprising: a strategy determination module for determining, in response to receiving an obstacle avoidance instruction, a lane change obstacle avoidance strategy based on first obstacle data of a target obstacle, wherein the obstacle avoidance instruction is generated in response to determining that a first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition, and the first relationship between the target obstacle and the vehicle is generated based on the first obstacle data of the target obstacle and the first vehicle data of the vehicle; a control module for controlling the vehicle to travel according to the lane change obstacle avoidance strategy; and an update module for updating, in response to receiving a risk relief instruction, the lane change planning path so that the vehicle travels according to the updated lane change planning path, wherein the risk relief instruction is generated in response to determining that a second relationship between the target obstacle and the vehicle does not satisfy the predetermined collision condition, and the second relationship between the target obstacle and the vehicle is generated based on second obstacle data of the target obstacle and the second vehicle data of the vehicle.

[0007] 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; wherein 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 perform a method as disclosed herein.

[0008] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method of the present disclosure.

[0009] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method of the present disclosure when executed by a processor.

[0010] According to another aspect of the present disclosure, an autonomous driving vehicle is provided, comprising the electronic device of the present disclosure.

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

[0012] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0013] Figure 1 Schematically illustrates an exemplary system architecture to which the control method and apparatus for an autonomous driving vehicle according to an embodiment of the present disclosure may be applied;

[0014] Figure 2 A diagram schematically illustrates an application scenario of a method for controlling an autonomous driving vehicle according to an embodiment of the present disclosure;

[0015] Figure 3 A flowchart schematically illustrates a method for controlling an autonomous driving vehicle according to an embodiment of the present disclosure;

[0016] Figure 4 A schematic diagram schematically illustrates a method for controlling an autonomous driving vehicle according to an embodiment of the present disclosure;

[0017] Figure 5 A flowchart schematically illustrates a method for controlling an autonomous driving vehicle according to another embodiment of the present disclosure;

[0018] Figure 6 A block diagram schematically illustrates a control device for an autonomous driving vehicle according to an embodiment of the present disclosure; and

[0019] Figure 7 A block diagram of an electronic device suitable for implementing a control method for an autonomous driving vehicle according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0020] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] The present disclosure provides a control method and device for an autonomous driving vehicle, an electronic device, an autonomous driving vehicle, a storage medium, and a program product.

[0022] According to an embodiment of the present disclosure, a control method for an autonomous driving vehicle is provided, comprising: in response to receiving an obstacle avoidance instruction, determining a lane change obstacle avoidance strategy based on first obstacle data of a target obstacle, wherein the obstacle avoidance instruction is generated in response to determining that a first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition, and the first relationship between the target obstacle and the vehicle is generated based on the first obstacle data of the target obstacle and the first vehicle data of the vehicle; controlling the vehicle to travel according to the lane change obstacle avoidance strategy; and in response to receiving a risk relief instruction, updating a lane change planning path so that the vehicle travels according to the updated lane change planning path, wherein the risk relief instruction is generated in response to determining that a second relationship between the target obstacle and the vehicle does not satisfy the predetermined collision condition, and the second relationship between the target obstacle and the vehicle is generated based on second obstacle data of the target obstacle and the second vehicle data of the vehicle.

[0023] In the technical solution disclosed herein, the collection, storage, use, processing, transmission, provision, disclosure and application of user personal information involved comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0024] In the technical solution disclosed herein, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0025] Figure 1 An exemplary system architecture to which the control method and apparatus for an autonomous driving vehicle according to an embodiment of the present disclosure can be applied is schematically illustrated.

[0026] It should be noted that Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios. For example, in another embodiment, an exemplary system architecture to which the control method and apparatus for an autonomous vehicle may be applied may include an onboard terminal of the autonomous vehicle, but the onboard terminal may implement the control method and apparatus for an autonomous vehicle provided by the embodiments of the present disclosure without interacting with a server.

[0027] like Figure 1As shown, the system architecture 100 according to this embodiment may include an autonomous driving vehicle 101, a network 102, and a server 103. The autonomous driving vehicle 101 may be communicatively connected to one or more servers 103 via the network 102. The network 102 may be any type of network, such as a wired or wireless local area network (LAN), a wide area network (WAN) such as the Internet, a cellular network, a satellite network, or a combination thereof. The server 103 may be any type of server or server cluster, such as a network or cloud server, an application server, a back-end server, or a combination thereof. The server may be a data analysis server, a content server, a traffic information server, a map and point of interest (MPOI) server, or a location server, etc.

[0028] The autonomous vehicle 101 may refer to a vehicle configured to operate in an autonomous driving mode, but is not limited thereto. The autonomous vehicle may also operate in a manual mode, a fully autonomous driving mode, or a partially autonomous driving mode.

[0029] Autonomous vehicle 101 may include an onboard terminal, a vehicle control module, a wireless communication module, a user interface module, and a sensor module. Autonomous vehicle 101 may also include common components found in conventional vehicles, such as an engine, wheels, a steering wheel, and a transmission. These common components can be controlled by the onboard terminal and the vehicle control module using various communication commands, such as acceleration, deceleration, steering, and braking commands.

[0030] The various modules in the autonomous vehicle 101 can be communicatively coupled to each other via an interconnect, a bus, a network, or a combination thereof. For example, they can be communicatively coupled to each other via a controller area network (CAN) bus. The CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host computer.

[0031] The sensing module may include, but is not limited to, one or more cameras, a global positioning system (GPS) unit, an inertial measurement unit (IMU), a radar unit, and a light detection and ranging (LIDAR) unit. The GPS unit may include a transceiver operable to provide information about the position of the autonomous vehicle. The IMU unit may sense the position and orientation changes of the autonomous vehicle based on inertial acceleration. The radar unit may represent a system that uses radio signals to sense obstacles in the autonomous vehicle's surroundings. In addition to sensing obstacles, the radar unit may also sense the speed and / or heading of the obstacles. The LIDAR unit may use lasers to sense obstacles in the autonomous vehicle's environment. The LIDAR unit may include, among other components, one or more laser sources, a laser scanner, and one or more detectors. The camera may include one or more devices for capturing images of the autonomous vehicle's surroundings. The camera may be a still camera and / or a video camera. The camera may be mechanically movable, for example, by mounting the camera on a rotating or tilting platform.

[0032] The sensing module may also include other sensors, such as a sonar sensor, an infrared sensor, a steering sensor, a throttle sensor, a brake sensor, and an audio sensor (e.g., a microphone). The audio sensor may be configured to collect sound from the environment surrounding the autonomous vehicle. The steering sensor may be configured to sense the steering angle of the steering wheel, the wheels of the autonomous vehicle, or a combination thereof. The throttle sensor and the brake sensor respectively sense the throttle position and brake position of the autonomous vehicle. In some cases, the throttle sensor and the brake sensor may be integrated into an integrated throttle / brake sensor.

[0033] The vehicle control module may include, but is not limited to, a steering unit, a throttle unit (also known as an acceleration unit), and a brake unit. The steering unit is used to adjust the direction or forward direction of the autonomous vehicle. The throttle unit is used to control the speed of the motor or engine, thereby controlling the speed and acceleration of the autonomous vehicle. The brake unit decelerates the autonomous vehicle by providing friction to slow down the wheels or tires of the autonomous vehicle.

[0034] The wireless communication module allows communication between the autonomous vehicle and external modules, such as devices, sensors, other vehicles, etc. For example, the wireless communication module can communicate directly with one or more devices wirelessly, or wirelessly communicate via a communication network, such as communicating with a server via the network. The wireless communication module can use any cellular communication network or wireless local area network (WLAN), such as WiFi, to communicate with another component or module. The user interface module can be part of the peripheral devices implemented in the autonomous vehicle, including, for example, a keyboard, a touch screen display device, a microphone, and a speaker.

[0035] Some or all of the functions of the autonomous vehicle 101 may be controlled or managed by an onboard terminal, particularly when operating in autonomous driving mode. The onboard terminal includes the necessary hardware (e.g., processor, memory, storage device) and software (e.g., operating system, planning and routing programs) to receive information from the sensing module, control module, wireless communication module, and / or user interface module, process the received information, and generate instructions for controlling the autonomous vehicle. Alternatively, the onboard terminal may be integrated with the control module.

[0036] For example, a passenger may specify a trip's starting location and destination, for example, via a user interface module. The vehicle terminal then obtains trip-related data. For example, the vehicle terminal may obtain the location and drivable route from an MPOI server, which may be part of the server. The location server provides location services, and the MPOI server provides map services. Alternatively, such locations and maps may be cached locally in the vehicle terminal's permanent storage device.

[0037] As the autonomous vehicle moves along a drivable route, the vehicle terminal can also obtain real-time traffic information from a traffic information system or server. The server can be operated by a third-party entity. The server's functionality can be integrated with the vehicle terminal. Based on real-time traffic information, location information, and real-time local environmental data detected or sensed by the sensor module, the vehicle terminal can plan an optimal route and control the autonomous vehicle, for example, via the control module, based on the planned optimal route to reach the designated destination safely and efficiently.

[0038] It should be understood that Figure 1 The number of autonomous driving vehicles, networks, and servers shown in the figure is merely illustrative. Any number of autonomous driving vehicles, networks, and servers may be used depending on the implementation requirements.

[0039] It should be noted that the sequence numbers of the operations in the following method are only used to indicate the operation for the purpose of description, and should not be regarded as indicating the order in which the operations should be performed. Unless explicitly stated, the method does not need to be performed in the order shown.

[0040] Figure 2 The following schematically shows an application scenario diagram of the control method for an autonomous driving vehicle according to an embodiment of the present disclosure.

[0041] like Figure 2As shown, the vehicle (i.e., the autonomous driving vehicle, hereinafter referred to as the vehicle) ADC201 is operating in the autonomous driving mode, and is controlled by the on-board terminal to change lanes from the straight lane to the left lane. The lane change planning path of the vehicle ADC201 is set to: from the driving position of the straight lane to the lane change entrance between the first obstacle OBS201 and the second obstacle OBS202 driving in the left lane. In the process of the vehicle ADC201 driving according to the lane change planning path, the second obstacle OBS202 driving on the left lane suddenly accelerates. If the vehicle ADC201 continues to drive according to the lane change planning path, there will be a risk of collision. In this case, the on-board terminal can generate a lane change obstacle avoidance strategy based on the obstacle data of the second obstacle OBS202, such as the driving path and driving speed of the second obstacle OBS202. The vehicle ADC201 is controlled to drive according to the lane change obstacle avoidance strategy so that during the lane change process, the second obstacle OBS202 can be safely avoided while completing the lane change task.

[0042] Figure 3 A flowchart of a method for controlling an autonomous driving vehicle according to an embodiment of the present disclosure is schematically shown.

[0043] like Figure 3 As shown, the method includes operations S310 to S330.

[0044] In operation S310, in response to receiving an obstacle avoidance instruction, a lane change obstacle avoidance strategy is determined based on first obstacle data of a target obstacle. The obstacle avoidance instruction is generated in response to determining that a first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition, the first relationship between the target obstacle and the vehicle being generated based on the first obstacle data of the target obstacle and first vehicle data of the vehicle.

[0045] In operation S320 , the vehicle is controlled to travel according to the lane change and obstacle avoidance strategy.

[0046] In operation S330, in response to receiving the risk elimination instruction, the lane change planned path is updated so that the vehicle travels along the updated lane change planned path. The risk elimination instruction is generated in response to determining that a second relationship between the target obstacle and the vehicle does not satisfy a predetermined collision condition, the second relationship between the target obstacle and the vehicle being generated based on second obstacle data of the target obstacle and second vehicle data of the vehicle.

[0047] According to an embodiment of the present disclosure, an obstacle avoidance instruction may be generated in response to determining that the first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition. The obstacle avoidance instruction may be an obstacle avoidance instruction triggered by the driver based on the surrounding environment information, but is not limited thereto. It may also be an obstacle avoidance instruction automatically triggered by the server or the vehicle-mounted terminal based on the surrounding environment information. The first relationship between the target obstacle and the vehicle may include at least one of the following: a relative distance relationship between the target obstacle and the vehicle, a relative speed relationship between the target obstacle and the vehicle. The first relationship between the target obstacle and the vehicle satisfies the predetermined collision condition, which may mean that there is a risk of collision between the vehicle and the target obstacle.

[0048] According to an embodiment of the present disclosure, a lane change obstacle avoidance strategy may involve changing (e.g., fine-tuning) the vehicle's driving state while maintaining a lane change. Changing the vehicle's driving state may include, for example, changing the vehicle's longitudinal acceleration, changing the vehicle's lateral acceleration, changing the vehicle's yaw angle, and changing the vehicle's heading angle.

[0049] According to other embodiments of the present disclosure, the following operations may also be performed: for example, in response to receiving an obstacle avoidance instruction, a lane change interruption strategy or a lane change termination strategy is determined based on the first obstacle data of the target obstacle. The lane change interruption strategy may refer to: a strategy for interrupting lane change. For example, an instruction to control the vehicle to return to the center urgently. Specifically, it may refer to replanning a new path based on the current position of the vehicle in response to receiving an obstacle avoidance instruction, driving along the new path, and terminating the lane change (not returning to the initial lane). The target vehicle lane change termination strategy may refer to: a strategy for canceling the lane change. For example, a strategy to control the vehicle to return to the initial lane.

[0050] According to embodiments of the present disclosure, in response to receiving a risk resolution instruction, the vehicle terminal or server can control the vehicle to follow the target lane change plan path. Compared to controlling the vehicle according to a lane change interruption strategy or a lane change termination strategy, controlling the vehicle according to a lane change obstacle avoidance strategy can quickly execute the lane change task when the risk is resolved, thereby improving the flexibility and intelligence of lane changes.

[0051] Figure 4 A schematic diagram of a control method for an autonomous driving vehicle according to an embodiment of the present disclosure is schematically shown.

[0052] like Figure 4As shown in the first scenario, in response to receiving a lane change instruction, the onboard terminal on vehicle ADC401 can obtain lane change scenario data about the surrounding area of vehicle ADC401 from the sensor module. Based on the lane change scenario data, the vehicle determines the lane change entrance, such as the entrance between the first obstacle OBS401 and the second obstacle OBS402. Based on the driving position and the lane change entrance, vehicle ADC401 generates a planned lane change path.

[0053] like Figure 4 As shown in the second scenario, while the onboard terminal controls the vehicle ADC401 to change lanes according to the planned lane change path, the onboard terminal can monitor obstacle data related to the lane change in the surrounding environment through the sensor module installed on the vehicle ADC401. Based on the obstacle data of multiple obstacles, a target obstacle is determined from the multiple obstacles, for example, the second obstacle OBS402 is used as the target obstacle. The target obstacle may be an obstacle with which the vehicle will have a collision risk if it continues to travel along the planned lane change path.

[0054] According to an embodiment of the present disclosure, the vehicle-mounted terminal may determine a first relationship between the target obstacle and the vehicle based on acquired first obstacle data of the target obstacle, first vehicle data of the vehicle, etc. In response to determining that the first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition, the vehicle-mounted terminal or the server generates an obstacle avoidance instruction.

[0055] like Figure 4 As shown in the third scenario in , the vehicle-mounted terminal can determine a lane change obstacle avoidance strategy based on the first obstacle data of the target obstacle in response to receiving the obstacle avoidance instruction. Control the vehicle ADC401 to drive according to the lane change obstacle avoidance strategy. For example, determine the lateral acceleration and longitudinal acceleration according to the lane change obstacle avoidance strategy. Control the vehicle ADC401 to drive according to the lateral acceleration and longitudinal acceleration. For example, according to the lane change obstacle avoidance strategy, determine the lateral driving degree and longitudinal acceleration of the vehicle ADC401 to avoid the second obstacle OBS402, for example, accelerate laterally in a direction away from the second obstacle OBS402, and decelerate longitudinally.

[0056] like Figure 4As shown in the fourth scenario, when driving according to the lane change obstacle avoidance strategy, the vehicle uses the sensing module to collect the second obstacle data about the target obstacle, such as the second obstacle OBS402, in real time. The on-board terminal receives the second obstacle data about the target obstacle, and determines the second relationship between the target obstacle and the vehicle ADC401 based on the second obstacle data of the target obstacle and the second vehicle data of the vehicle. In response to determining that the second relationship between the target obstacle and the vehicle does not meet the predetermined collision condition, the on-board terminal generates a risk relief instruction. In response to receiving the risk relief instruction, the on-board terminal changes from the lane change avoidance stage to the lane change planning path update stage, that is, updates the lane change planning path to generate an updated lane change planning path. The on-board terminal can control the vehicle to drive according to the updated lane change planning path, and finally complete the lane change task.

[0057] According to an embodiment of the present disclosure, the vehicle-mounted terminal or server determines, based on the second obstacle data of the target obstacle and the second vehicle data of the vehicle, that the second relationship between the target obstacle and the vehicle does not satisfy a predetermined collision condition. This second relationship between the target obstacle and the vehicle not satisfying the predetermined collision condition may mean that there is no risk of collision between the vehicle and the target obstacle. For example, the target obstacle has already passed the vehicle. The vehicle is controlled to increase the distance between the vehicle and the target obstacle at a predetermined speed and direction.

[0058] By utilizing the control method for controlling an autonomous driving vehicle provided by the embodiments of the present disclosure, it is possible to avoid the waiting operation for starting a lane change due to the adoption of an interruption or termination of a lane change strategy, thereby reducing the lane change time, improving the lane change efficiency, and improving the safety and continuity of the autonomous driving vehicle during the lane change process.

[0059] According to an embodiment of the present disclosure, in response to receiving a risk elimination instruction, current lane change scenario data may be determined. Based on the current lane change scenario data, a lane change planning path may be updated so that the vehicle terminal controls the vehicle to travel according to the updated lane change planning path.

[0060] According to an embodiment of the present disclosure, the current lane change scenario data may include at least one of the following: data of obstacles related to lane change, vehicle data, environment data, and road traffic regulation data.

[0061] According to an embodiment of the present disclosure, the data of an obstacle related to lane change may include state data and attribute data such as the size of the obstacle, the speed of the obstacle, the direction of the obstacle, and the acceleration of the obstacle.

[0062] According to an embodiment of the present disclosure, vehicle data may include status data and attribute data such as the size of the vehicle, the driving speed of the vehicle, the driving direction of the vehicle, and the driving degree of the vehicle.

[0063] According to an embodiment of the present disclosure, environmental data may include objective driving environment data such as weather, visibility, road muddiness, road congestion, and road construction conditions.

[0064] According to an embodiment of the present disclosure, the road traffic rule data may include subjective driving rule data such as speed limit rules, no-reversing rules, and no-lane-changing-across-solid-line rules.

[0065] According to an embodiment of the present disclosure, the lane change planning path is updated based on the current lane change scenario data, so that the updated lane change planning path is safer and more effective.

[0066] Figure 5 A flowchart of a method for controlling an autonomous driving vehicle according to another embodiment of the present disclosure is schematically shown.

[0067] like Figure 5 As shown, operations S510 to S580 are included.

[0068] In operation S510, a determination is made as to whether a first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition based on first obstacle data of the target obstacle and first vehicle data of the vehicle. In response to determining that the first relationship between the target obstacle and the vehicle satisfies the predetermined collision condition, operation S520 is executed. In response to determining that a second relationship between the target obstacle and the vehicle does not satisfy the predetermined collision condition, operation S530 is executed.

[0069] In operation S520, in response to receiving the obstacle avoidance instruction, a lane change obstacle avoidance strategy is determined based on the first obstacle data of the target obstacle, and the vehicle is controlled to travel according to the lane change obstacle avoidance strategy.

[0070] In operation S530 , the vehicle is controlled to continue traveling along the predetermined lane change planned path.

[0071] In operation S540, it is determined whether the number of jump instructions received within a predetermined period is greater than or equal to a predetermined jump threshold. If the number of jump instructions is greater than or equal to the predetermined jump threshold, S550 is executed; if the number of jump instructions is less than the predetermined jump threshold, S560 is executed.

[0072] In operation S550 , the vehicle is controlled to return to the initial lane and the lane change is canceled.

[0073] In operation S560, while controlling the vehicle to travel according to the lane change obstacle avoidance strategy, it is determined whether the second relationship between the target obstacle and the vehicle satisfies a predetermined collision condition. In response to determining that the second relationship between the target obstacle and the vehicle does not satisfy the predetermined collision condition, operation S570 is executed. In response to determining that the second relationship between the target obstacle and the vehicle satisfies the predetermined collision condition, operation S580 is executed.

[0074] In operation S570 , in response to receiving the risk elimination instruction, the lane change planning path is updated, and the vehicle is controlled to travel according to the updated lane change planning path.

[0075] In operation S580 , the second obstacle data of the target obstacle is used as the first obstacle data.

[0076] According to an embodiment of the present disclosure, the jump instruction includes at least one of the following: an obstacle avoidance instruction and a risk elimination instruction. The number of jump instructions may include a first number of obstacle avoidance instructions received, a second number of risk elimination instructions received, or the sum of the first number of obstacle avoidance instructions received and the second number of risk elimination instructions received.

[0077] According to an embodiment of the present disclosure, during a lane change, the vehicle can follow an obstacle avoidance instruction and drive according to a lane change obstacle avoidance strategy. Upon receiving a risk resolution instruction, the vehicle can continue changing lanes. The obstacle avoidance, risk resolution, and lane change operations are repeated until the lane change is completed or canceled.

[0078] According to an embodiment of the present disclosure, the predetermined jump threshold may refer to the maximum number of jump instructions received. By setting a predetermined jump threshold, the number of jump instructions received can be controlled, thereby reasonably controlling the duration of the vehicle's lane change. For example, when the number of jump instructions is greater than or equal to the predetermined jump threshold, the lane change is canceled. In the actual lane change process, the higher the number of jump instructions received, the more complex the lane change scenario is or the more variable factors there are in the lane change scenario. In this process, forcibly executing the lane change operation is dangerous, and the risk of collision is high. By setting a predetermined jump threshold, the lane change is canceled when it is determined that the number of jump instructions is greater than or equal to the predetermined jump threshold, thereby improving the intelligence, flexibility and safety of the lane change, and avoiding traffic accidents caused by forced lane changes when the lane change scenario is complex or there are many sudden changes.

[0079] According to an embodiment of the present disclosure, determining the second relationship between the target obstacle and the vehicle based on the second obstacle data of the target obstacle and the second vehicle data of the vehicle may include: determining the current lane change category result based on the current lane change scene data. Determining the interaction data category based on the current lane change category result. Determining target obstacle data that matches the interaction data category from the second obstacle data. Determining target vehicle data that matches the interaction data category from the second vehicle data. Determining the second relationship between the target obstacle and the vehicle based on the target obstacle data of the target obstacle and the target vehicle data of the vehicle. According to an embodiment of the present disclosure, determining the current lane change category result based on the current lane change scene data may include: determining target template lane change scene data that matches the current lane change scene data from multiple template lane change scene data. Determining the current lane change category result that matches the target template lane change scene data based on the mapping relationship between the template lane change scene data and the lane change category result.

[0080] According to an embodiment of the present disclosure, a lane change classification result may refer to: a scene classification result of a traffic scene formed between a vehicle and surrounding obstacles during a vehicle lane change. For example, a lane change classification result in which a vehicle and a parallel obstacle change lanes simultaneously, a lane change classification result in which an obstacle behind the vehicle and the vehicle change lanes simultaneously, and a lane change classification result in which an obstacle suddenly accelerates. However, the present invention is not limited thereto. A lane change classification result may also refer to: a classification result of the relative positional relationship between obstacles surrounding the vehicle and the vehicle during a vehicle lane change. For example, a classification result in which an obstacle is located in one or more of the following directions: the front, rear, left, or right of the vehicle.

[0081] According to an embodiment of the present disclosure, multiple template lane change scene data of different lane change category results can be obtained by collecting actual lane change scene data or through simulation. Taking the lane change category result representing the relative position relationship between the vehicle and the obstacle as an example, a mapping relationship between the lane change category result and the template lane change scene data can be constructed based on the data of the relative position relationship between the vehicle and the obstacle in the template lane change scene data. Target template lane change scene data that matches the current lane change scene data is determined from the multiple template lane change scene data. Based on the mapping relationship between the template lane change scene data and the lane change category result, the current lane change category result is determined.

[0082] According to embodiments of the present disclosure, the similarity between multiple lane change scene data templates and the current lane change scene data can be determined, resulting in multiple similarities. The template with the highest similarity is then used as the target lane change scene data template. The method for determining the similarity is not limited. For example, the template lane change scene data and the current lane change scene data can be input into a trained cross-encoder or bi-encoder to obtain a similarity output result.

[0083] According to embodiments of the present disclosure, a mapping relationship can be established between lane change classification results and predetermined interaction data categories. Based on this mapping relationship, the interaction data category that matches the current lane change classification result can be determined. Predetermined interaction data categories may include interaction-related driving data categories such as driving trajectory, driving speed, and driving direction.

[0084] According to an embodiment of the present disclosure, once an interaction data category matching the current lane change category has been determined, target obstacle data matching the interaction data category can be determined from the second obstacle data. Target vehicle data matching the interaction data category can also be determined from the second vehicle data. Based on the target obstacle data of the target obstacle and the target vehicle data of the vehicle, a second relationship between the target obstacle and the vehicle can be determined.

[0085] For example, if the current lane change classification result is determined to represent a lane change classification result in which a vehicle changes lanes simultaneously with a parallel obstacle, the interaction data categories that match the current lane change classification result may include interaction-related driving data categories such as a driving trajectory category and a driving speed category. Target obstacle data that matches the interaction data category may be determined from the second obstacle data, such as the target obstacle's driving trajectory data and the target obstacle's driving speed data. Target vehicle data that matches the interaction data category may also be determined from the second vehicle data, such as the vehicle's driving trajectory data and the vehicle's driving speed data. Based on the target obstacle data and the vehicle's target data, a second relationship between the target obstacle and the vehicle may be determined, such as whether the second relationship satisfies a predetermined collision condition.

[0086] According to an embodiment of the present disclosure, based on the current lane change scene data, the current lane change category result is determined. Based on the current lane change category result, the interaction data category is determined. A preliminary understanding of the vehicle's lane change scenario is achieved. Target obstacle data that matches the interaction data category is determined from the second obstacle data. Target vehicle data that matches the interaction data category is determined from the second vehicle data. Based on the target obstacle data of the target obstacle and the target vehicle data of the vehicle, a second relationship between the target obstacle and the vehicle is determined. Target obstacle data is filtered from the second obstacle data, and target vehicle data is filtered from the second vehicle data, thereby achieving an accurate understanding of the lane change scenario. Thus, complex and changeable lane change scenarios can be analyzed simply and accurately by pre-dividing the lane change category results and predetermined interaction data categories.

[0087] Figure 6A block diagram of a control device for an autonomous driving vehicle according to an embodiment of the present disclosure is schematically shown.

[0088] like Figure 6 As shown, the control device 600 of the autonomous driving vehicle includes: a strategy determination module 610, a control module 620, and an update module 630.

[0089] A strategy determination module 610 is configured to determine, in response to receiving an obstacle avoidance instruction, a lane change obstacle avoidance strategy based on first obstacle data of a target obstacle, wherein the obstacle avoidance instruction is generated in response to determining that a first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition, and the first relationship between the target obstacle and the vehicle is generated based on the first obstacle data of the target obstacle and the first vehicle data of the vehicle.

[0090] The control module 620 is used to control the vehicle to drive according to the lane change and obstacle avoidance strategy.

[0091] An updating module 630 is configured to update a lane change planning path in response to receiving a risk resolution instruction so that the vehicle travels along the updated lane change planning path, wherein the risk resolution instruction is generated in response to determining that a second relationship between the target obstacle and the vehicle does not satisfy a predetermined collision condition, and the second relationship between the target obstacle and the vehicle is generated based on second obstacle data of the target obstacle and second vehicle data of the vehicle.

[0092] According to an embodiment of the present disclosure, the update module includes: a first data determination unit and an update unit.

[0093] The first data determining unit is configured to determine current lane change scenario data in response to receiving the risk elimination instruction. The current lane change scenario data includes at least one of the following: obstacle data related to lane change, vehicle data, environment data, and road traffic rule data.

[0094] The updating unit is used to update the lane change planning path based on the current lane change scenario data.

[0095] According to an embodiment of the present disclosure, the control device of the autonomous driving vehicle further includes, before the update module: a second acquisition module, a second relationship determination module, and a second generation module.

[0096] The second acquisition module is used to acquire second obstacle data of the target obstacle and second vehicle data of the vehicle.

[0097] The second relationship determination module is configured to determine a second relationship between the target obstacle and the vehicle based on second obstacle data of the target obstacle and second vehicle data of the vehicle.

[0098] The second generating module is configured to generate a risk elimination instruction in response to determining that the second relationship between the target obstacle and the vehicle does not satisfy a predetermined collision condition.

[0099] According to an embodiment of the present disclosure, the second relationship determination module includes: a result determination unit, a category determination unit, a second data determination unit, a third data determination unit, and a relationship determination unit.

[0100] The result determination unit is used to determine the current lane change category result based on the current lane change scenario data.

[0101] The category determination unit is used to determine the category of the interaction data based on the current lane change category result.

[0102] The second data determining unit is configured to determine target obstacle data matching the interaction data category from the second obstacle data.

[0103] The third data determination unit is configured to determine target vehicle data matching the interaction data category from the second vehicle data.

[0104] The relationship determining unit is configured to determine a second relationship between the target obstacle and the vehicle based on the target obstacle data of the target obstacle and the target vehicle data of the vehicle.

[0105] According to an embodiment of the present disclosure, the control device of the autonomous driving vehicle further includes: a calculation module and a cancellation module.

[0106] The calculation module is configured to determine the number of jump instructions received within a predetermined period of time. The jump instruction includes at least one of the following: an obstacle avoidance instruction and a risk elimination instruction.

[0107] The cancellation module is configured to control the vehicle to return to the initial lane and cancel the lane change in response to determining that the number of jump instructions is greater than or equal to a predetermined jump threshold.

[0108] According to an embodiment of the present disclosure, the control module includes: a speed determination unit and a control unit.

[0109] The speed determination unit is used to determine the lateral acceleration and longitudinal acceleration according to the lane change obstacle avoidance strategy.

[0110] The control unit is used to control the vehicle to travel according to lateral acceleration and longitudinal acceleration.

[0111] According to an embodiment of the present disclosure, the control device of the autonomous driving vehicle further includes, before the control module: a first acquisition module, a first relationship determination module, and a first generation module.

[0112] The first acquisition module is configured to acquire first obstacle data of a target obstacle and first vehicle data of a vehicle.

[0113] The first relationship determination module is configured to determine a first relationship between the target obstacle and the vehicle based on first obstacle data of the target obstacle and first vehicle data of the vehicle.

[0114] The first generating module is configured to generate an obstacle avoidance instruction in response to determining that a first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition.

[0115] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, an autonomous driving vehicle, and a computer program product.

[0116] According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein 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 execute a method as in the embodiment of the present disclosure.

[0117] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute a method according to an embodiment of the present disclosure.

[0118] According to an embodiment of the present disclosure, an autonomous driving vehicle is configured with the above-mentioned electronic device, and the configured electronic device can implement the control method of the autonomous driving vehicle described in the above embodiment when its processor executes it.

[0119] According to an embodiment of the present disclosure, a computer program product includes a computer program. When the computer program is executed by a processor, the method according to the embodiment of the present disclosure is implemented.

[0120] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, 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 claimed herein.

[0121] like Figure 7As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the device 700 can also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0122] Various components in device 700 are connected to I / O interface 705, including an input unit 706, such as a keyboard, mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, optical disk, etc.; and a communication unit 709, such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0123] The computing unit 701 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 701 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 that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as the control method of an autonomous vehicle. For example, in some embodiments, the control method of an autonomous vehicle can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the control method of the autonomous vehicle described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the control method of the autonomous vehicle by any other appropriate means (e.g., by means of firmware).

[0124] Various embodiments of the systems and techniques described above 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), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes 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.

[0125] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0126] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can 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 can 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.

[0127] 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).

[0128] The systems and techniques described herein can 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 having 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 can 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.

[0129] A computer system may include clients and servers. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0130] It should be understood that the various forms of the 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 a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0131] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for controlling an autonomous vehicle, comprising: During a lane change, in response to receiving an obstacle avoidance instruction, determining a lane change obstacle avoidance strategy based on first obstacle data of a target obstacle, wherein the obstacle avoidance instruction is generated in response to determining that a first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition, the first relationship between the target obstacle and the vehicle being generated based on the first obstacle data of the target obstacle and first vehicle data of the vehicle, and the lane change obstacle avoidance strategy is used to represent a change in a driving state of the vehicle while maintaining the lane change; Controlling the vehicle to travel according to the lane change and obstacle avoidance strategy; and In response to receiving a risk elimination instruction, updating a lane change planned path so that the vehicle travels according to the updated lane change planned path, wherein the risk elimination instruction is generated in response to determining that a second relationship between the target obstacle and the vehicle does not satisfy the predetermined collision condition, the second relationship between the target obstacle and the vehicle being generated based on second obstacle data of the target obstacle and second vehicle data of the vehicle; The second relationship is determined in the following manner: Determine the current lane change category result based on the current lane change scenario data; determining an interaction data category based on the current lane change category result; Determining target obstacle data matching the interaction data category from the second obstacle data; Determining target vehicle data matching the interaction data category from the second vehicle data; and The second relationship between the target obstacle and the vehicle is determined based on the target obstacle data of the target obstacle and the target vehicle data of the vehicle.

2. The method according to claim 1, wherein In response to receiving the risk elimination instruction, updating the lane change planning path includes: In response to receiving the risk resolution instruction, determining current lane change scenario data, wherein the current lane change scenario data includes at least one of the following: obstacle data, vehicle data, environment data, and road traffic regulation data related to the lane change; and Based on the current lane change scenario data, the lane change planning path is updated.

3. The method according to claim 1 , further comprising, before updating the lane change planning path in response to receiving the risk resolution instruction: Acquiring second obstacle data of the target obstacle and second vehicle data of the vehicle; determining a second relationship between the target obstacle and the vehicle based on second obstacle data of the target obstacle and second vehicle data of the vehicle; as well as In response to determining that the second relationship between the target obstacle and the vehicle does not satisfy the predetermined collision condition, the risk resolution instruction is generated.

4. The method according to any one of claims 1 to 3, further comprising: Determining the number of jump instructions received within a predetermined time period, wherein the jump instruction includes at least one of the following: the obstacle avoidance instruction, the risk elimination instruction; and In response to determining that the number of the jump commands is greater than or equal to a predetermined jump threshold, the vehicle is controlled to return to the initial lane and the lane change is canceled.

5. The method according to any one of claims 1 to 3, wherein The controlling the vehicle to travel according to the lane change obstacle avoidance strategy includes: Determining lateral acceleration and longitudinal acceleration according to the lane change obstacle avoidance strategy; and The vehicle is controlled to travel according to the lateral acceleration and the longitudinal acceleration.

6. The method according to any one of claims 1 to 3, further comprising, before controlling the vehicle to travel according to the lane change and obstacle avoidance strategy: Acquiring first obstacle data of the target obstacle and first vehicle data of the vehicle; determining the first relationship between the target obstacle and the vehicle based on first obstacle data of the target obstacle and first vehicle data of the vehicle; as well as In response to determining that a first relationship between the target obstacle and the vehicle satisfies the predetermined collision condition, the obstacle avoidance instruction is generated.

7. A control device for an autonomous vehicle, comprising: a strategy determination module configured to, in response to receiving an obstacle avoidance instruction during a lane change, determine a lane change obstacle avoidance strategy based on first obstacle data of a target obstacle, wherein the obstacle avoidance instruction is generated in response to determining that a first relationship between the target obstacle and the vehicle satisfies a predetermined collision condition, the first relationship between the target obstacle and the vehicle being generated based on the first obstacle data of the target obstacle and first vehicle data of the vehicle, and the lane change obstacle avoidance strategy is configured to represent a change in a driving state of the vehicle while maintaining the lane change; a control module, configured to control the vehicle to travel according to the lane change and obstacle avoidance strategy; and an updating module configured to update the lane change plan path in response to receiving a risk elimination instruction so that the vehicle travels according to the updated lane change plan path, wherein the risk elimination instruction is generated in response to determining that a second relationship between the target obstacle and the vehicle does not satisfy the predetermined collision condition, the second relationship between the target obstacle and the vehicle being generated based on second obstacle data of the target obstacle and second vehicle data of the vehicle; The second relationship is determined in the following manner: Determine the current lane change category result based on the current lane change scenario data; determining an interaction data category based on the current lane change category result; Determining target obstacle data matching the interaction data category from the second obstacle data; Determining target vehicle data matching the interaction data category from the second vehicle data; and The second relationship between the target obstacle and the vehicle is determined based on the target obstacle data of the target obstacle and the target vehicle data of the vehicle.

8. The device according to claim 7, wherein The update module includes: a first data determining unit, configured to determine current lane change scenario data in response to receiving the risk elimination instruction, wherein the current lane change scenario data includes at least one of the following: obstacle data, vehicle data, environment data, and road traffic regulation data related to the lane change; and An updating unit is used to update the lane change planning path based on the current lane change scenario data.

9. The apparatus according to claim 8, further comprising, before the updating module: a second acquisition module, configured to acquire second obstacle data of the target obstacle and second vehicle data of the vehicle; a second relationship determining module, configured to determine the second relationship between the target obstacle and the vehicle based on second obstacle data of the target obstacle and second vehicle data of the vehicle; as well as The second generating module is configured to generate the risk elimination instruction in response to determining that the second relationship between the target obstacle and the vehicle does not satisfy the predetermined collision condition.

10. The apparatus according to any one of claims 7 to 9, further comprising: a calculation module, configured to determine the number of jump instructions received within a predetermined period of time, wherein the jump instruction includes at least one of the following: the obstacle avoidance instruction, the risk elimination instruction; and The cancellation module is configured to control the vehicle to return to the initial lane and cancel the lane change in response to determining that the number of the jump instructions is greater than or equal to a predetermined jump threshold.

11. The device according to any one of claims 7 to 9, wherein The control module includes: a speed determination unit, configured to determine lateral acceleration and longitudinal acceleration according to the lane change obstacle avoidance strategy; and A control unit is used to control the vehicle to travel according to the lateral acceleration and the longitudinal acceleration.

12. The apparatus according to any one of claims 7 to 9, further comprising, before the control module: a first acquisition module, configured to acquire first obstacle data of the target obstacle and first vehicle data of the vehicle; a first relationship determining module, configured to determine the first relationship between the target obstacle and the vehicle based on first obstacle data of the target obstacle and first vehicle data of the vehicle; as well as The first generating module is configured to generate the obstacle avoidance instruction in response to determining that a first relationship between the target obstacle and the vehicle satisfies the predetermined collision condition.

13. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, 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 6.

14. 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 to 6.

15. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

16. An autonomous driving vehicle comprising: The electronic device according to claim 13.

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