Risk obstacle determination method, autonomous driving vehicle, electronic device and medium

Abstract

The present disclosure provides a method, device, autonomous driving vehicle, electronic device and storage medium for determining risk obstacles, and relates to the field of computer technology, and in particular to the field of autonomous driving. The specific implementation scheme is: determining an obstacle whose existence is uncertain; performing a first-level collision detection on the obstacle to obtain a first-level collision detection result; when it is determined that the first-level collision detection result is used to characterize the existence of a collision risk between the autonomous driving vehicle and the obstacle, performing a second-level collision detection on the obstacle to obtain a second-level collision detection result; and when it is determined that the second-level collision detection result is used to characterize the existence of a collision risk between the autonomous driving vehicle and the obstacle, determining that the obstacle is a target risk obstacle. The above scheme improves the processing capability for obstacle collision risks, and in particular, effectively improves the safety of autonomous driving vehicles in the case of multi-sensor fusion.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, in particular to the field of autonomous driving, and specifically to a method and apparatus for determining risk obstacles, an autonomous driving vehicle, an electronic device, a storage medium, and a program product. Background Art

[0002] During the driving process of an autonomous vehicle, there is a possibility of collision with an obstacle. Therefore, the autonomous vehicle usually determines whether there is a collision risk between the autonomous vehicle and the obstacle based on its own motion information and the motion information of the obstacle, and replans the driving trajectory of the autonomous vehicle to ensure safe driving. Summary of the Invention

[0003] The present disclosure provides a risk obstacle determination method, device, autonomous driving vehicle, electronic device, storage medium, and program product.

[0004] According to one aspect of the present disclosure, a method for determining a risk obstacle is provided, comprising:

[0005] Identify obstacles whose existence is uncertain;

[0006] Performing a first-level collision detection on the above-mentioned obstacle to obtain a first-level collision detection result;

[0007] If it is determined that the first-level collision detection result indicates that there is a collision risk between the autonomous driving vehicle and the obstacle, performing a second-level collision detection on the obstacle to obtain a second-level collision detection result; and

[0008] When it is determined that the secondary collision detection result is used to characterize the existence of a collision risk between the autonomous driving vehicle and the obstacle, the obstacle is determined to be a target risk obstacle.

[0009] According to another aspect of the present disclosure, there is provided a risk obstacle determination device, comprising:

[0010] Obstacle determination module, used to determine obstacles whose existence is uncertain;

[0011] A first-level detection module is used to perform first-level collision detection on the above-mentioned obstacles and obtain first-level collision detection results;

[0012] a secondary detection module, configured to, upon determining that the primary collision detection result indicates a collision risk between the autonomous driving vehicle and the obstacle, perform a secondary collision detection on the obstacle to obtain a secondary collision detection result; and

[0013] A risk determination module is used to determine that the obstacle is a target risk obstacle when it is determined that the secondary collision detection result is used to indicate that there is a collision risk between the autonomous driving vehicle and the obstacle.

[0014] 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 so that the at least one processor can execute the method as disclosed in the present disclosure.

[0015] 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 enable the computer to execute the method of the present disclosure.

[0016] 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.

[0017] According to another aspect of the present disclosure, an autonomous driving vehicle is provided, comprising the electronic device of the present disclosure.

[0018] 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

[0019] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0020] Figure 1A Schematically illustrates a scenario in which the risk obstacle determination method and apparatus according to an embodiment of the present disclosure can be applied;

[0021] Figure 1B The structure of an autonomous driving vehicle according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 2 The flowchart of the method for determining risk obstacles according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 3 Schematically shows a flow chart for determining a target area according to an embodiment of the present disclosure;

[0024] Figure 4 A schematic diagram of primary collision detection according to an embodiment of the present disclosure is schematically shown;

[0025] Figure 5A schematic diagram of secondary collision detection according to an embodiment of the present disclosure is schematically shown;

[0026] Figure 6 The following schematically shows a flow chart of a method for determining a risk obstacle according to another embodiment of the present disclosure;

[0027] Figure 7 A block diagram schematically illustrates a risk obstacle determination device according to an embodiment of the present disclosure; and

[0028] Figure 8 A block diagram of an electronic device suitable for implementing a risk obstacle determination method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0029] 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.

[0030] 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.

[0031] In the technical solution disclosed herein, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0032] In order to enable autonomous vehicles to drive safely and comfortably on public roads, multiple sensors of different types are usually configured, and the perception data of multiple sensors are integrated to achieve perception of the surrounding environment of the autonomous vehicle. In this way, the obstacles encountered by the autonomous vehicle during driving are determined. However, in special weather conditions such as night, rain and fog, or when facing irregular obstacles or obstacles that are blocked, obstacles may be missed or misdetected, thereby affecting the accuracy and precision of obstacle detection. Therefore, for obstacles with uncertain existence, such as obstacles with unstable or abnormal existence, they can be reported to the cloud server for monitoring, and the autonomous vehicle can be remotely controlled to reduce the occurrence of safety accidents.

[0033] The current improvement in sensor detection range means that the surrounding environment perceived by autonomous vehicles is usually panoramic, and the number of obstacles with uncertain existence detected is relatively large, which leads to an increase in the reporting frequency, thus affecting the remote processing capability of obstacles with collision risks.

[0034] In view of this, an embodiment of the present disclosure provides a method for determining a risk obstacle, including: determining an obstacle whose existence is uncertain; performing a first-level collision detection on the obstacle to obtain a first-level collision detection result; when it is determined that the first-level collision detection result is used to characterize the existence of a collision risk between the autonomous driving vehicle and the obstacle, performing a second-level collision detection on the obstacle to obtain a second-level collision detection result; and when it is determined that the second-level collision detection result is used to characterize the existence of a collision risk between the autonomous driving vehicle and the obstacle, determining that the obstacle is a target risk obstacle.

[0035] Figure 1A A schematic diagram of a scenario in which the risk obstacle determination method and apparatus according to an embodiment of the present disclosure can be applied is shown schematically.

[0036] It should be noted that Figure 1A The examples shown are merely examples of system architectures to which the embodiments of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure. This does not mean that the embodiments of the present disclosure cannot be applied to other devices, systems, environments, or scenarios. For example, in another embodiment, an exemplary system architecture to which the risk obstacle determination method and apparatus can be applied may include a terminal device, but the terminal device can implement the risk obstacle determination method and apparatus provided by the embodiments of the present disclosure without interacting with a server.

[0037] like Figure 1A As 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 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. For example, the server 103 receives obstacle information transmitted from the autonomous driving vehicle 101, and remotely controls the driving or obstacle avoidance of the autonomous driving vehicle 101 based on the obstacle information.

[0038] 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.

[0039] It should be understood that Figure 1AThe 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.

[0040] Figure 1B The structure diagram of the autonomous driving vehicle according to the embodiment of the present disclosure is schematically shown. Figure 1B As shown, autonomous vehicle 101 may include an onboard terminal 1011, a perception module 1012, a collision detection module 1013, and a path planning module 1014. 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.

[0041] 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.

[0042] The vehicle-mounted terminal 1011 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, a speed sensing unit, an image recognition unit, and a light detection and ranging (LIDAR) unit. The GPS unit may include a transceiver operable to provide information about the autonomous vehicle's location. The IMU unit may sense the autonomous vehicle's position and orientation changes based on inertial acceleration. The radar unit may represent a system that uses radio signals to sense obstacles within the autonomous vehicle's surroundings. In addition to sensing obstacles, the radar unit may also sense the speed and / or heading of 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 being mounted on a rotating or tilting platform.

[0043] The vehicle-mounted terminal 1011 may also include other sensors, such as a sonar sensor, an infrared sensor, a steering sensor, a speed 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.

[0044] The vehicle-mounted terminal 1011 can obtain the autonomous driving vehicle information, obstacle information, map information and perception data of the surrounding environment such as traffic lights or signs of the autonomous driving vehicle 101 itself.

[0045] The perception module 1012 can receive multiple types of perception data from the vehicle-mounted terminal 1011, such as image data and point cloud data. It can fuse the multiple perception data to obtain target area and obstacle information. It can also identify obstacles whose presence is uncertain based on the obstacle information and target area.

[0046] The collision detection module 1013 can receive obstacle information about uncertain obstacles sent by the perception module 1012 and, based on the obstacle information about uncertain obstacles, determine whether there is a collision risk between the obstacle and the autonomous vehicle. If a collision risk is determined to exist, the obstacle information is reported to the cloud server. If no collision risk is determined to exist, the obstacle information is transmitted to the path planning module 1014.

[0047] The path planning module 1014 may receive obstacle information of obstacles whose existence is determined and sent by the perception module 1012, and perform path planning based on the obstacle information of obstacles whose existence is determined, to obtain planned path information.

[0048] It should be noted that the autonomous driving vehicle 101 may also include a vehicle control module and a wireless communication module.

[0049] 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 vehicle control module may receive the planned path information from the path planning module and control the steering unit, throttle unit, and brake unit based on the planned path information. 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 decelerate the wheels or tires of the autonomous vehicle.

[0050] 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.

[0051] 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.

[0052] Figure 2 The flowchart of the risk obstacle determination method according to an embodiment of the present disclosure is schematically shown.

[0053] like Figure 2 As shown, the method includes operations S210 to S240.

[0054] In operation S210 , an obstacle of uncertain existence is determined.

[0055] In operation S220 , a first-level collision detection is performed on the obstacle to obtain a first-level collision detection result.

[0056] In operation S230 , when it is determined that the first-level collision detection result is used to indicate that there is a collision risk between the autonomous driving vehicle and the obstacle, a second-level collision detection is performed on the obstacle to obtain a second-level collision detection result.

[0057] In operation S240 , when it is determined that the secondary collision detection result is used to indicate that there is a collision risk between the autonomous driving vehicle and the obstacle, the obstacle is determined to be a target risk obstacle.

[0058] According to an embodiment of the present disclosure, obstacles with uncertain existence represent obstacles with unstable or abnormal existence, such as cars with open doors, special-shaped cars, semi-trailers, or cars pulling cargo. For obstacles with uncertain existence, it is necessary to further determine whether there is a collision risk between the autonomous driving vehicle and the obstacle. If there is a collision risk, the obstacle information can be reported to the cloud server. So that the cloud server receives the obstacle information and remotely controls the autonomous driving vehicle to perform obstacle avoidance processing, so that the autonomous driving vehicle can drive safely and comfortably. For obstacles with certain existence, such as autonomous driving vehicles in motion, the autonomous driving system can directly plan the driving path of the autonomous driving vehicle based on the obstacle information of the obstacle, thereby controlling the autonomous driving vehicle to avoid obstacles, so that the autonomous driving vehicle can drive safely.

[0059] According to an embodiment of the present disclosure, after determining an obstacle whose existence is uncertain, the autonomous vehicle may not report it directly to the cloud server. Instead, it may perform a first-level collision detection on the obstacle. Performing a first-level collision detection on the obstacle and obtaining a first-level collision detection result may include determining the speeds of the autonomous vehicle and the obstacle. Determining the first-level collision detection result based on the speeds of the autonomous vehicle and the obstacle is not limited to this. The first-level collision detection result may also be determined based on the acceleration and speed of the autonomous vehicle and the obstacle.

[0060] According to an embodiment of the present disclosure, when a first-level collision detection result is determined to indicate a collision risk between the autonomous vehicle and an obstacle, a second-level collision detection is performed on the obstacle. A determination is made based on the distance between the autonomous vehicle and the obstacle to determine whether there is a collision risk between the two. If the distance is less than a threshold, it indicates that the obstacle and the autonomous vehicle are close enough to cause a collision. Therefore, the obstacle is identified as a target risk obstacle, and its obstacle information is stored in a list, which is then reported to a cloud server.

[0061] According to an embodiment of the present disclosure, when a primary collision detection result or a secondary collision detection result indicates that there is no collision risk between the autonomous vehicle and the obstacle, the obstacle can be determined to be a non-target risk obstacle. Obstacle information for the obstacle can be transmitted to the path planning layer of the autonomous vehicle. The path planning layer then performs obstacle avoidance processing based on the obstacle information.

[0062] According to the embodiments of the present disclosure, two-level collision risk detection is performed for obstacles whose existence is uncertain. When the obstacle is determined to be a target risk obstacle, it is reported. This reduces the reporting frequency, reduces the data processing volume of the cloud server, and further improves the remote processing capability for obstacles, effectively improving the safety of autonomous driving vehicles.

[0063] According to the embodiments of the present disclosure, Figure 2 Operation S210 shown, determining an obstacle of uncertain existence, may include the following operations.

[0064] For example, a target area is determined based on the planned path information, an initial obstacle whose existence is uncertain is determined, and if the initial obstacle is determined to be within the target area, the obstacle is determined based on the initial obstacle.

[0065] According to an embodiment of the present disclosure, a target area is determined based on planned path information. The target area can represent an area where a collision risk may occur for an autonomous driving vehicle. The initial obstacles include obstacles whose existence is uncertain within the target area, and obstacles whose existence is uncertain on or outside the target area. When it is determined that the initial obstacle is located within the target area, the initial obstacle can be used as an obstacle. Thus, the obstacle that needs to be detected for collision is determined. By preliminarily screening the initial obstacles, the initial obstacles outside the target area and on the target area are filtered out, and the initial obstacles within the target area remain, the initial obstacle is used as an obstacle for subsequent primary collision detection and secondary collision detection, which reduces the amount of data processing to a certain extent and effectively improves the detection efficiency.

[0066] According to an embodiment of the present disclosure, determining a target area based on planned path information may include the following operations.

[0067] For example, based on the planned path information, a target path point is determined. Based on the target path point, perceived road boundary information, prescribed road boundary information, and predetermined safety boundary information are determined. The perceived road boundary information is obtained from the perception data. The prescribed road boundary information is obtained from the map data. Target boundary information is determined from the perceived road boundary information, prescribed road boundary information, and predetermined safety boundary information. Based on the target boundary information, a target area is determined.

[0068] According to an embodiment of the present disclosure, determining a target pathpoint based on planned path information may include the following operations: determining a pathpoint from the planned path information; a path distance between the pathpoint and a current trajectory point of the autonomous vehicle being greater than an obstacle detection distance threshold; and determining the target pathpoint based on the current trajectory point and the pathpoint of the autonomous vehicle.

[0069] According to an embodiment of the present disclosure, determining a target path point based on the current trajectory points and path points of the autonomous vehicle may include interpolating the path points and the current trajectory points of the autonomous vehicle to obtain the target path point. Because the distance between the path points in the planned path information and the current trajectory points of the autonomous vehicle is generally greater than the obstacle detection distance threshold, interpolation is first performed to densely populate the path points, thereby making the distance between the path points approximately equal to or less than the obstacle detection distance threshold. This allows for accurate and effective demarcation of the target area using the target path points.

[0070] According to other embodiments of the present disclosure, when multiple waypoints in the planned path information are densely packed, for example, when the path distance between the multiple waypoints and the current trajectory point of the autonomous vehicle is less than an obstacle detection distance threshold, the waypoints can be directly used as target waypoints. This will not be further described here.

[0071] According to an embodiment of the present disclosure, the perceived road boundary information is obtained from the perception data collected by the sensor, and the perception data includes obstacle information on the edge of the road. For example, the perception data includes the location information of the fence or guardrail set along the edge of the road. The prescribed road boundary information is obtained from the map data. For example, the prescribed road boundary information includes the boundary line, lane line, etc. of the road determined based on traffic rules. The predetermined safety boundary information is determined based on the width of the autonomous driving vehicle itself. The planned path information can be used as the center line to form two boundary lines based on half the vehicle width or one vehicle width, thereby determining the predetermined safety boundary information.

[0072] According to an embodiment of the present disclosure, the boundary information closest to the target path point is determined from the perceived road boundary information, the specified road boundary information, and the predetermined safety boundary information, and is determined as the target boundary information. The target area is divided based on the target boundary information. Selecting the boundary information closest to the autonomous vehicle as the target boundary information makes the autonomous vehicle more accurate during collision detection and effectively improves the accuracy of obstacle screening.

[0073] Figure 3 The flowchart of determining the target area according to an embodiment of the present disclosure is schematically shown.

[0074] like Figure 3 As shown, the method includes operations S310 to S350.

[0075] In operation S310, the current trajectory point of the autonomous driving vehicle and the path points in the planned path information are interpolated to obtain the target path point.

[0076] In operation S320 , predetermined safety margin information around each target path point is determined.

[0077] In operation S330, the sensed road boundary information of each target path point is determined, and the boundary information closest to the target path point is determined from the predetermined safety boundary information and the sensed road boundary information, and is saved.

[0078] In operation S340, the map road boundary information of each target path point is determined, and the boundary information closest to the target path point is determined from the map road boundary information and the stored boundary information to obtain the target boundary information.

[0079] In operation S350 , a target area is determined based on the target boundary information.

[0080] According to an embodiment of the present disclosure, determining an initial obstacle whose existence is uncertain may include the following operations.

[0081] For example, based on historical perception data sequences, the type of obstacle to be identified is determined. The type indicates whether the obstacle to be identified is an obstacle whose existence is uncertain. Based on the type, the initial obstacle is determined from the obstacles to be identified.

[0082] According to an embodiment of the present disclosure, a historical perception data sequence includes data obtained by arranging perception data of multiple historical moments in time sequence. The perception data of each historical moment may include data collected by multiple sensors. For example, the perception data includes image data and point cloud data. In the case where it is determined that the type of obstacle to be identified is an obstacle with uncertain existence, the obstacle to be identified is determined as an initial obstacle. In the case where it is determined that the type of obstacle to be identified is an obstacle with certain existence, the obstacle to be identified may be determined as a known obstacle. The known obstacles are sent to the path planning module for obstacle avoidance. According to an embodiment of the present disclosure, determining the type of obstacle to be identified based on the historical perception data sequence may include the following operations.

[0083] For example, based on the obstacle category sequence, the category detection result is determined. Based on the position sequence, the position detection result is determined. Based on the speed sequence, the speed detection result is determined. Based on the category detection result, the position detection result, and the speed detection result, the type of obstacle to be identified is determined.

[0084] According to an embodiment of the present disclosure, the historical perception data sequence may include an obstacle category sequence, a position sequence, and a speed sequence. However, the sequence is not limited thereto. The sequence may also include at least one or two of the following: an obstacle category sequence, a position sequence, and a speed sequence.

[0085] According to an embodiment of the present disclosure, for an obstacle category sequence, image information of the obstacle to be identified can be collected by a monitoring device and input into a trained image recognition neural network to determine the category of the obstacle to be identified. The recognition result corresponding to each historical moment is saved in the obstacle category sequence. For a position sequence, the position of the obstacle to be identified can be tracked by radar to obtain the position information of the obstacle to be identified at each historical moment and save it in the position sequence. For a speed sequence, the movement speed of the obstacle to be identified at each historical moment can be collected by a speed sensor and saved in the speed sequence.

[0086] According to embodiments of the present disclosure, category detection results can be determined based on an obstacle category sequence. For example, category stability can be determined based on the categories corresponding to each historical moment in the obstacle category sequence, and the category stability can be used as the category detection result. Specifically, category stability is determined by determining the difference between the obstacle categories corresponding to each historical moment. For example, if the obstacle category is identified as a dog at 10:00 and an umbrella at 10:01, the obstacle category stability is determined to be low.

[0087] According to an embodiment of the present disclosure, the position detection result can be determined based on the position sequence. For example, based on the position corresponding to each historical moment in the position sequence, the position stability is determined, and the position stability is used as the position detection result. Specifically, the position stability is determined by determining the fluctuation amplitude of the difference between the positions at each historical moment. For example: the difference between 10:00 and 10:01 is 1 meter, the difference between 10:01 and 10:02 is also 1 meter; the difference between 10:02 and 10:03 is also 1 meter, which means that the movement process of the obstacle is stable and the stability is relatively high. A position fluctuation threshold can be set to measure the position stability. For example, if the fluctuation between the differences is set to 5 meters, it is determined that the position stability of the obstacle is low.

[0088] According to an embodiment of the present disclosure, a speed detection result can be determined based on a speed sequence. For example, based on the speed corresponding to each historical moment in the speed sequence, the speed stability is determined, and the speed stability is used as the speed detection result. Specifically, by determining the movement speed of the obstacle at each historical moment, for example: the movement speed of the obstacle at 10:00 is 48km / h, the movement speed of the obstacle at 10:01 is also 48km / h, and the movement speed of the obstacle at 10:03 is also 48km / h, then it is determined that the movement stability of the obstacle is high. A speed fluctuation threshold can be set to measure the position stability. For example, if the speed fluctuation value between two adjacent moments reaches a threshold of 1km / h, then it is determined that the position stability of the obstacle is low.

[0089] According to an embodiment of the present disclosure, determining the type of obstacle to be identified based on the category detection results, position detection results, and speed detection results may include: determining target stability based on category stability, position stability, and speed stability; and determining the type of obstacle to be identified based on target stability. For example, the category stability, position stability, and speed stability are weighted and summed to obtain target stability. For example, a higher stability is assigned a value of 1, and a lower stability is assigned a value of -1. A corresponding weight is set for the importance of each stability, and the weighted sum is performed to obtain target stability. The higher the target stability, the more stable the obstacle. If the target stability is greater than a preset threshold, the obstacle to be identified is determined to be an obstacle with certain existence. If the target stability is less than the preset threshold, the obstacle to be identified is determined to be an obstacle with uncertain existence.

[0090] For example, set the weight of category stability to 6, the weight of position stability to 2, and the weight of speed stability to 2. Assuming the preset threshold is 5, and the value assigned to category stability is 1, the value assigned to speed stability is -1, and the value assigned to position stability is -1, then the value assigned to target stability is 2, which is less than the preset threshold. In this case, the obstacle to be identified is an obstacle of uncertain existence.

[0091] According to the embodiments of the present disclosure, the type of obstacle to be identified is determined by combining the obstacle category sequence, position sequence, and speed sequence in the historical perception data sequence. This allows for a certain degree of fusion of multi-sensory data to generate multi-sensory fusion data. This improves the accuracy of determining whether the obstacle to be identified is an initial obstacle whose existence is uncertain.

[0092] According to the embodiments of the present disclosure, the risk obstacle determination method provided by the present disclosure is used to improve the processing capability for obstacle collision risks, especially effectively improving the safety of autonomous driving vehicles in the case of multi-sensor fusion.

[0093] According to embodiments of the present disclosure, if an initial obstacle is determined to be located within the target area and its presence is uncertain, the obstacle can be preliminarily determined to have unstable properties. This may result in a certain level of false detection. A first-level collision detection can be performed on such obstacles to determine whether there is a collision risk between the obstacle and the autonomous vehicle. This reduces the frequency of reporting to the cloud server and improves the safety of autonomous driving.

[0094] According to the embodiments of the present disclosure, Figure 2 Operation S220 shown, performing a first-level collision detection on the obstacle to obtain a first-level collision detection result, may include the following operations.

[0095] For example, the speed of an obstacle in the planned path is determined. Based on the speed of the autonomous vehicle and the speed of the obstacle, a first-level collision detection result is obtained.

[0096] According to an embodiment of the present disclosure, when the autonomous vehicle's speed is less than or equal to the speed of an obstacle, the resulting primary collision detection result indicates that there is no collision risk between the autonomous vehicle and the obstacle. The obstacle can be treated as a known obstacle, and its obstacle information can be transmitted to the path planning module. When the autonomous vehicle's speed is greater than the speed of the obstacle, the resulting primary collision detection result indicates that there is a collision risk between the autonomous vehicle and the obstacle, and a secondary collision detection is performed on the obstacle.

[0097] According to embodiments of the present disclosure, determining the speed of an obstacle in the direction of a planned path may include decomposing the obstacle's speed into a speed parallel to the planned path and a speed perpendicular to the planned path. This allows the obstacle and the autonomous vehicle to be compared in the same direction of motion, making the resulting primary collision detection result intuitive, simple, and effective.

[0098] According to an embodiment of the present disclosure, determining the speed of an obstacle in the direction of a planned path may include the following operations: determining a second target road segment from a plurality of target path points based on the current position of the obstacle. The plurality of target path points are determined based on the planned path information of the autonomous driving vehicle. Determining the speed of the obstacle in the direction of the planned path based on the second target road segment and the speed of the obstacle in the world coordinate system.

[0099] Figure 4 A schematic diagram of primary collision detection according to an embodiment of the present disclosure is schematically shown.

[0100] like Figure 4 As shown, route AB is the planned path determined based on the autonomous vehicle's planned path information. The two dashed lines on the left and right of the planned path are the target boundaries determined based on the autonomous vehicle's target boundary information. The area within the target boundaries is the target area. Based on the historical perception data sequence, obstacles within the target area are determined to be obstacles P, whose existence is uncertain.

[0101] The second target section can be determined from a plurality of target path points based on the current position of the obstacle P. Figure 4 As shown, the multiple solid points on route AB are target path points. Based on the current location of obstacle P, the two target path points d1 and d2 closest to obstacle P are determined, and the section between d1 and d2 is used as the second target section. The driving direction between the second target sections is the planned route direction.

[0102] like Figure 4 As shown, the obstacle P moves at a speed of v1, which is (v x , v y ). v1 can be decomposed into the velocity v parallel to the planned path direction p and the velocity v perpendicular to the planned path m The speed v of the obstacle P in the planned path direction is determined by p , the conversion formula can be as follows:

[0103]

[0104] According to the embodiments of the present disclosure, Figure 2 Operation S230 shown, performing secondary collision detection on the obstacle to obtain a secondary collision detection result, may include the following operations.

[0105] For example, the longitudinal distance between the autonomous vehicle and the obstacle along the planned path after a predetermined reaction time is determined. Based on the longitudinal distance, the autonomous vehicle's speed, and the obstacle's speed, the available travel time along the planned path is determined. Based on the available travel time, the obstacle's target position is determined. Based on the obstacle's target position, a secondary collision detection result is generated.

[0106] According to an embodiment of the present disclosure, the predetermined reaction time of an autonomous vehicle is determined based on the reaction sensitivity of the autonomous vehicle and can be set to, for example, 1 second. The autonomous vehicle continues to travel along the planned path and at the planned speed within the predetermined reaction time. The longitudinal distance between the autonomous vehicle and the obstacle in the planned path direction after the predetermined reaction time has elapsed is determined. The drivable time is determined based on the longitudinal distance, the driving speed, and the speed of the obstacle. This drivable time can be understood as the time it takes for the autonomous vehicle to reach alignment with the obstacle in the planned path direction after the predetermined reaction time has elapsed and then decelerates. As the autonomous vehicle decelerates within the drivable time, the longitudinal distance between the autonomous vehicle and the obstacle in the planned path direction gradually decreases until they are aligned. Therefore, the target position of the obstacle is determined based on the drivable time, and further, the risk of collision between the autonomous vehicle and the obstacle is determined based on the target position.

[0107] According to the embodiment of the present disclosure, during the secondary collision detection process, the predetermined reaction time and the drivable time in the planned path direction are taken into consideration, which can improve the processing accuracy while combining the actual real situation.

[0108] According to an embodiment of the present disclosure, determining the duration of a planned path based on the longitudinal distance, the speed of the autonomous vehicle, and the speed of an obstacle may include the following operations: determining a maximum acceleration of the autonomous vehicle for braking based on the longitudinal distance, the speed of the autonomous vehicle, and the speed of the obstacle; and determining the duration of the planned path based on the acceleration threshold and the longitudinal distance if the maximum acceleration is greater than a threshold.

[0109] According to an embodiment of the present disclosure, the maximum acceleration determined based on the longitudinal distance, the driving speed of the autonomous driving vehicle and the movement speed of the obstacle is the acceleration required to stop the vehicle without colliding in the direction of the planned path. The acceleration threshold is the limit acceleration within the equipment performance range of the autonomous driving vehicle. In the case where the maximum acceleration is greater than the acceleration threshold, the drivable time is determined based on the acceleration threshold and the longitudinal distance. In the case where the maximum acceleration is less than or equal to the acceleration threshold, the drivable time can be determined based on the maximum acceleration and the longitudinal distance. The drivable time may include: the quotient of the longitudinal distance and the maximum acceleration or the acceleration threshold.

[0110] According to an embodiment of the present disclosure, the maximum acceleration and the acceleration threshold are compared, and the drivable time is determined based on the smaller value, thereby improving the detection accuracy of the secondary collision detection in combination with the actual situation.

[0111] According to an embodiment of the present disclosure, obtaining a secondary collision detection result based on the target position of an obstacle may include the following operations: determining the target sub-positions of each of the plurality of edge points of the obstacle based on the target position of the obstacle. For each target sub-position, determining a first target section from a plurality of target path points to obtain a plurality of first target sections. The plurality of target path points are determined based on the planned path information of the autonomous driving vehicle. Based on the plurality of first target sections and the plurality of target sub-positions, a plurality of lateral spacings are determined. The lateral spacing is the vertical distance between the target sub-position and the first target section that matches the target sub-position. Based on the plurality of lateral spacings and the lateral spacing threshold, a secondary collision detection result is obtained.

[0112] According to an embodiment of the present disclosure, the movement speed of an obstacle can be decomposed into the direction of the planned path and the direction perpendicular to the planned path. In the case that the obstacle and the autonomous driving vehicle are aligned in the direction of the planned path, it is possible that the lateral distance between the obstacle and the autonomous driving vehicle in the direction perpendicular to the planned path is already very far apart and is greater than the lateral distance threshold. In this case, there is no risk of collision between the obstacle and the autonomous driving vehicle. In the case that the obstacle and the autonomous driving vehicle are aligned in the direction of the planned path, and the lateral distance between the obstacle and the autonomous driving vehicle in the direction perpendicular to the planned path is less than or equal to the lateral distance threshold, there is a risk of collision between the obstacle and the autonomous driving vehicle.

[0113] According to other embodiments of the present disclosure, the lateral spacing between the obstacle and the autonomous vehicle can be determined based on the target position of the obstacle and the target position of the autonomous vehicle. However, this is not limiting. Alternatively, the center position of the obstacle can be determined based on the target position of the obstacle, and the lateral spacing can be determined based on the center position and a first target road segment that matches the center position. The first target road segment that matches the center position is the road segment formed by the two target road segments that are closest to the center position among the multiple target road segments.

[0114] According to the embodiments of the present disclosure, multiple edge points of an obstacle are calculated to determine multiple lateral spacings, enabling comprehensive and accurate secondary collision detection results. Furthermore, using multiple target path points as the estimated position of the autonomous vehicle reduces data processing volume and improves processing efficiency while maintaining secondary collision detection accuracy.

[0115] Figure 5 A schematic diagram of secondary collision detection according to an embodiment of the present disclosure is schematically shown.

[0116] like Figure 5 As shown, route AB is a planned path determined based on the planned path information of the autonomous vehicle. The autonomous vehicle travels to path point d0 and performs a secondary collision detection on obstacle P at position S. The longitudinal distance L between the autonomous vehicle and obstacle P in the direction of the planned path is determined after a predetermined reaction time. Based on the longitudinal distance, the autonomous vehicle's speed, and the speed of obstacle P, the available travel time in the direction of the planned path is determined. Based on the available travel time, the target position E of obstacle P is determined.

[0117] like Figure 5 As shown, based on the target position E where the obstacle P is located, the target sub-positions of the four edge points of the obstacle P are determined. And the first target road section that matches each of the four edge points is determined. For example, the first target road section formed by the target path points d3 and d4 that matches the edge point P1. Based on the target sub-position and the first target road section that matches the target sub-position, the lateral spacing is determined, such as the lateral spacing H between the edge point P1 and the first target road section. Multiple lateral spacings are obtained. The minimum lateral spacing is determined from the multiple lateral spacings. The minimum lateral spacing is the lateral spacing H. The lateral spacing H is compared with the lateral spacing threshold. When it is determined that the lateral spacing H is greater than the lateral spacing threshold, it is determined that there is no collision risk between the obstacle and the autonomous driving vehicle. When it is determined that the lateral spacing H is less than or equal to the lateral spacing threshold, it is determined that there is a collision risk between the obstacle and the autonomous driving vehicle.

[0118] According to an embodiment of the present disclosure, assuming that the longitudinal distance is L, based on the driving speed v of the autonomous driving vehiclec and the obstacle's speed v p Determine the maximum acceleration A of the autonomous vehicle at its current speed using the following formula:

[0119]

[0120] According to an embodiment of the present disclosure, a determination is made as to whether the maximum acceleration A is greater than an acceleration threshold Ta. The setting of the acceleration threshold is related to the braking performance of the autonomous vehicle. If it is greater, the maximum acceleration required for braking the autonomous vehicle is determined to be the acceleration threshold Ta. If it is not greater, the maximum acceleration required for braking the autonomous vehicle is determined to be A.

[0121] According to an embodiment of the present disclosure, when it is determined that the maximum acceleration is greater than the acceleration threshold, the travelable time is determined based on the acceleration threshold and the longitudinal distance. The calculation formula is as follows:

[0122]

[0123] Among them, Ta represents the acceleration threshold, T r represents the expected reaction time of the autonomous vehicle, T c Indicates the exercisable period.

[0124] According to an embodiment of the present disclosure, based on the traversable time and the initial position (x0, y0) of the obstacle, the target position (x0, y0) of the obstacle after the traversable time can be determined. n ,y n ), the calculation formula is as follows:

[0125]

[0126] Figure 6 The flowchart of a method for determining a risk obstacle according to another embodiment of the present disclosure is schematically shown.

[0127] like Figure 6 As shown, the method includes the following operations S601 to S610.

[0128] In operation S601 , an obstacle of uncertain existence is determined.

[0129] In operation S602, it is determined whether the speed of the obstacle in the planned path direction is less than the driving speed of the autonomous driving vehicle. If the speed is less than the driving speed, operation S603 is performed; otherwise, operation S610 is performed.

[0130] In operation S603 , a longitudinal distance between the autonomous driving vehicle and the obstacle in the direction of the planned path after a predetermined reaction time is determined.

[0131] In operation S604 , a maximum acceleration of the autonomous driving vehicle for braking is determined based on the longitudinal distance, the driving speed of the autonomous driving vehicle, and the moving speed of the obstacle.

[0132] In operation S605, it is determined whether the maximum acceleration is greater than the acceleration threshold. If the maximum acceleration is greater than the acceleration threshold, operation S6061 is performed. If the maximum acceleration is less than or equal to the acceleration threshold, operation S6062 is performed.

[0133] In operation S6061 , an expedited duration is determined based on the acceleration threshold and the longitudinal distance.

[0134] In operation S6062, a drivable time period is determined based on the maximum acceleration and the longitudinal distance.

[0135] In operation S607 , a target position of the obstacle is determined based on the drivable time.

[0136] In operation S608, it is determined whether the lateral distance between the obstacle and the second target road segment is greater than a lateral distance threshold. If it is determined that the lateral distance is less than or equal to the lateral distance threshold, operation S609 is performed. Otherwise, operation S610 is performed.

[0137] In operation S609 , it is determined that there is a collision risk between the obstacle and the autonomous driving vehicle.

[0138] In operation S610 , it is determined that there is no collision risk between the obstacle and the autonomous driving vehicle.

[0139] Figure 7 The block diagram of the risk obstacle determination device according to an embodiment of the present disclosure is schematically shown.

[0140] like Figure 7 As shown, a risk obstacle determination device 700 of this embodiment includes an obstacle determination module 710 , a primary detection module 720 , a secondary detection module 730 and a risk determination module 740 .

[0141] The obstacle determination module 710 is configured to determine obstacles whose existence is uncertain. In one embodiment, the obstacle determination module 710 may be configured to perform the operation S210 described above, which will not be described in detail herein.

[0142] The first level detection module 720 is used to perform a first level collision detection on the obstacle and obtain a first level collision detection result. In one embodiment, the first level detection module 720 can be used to perform the operation S220 described above, which will not be repeated here.

[0143] Secondary detection module 730 is configured to, upon determining that the primary collision detection result indicates a collision risk between the autonomous vehicle and the obstacle, perform secondary collision detection on the obstacle to obtain a secondary collision detection result. In one embodiment, secondary detection module 730 may be configured to perform operation S230 described above and will not be further described herein.

[0144] Risk determination module 740 is configured to determine that an obstacle is a target risk obstacle when the secondary collision detection result indicates a collision risk between the autonomous vehicle and the obstacle. In one embodiment, risk determination module 740 may be configured to perform operation S240 described above and will not be further described here.

[0145] According to the embodiments of the present disclosure, two-level collision risk detection is performed for obstacles whose existence is uncertain. When the obstacle is determined to be a target risk obstacle, it is reported. This reduces the reporting frequency, reduces the data processing volume of the cloud server, and further improves the remote processing capability for obstacles, effectively improving the safety of autonomous driving vehicles.

[0146] According to an embodiment of the present disclosure, the obstacle determination module 710 includes an area determination submodule, a region determination submodule, and an obstacle determination submodule.

[0147] The area determination submodule is used to determine the target area based on the planned path information.

[0148] The initial determination submodule is used to determine the initial obstacles whose existence is uncertain.

[0149] The obstacle determination submodule is configured to determine an obstacle based on the initial obstacle when it is determined that the initial obstacle is located within the target area.

[0150] According to an embodiment of the present disclosure, the region determination submodule includes a waypoint determination unit, an information determination unit, a boundary determination unit, and a region determination unit.

[0151] The path point determination unit is used to determine the target path point based on the planned path information.

[0152] An information determination unit is used to determine perceived road boundary information, prescribed road boundary information and predetermined safety boundary information based on the target path point, wherein the perceived road boundary information is obtained from the perception data and the prescribed road boundary information is obtained from the map data.

[0153] The boundary determination unit is used to determine target boundary information from the perceived road boundary information, the specified road boundary information and the predetermined safety boundary information.

[0154] The region determination unit is configured to determine a target region based on target boundary information.

[0155] According to an embodiment of the present disclosure, the initial determination submodule includes a type determination unit and an initial determination unit.

[0156] The type determination unit is used to determine the type of the obstacle to be identified based on the historical perception data sequence, wherein the type is used to characterize whether the obstacle to be identified is an obstacle of uncertain existence.

[0157] The initial determination unit is configured to determine an initial obstacle from obstacles to be identified based on the type.

[0158] According to an embodiment of the present disclosure, the type determination unit includes a category determination subunit, a position determination subunit, a speed determination subunit, and a type determination subunit.

[0159] The category determination subunit is used to determine the category detection result based on the obstacle category sequence.

[0160] The position determination subunit is used to determine the position detection result based on the position sequence.

[0161] The speed determination subunit is used to determine the speed detection result based on the speed sequence.

[0162] The type determination subunit is used to determine the type of obstacle to be identified based on the category detection result, the position detection result and the speed detection result.

[0163] According to an embodiment of the present disclosure, the primary detection module 720 includes a motion determination submodule and a first result determination submodule.

[0164] The motion determination submodule is used to determine the speed of the obstacle in the direction of the planned path.

[0165] The first result determination submodule is used to obtain a first-level collision detection result based on the driving speed of the autonomous driving vehicle and the movement speed of the obstacle.

[0166] According to an embodiment of the present disclosure, the secondary detection module 730 includes a longitudinal determination submodule, a duration determination submodule, a position determination submodule, and a second result determination submodule.

[0167] The longitudinal determination submodule is used to determine the longitudinal distance between the autonomous driving vehicle and the obstacle in the planned path direction after a predetermined reaction time.

[0168] The duration determination submodule is used to determine the drivable duration in the direction of the planned path based on the longitudinal spacing, the driving speed of the autonomous driving vehicle, and the movement speed of the obstacles.

[0169] The position determination submodule is used to determine the target position of the obstacle based on the drivable time.

[0170] The second result determination submodule is used to obtain a secondary collision detection result based on the target position of the obstacle.

[0171] According to an embodiment of the present disclosure, the duration determination submodule includes a brake stop determination unit and a duration determination unit.

[0172] The braking determination unit is used to determine the maximum acceleration of the autonomous driving vehicle for braking based on the longitudinal distance, the driving speed of the autonomous driving vehicle and the movement speed of the obstacle.

[0173] The duration determining unit is configured to determine an expedited duration based on the acceleration threshold and the longitudinal distance when it is determined that the maximum acceleration is greater than the acceleration threshold.

[0174] According to an embodiment of the present disclosure, the second result determination submodule includes a sub-position determination unit, a first road segment determination unit, a distance determination unit, and a second result determination unit.

[0175] The sub-position determining unit is configured to determine the target sub-positions of the plurality of edge points of the obstacle based on the target position of the obstacle.

[0176] The first road section determination unit is used to determine a first target road section from multiple target path points for each target sub-position, thereby obtaining multiple first target road sections, wherein the multiple target path points are determined based on planned path information of the autonomous driving vehicle.

[0177] The spacing determination unit is configured to determine a plurality of lateral spacings based on the plurality of first target road segments and the plurality of target sub-positions, wherein the lateral spacing is a vertical distance between a target sub-position and a first target road segment matching the target sub-position.

[0178] The second result determination unit is configured to obtain a secondary collision detection result based on a plurality of lateral distances and lateral distance thresholds.

[0179] According to an embodiment of the present disclosure, the motion determination submodule includes a second road segment determination unit and a motion determination unit.

[0180] The second road section determination unit is used to determine a second target road section from a plurality of target path points based on the current position of the obstacle, wherein the plurality of target path points are determined based on planned path information of the autonomous driving vehicle.

[0181] The motion determination unit is used to determine the motion speed of the obstacle in the planned path direction based on the motion speeds of the second target road segment and the obstacle in the world coordinate system.

[0182] According to an embodiment of the present disclosure, the waypoint determination unit includes a waypoint determination subunit and a target determination subunit.

[0183] A waypoint determination subunit is used to determine a waypoint from the planned path information, wherein the path distance between the waypoint and the current trajectory point of the autonomous driving vehicle is greater than the obstacle detection distance threshold.

[0184] The target determination subunit is used to determine the target path point based on the current trajectory point and path point of the autonomous driving vehicle.

[0185] According to the module of the embodiment of the present disclosure, submodule, unit, subunit, any multiple, or at least part of the function of any multiple thereof can be realized in one module. According to the module of the embodiment of the present disclosure, submodule, unit, subunit, any one or more can be split into multiple modules to realize. According to the module of the embodiment of the present disclosure, submodule, unit, subunit, any one or more can be at least partially realized as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be realized by hardware or firmware of any other reasonable way of integrating or encapsulating the circuit, or by any one of the three implementation modes of software, hardware and firmware or by a suitable combination of any of them. Or, according to the module of the embodiment of the present disclosure, submodule, unit, subunit, one or more can be at least partially realized as a computer program module, which can execute the corresponding function when the computer program module is run.

[0186] According to embodiments of the present disclosure, any of the obstacle determination module 710, the primary detection module 720, the secondary detection module 730, and the risk determination module 740 may be implemented in a single module / unit / sub-unit, or any of these modules / units / sub-units may be split into multiple modules / units / sub-units. Alternatively, at least part of the functionality of one or more of these modules / units / sub-units may be combined with at least part of the functionality of other modules / units / sub-units and implemented in a single module / unit / sub-unit. According to embodiments of the present disclosure, at least one of the obstacle determination module 710, the primary detection module 720, the secondary detection module 730, and the risk determination module 740 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of these. Alternatively, at least one of the obstacle determination module 710, the primary detection module 720, the secondary detection module 730, and the risk determination module 740 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0187] It should be noted that the risk obstacle determination device part in the embodiment of the present disclosure corresponds to the risk obstacle determination method part in the embodiment of the present disclosure. The description of the risk obstacle determination device part specifically refers to the risk obstacle determination method part, which will not be repeated here.

[0188] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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 risk obstacle determination method described in the above embodiment when executed by its processor.

[0193] Figure 8 A block diagram of an electronic device suitable for implementing a risk obstacle determination method according to an embodiment of the present disclosure is schematically 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 may 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.

[0194] like Figure 8 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the device 800 can also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0195] Various components in device 800 are connected to an input / output (I / O) interface 805, including an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0196] The computing unit 801 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the risk obstacle determination method. For example, in some embodiments, the risk obstacle determination method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the risk obstacle determination method described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the risk obstacle determination method by any other suitable means (e.g., via firmware).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] A computer system may include a client and a server. 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.

[0203] 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.

[0204] 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 determining risk obstacles, comprising: Identify obstacles whose existence is uncertain; Performing a first-level collision detection on the obstacle to obtain a first-level collision detection result; In a case where it is determined that the first-level collision detection result is used to indicate the existence of a collision risk between the autonomous driving vehicle and the obstacle, performing a second-level collision detection on the obstacle to obtain a second-level collision detection result, including: determining a longitudinal distance between the autonomous driving vehicle and the obstacle in the direction of the planned path after a predetermined reaction time; determining a maximum acceleration of the autonomous driving vehicle for braking based on the longitudinal distance, the driving speed of the autonomous driving vehicle, and the movement speed of the obstacle; in a case where it is determined that the maximum acceleration is greater than an acceleration threshold, determining a drivable time based on the acceleration threshold and the longitudinal distance; determining a target position of the obstacle based on the drivable time; and obtaining the second-level collision detection result based on the target position of the obstacle; and In a case where it is determined that the secondary collision detection result is used to characterize the existence of a collision risk between the autonomous driving vehicle and the obstacle, the obstacle is determined to be a target risk obstacle.

2. The method according to claim 1, wherein The obstacle of uncertain existence is determined to include: Determine the target area based on the planned path information; Identify initial obstacles whose existence is uncertain; and In a case where it is determined that the initial obstacle is located within the target area, the obstacle is determined based on the initial obstacle.

3. The method according to claim 2, wherein: The determining of the target area based on the planned path information includes: Determining a target path point based on the planned path information; Determining, based on the target path point, perceived road boundary information, prescribed road boundary information, and predetermined safety boundary information, wherein the perceived road boundary information is obtained from the perception data, and the prescribed road boundary information is obtained from the map data; determining target boundary information from the perceived road boundary information, the prescribed road boundary information, and the predetermined safety boundary information; and The target area is determined based on the target boundary information.

4. The method according to claim 2, wherein: The determining of the initial obstacle of uncertain existence includes: Determining the type of the obstacle to be identified based on the historical perception data sequence, wherein the type is used to indicate whether the obstacle to be identified is an obstacle of uncertain existence; and Based on the type, the initial obstacle is determined from the obstacles to be identified.

5. The method according to claim 4, wherein The historical perception data sequence includes an obstacle category sequence, a position sequence, and a speed sequence; The determining the type of the obstacle to be identified based on the historical perception data sequence includes: Determining a category detection result based on the obstacle category sequence; determining a position detection result based on the position sequence; determining a speed detection result based on the speed sequence; and The type of the obstacle to be identified is determined based on the category detection result, the position detection result, and the speed detection result.

6. The method according to any one of claims 1 to 5, wherein The performing a first-level collision detection on the obstacle to obtain a first-level collision detection result includes: Determining the speed of movement of the obstacle in the direction of the planned path; and The first-level collision detection result is obtained based on the driving speed of the autonomous driving vehicle and the movement speed of the obstacle.

7. The method according to claim 1, wherein The obtaining of the secondary collision detection result based on the target position of the obstacle includes: Determining target sub-positions of respective edge points of the obstacle based on the target position of the obstacle; For each target sub-position, determining a first target road segment from a plurality of target path points to obtain a plurality of first target road segments, wherein the plurality of target path points are determined based on planned path information of the autonomous driving vehicle; Determining a plurality of lateral spacings based on the plurality of first target road segments and the plurality of target sub-locations, wherein the lateral spacings are vertical distances between the target sub-location and a first target road segment matching the target sub-location; and The secondary collision detection result is obtained based on the multiple lateral distances and the lateral distance thresholds.

8. The method according to claim 6, wherein: Determining the movement speed of the obstacle in the direction of the planned path includes: Determining a second target road segment from a plurality of target path points based on the current position of the obstacle, wherein the plurality of target path points are determined based on planned path information of the autonomous driving vehicle; and The moving speed of the obstacle in the planned path direction is determined based on the moving speed of the second target road segment and the obstacle in the world coordinate system.

9. The method according to claim 3, wherein: The determining of the target path point based on the planned path information includes: Determining a path point from the planned path information, wherein a path distance between the path point and a current trajectory point of the autonomous driving vehicle is greater than an obstacle detection distance threshold; and The target path point is determined based on the current trajectory point of the autonomous driving vehicle and the path point.

10. A risk obstacle determination device, comprising: Obstacle determination module, used to determine obstacles whose existence is uncertain; A first-level detection module, configured to perform a first-level collision detection on the obstacle and obtain a first-level collision detection result; a secondary detection module, configured to, when determining that the primary collision detection result indicates that there is a collision risk between the autonomous driving vehicle and the obstacle, perform a secondary collision detection on the obstacle to obtain a secondary collision detection result; as well as a risk determination module, configured to, upon determining that the secondary collision detection result indicates that there is a collision risk between the autonomous driving vehicle and the obstacle, determine that the obstacle is a target risk obstacle; Wherein, the secondary detection module includes: a longitudinal determination submodule, configured to determine a longitudinal distance between the autonomous driving vehicle and the obstacle in the direction of the planned path after a predetermined reaction time; a duration determination submodule, configured to determine a duration of travel in the direction of the planned path based on the longitudinal distance, the driving speed of the autonomous driving vehicle, and the movement speed of the obstacle; a position determination submodule, configured to determine a target position of the obstacle based on the drivable time; and A second result determination submodule, configured to obtain the secondary collision detection result based on the target position of the obstacle; The duration determination submodule includes: a braking determination unit, configured to determine a maximum acceleration of the autonomous driving vehicle for braking based on the longitudinal distance, the travel speed of the autonomous driving vehicle, and the movement speed of the obstacle; and The duration determining unit is configured to determine the drivable duration based on the acceleration threshold and the longitudinal distance when it is determined that the maximum acceleration is greater than the acceleration threshold.

11. The device according to claim 10, wherein The obstacle determination module includes: The area determination submodule is used to determine the target area based on the planned path information; an initial determination submodule, for determining an initial obstacle whose existence is uncertain; and The obstacle determination submodule is configured to determine the obstacle based on the initial obstacle when it is determined that the initial obstacle is located in the target area.

12. The device according to claim 11, wherein The area determination submodule includes: a path point determination unit, configured to determine a target path point based on the planned path information; an information determining unit, configured to determine, based on the target path point, perceived road boundary information, prescribed road boundary information, and predetermined safety boundary information, wherein the perceived road boundary information is obtained from the perception data, and the prescribed road boundary information is obtained from the map data; a boundary determining unit, configured to determine target boundary information from the sensed road boundary information, the prescribed road boundary information, and the predetermined safety boundary information; and The area determination unit is configured to determine the target area based on the target boundary information.

13. The device according to claim 11, wherein The initial determination submodule includes: a type determination unit, configured to determine the type of the obstacle to be identified based on the historical perception data sequence, wherein the type is used to indicate whether the obstacle to be identified is an obstacle of uncertain existence; and An initial determination unit is configured to determine the initial obstacle from the obstacles to be identified based on the type.

14. The device according to claim 13, wherein The historical perception data sequence includes an obstacle category sequence, a position sequence, and a speed sequence; Wherein, the type determination unit includes: a category determination subunit, configured to determine a category detection result based on the obstacle category sequence; a position determination subunit, configured to determine a position detection result based on the position sequence; a speed determination subunit, configured to determine a speed detection result based on the speed sequence; and The type determination subunit is configured to determine the type of the obstacle to be identified based on the category detection result, the position detection result, and the speed detection result.

15. The device according to any one of claims 10 to 14, wherein the primary detection module comprises: A motion determination submodule, configured to determine a motion speed of the obstacle in the direction of the planned path; as well as The first result determination submodule is used to obtain the first-level collision detection result based on the driving speed of the autonomous driving vehicle and the movement speed of the obstacle.

16. The device according to claim 10, wherein The second result determination submodule includes: a sub-position determining unit, configured to determine a target sub-position of each of a plurality of edge points of the obstacle based on the target position of the obstacle; a first road segment determining unit, configured to determine, for each target sub-position, a first target road segment from a plurality of target path points, to obtain a plurality of first target road segments, wherein the plurality of target path points are determined based on planned path information of the autonomous driving vehicle; a spacing determination unit, configured to determine a plurality of lateral spacings based on the plurality of first target road segments and the plurality of target sub-locations, wherein the lateral spacing is a vertical distance between the target sub-location and a first target road segment matching the target sub-location; and The second result determination unit is configured to obtain the secondary collision detection result based on the multiple lateral distances and the lateral distance thresholds.

17. The device according to claim 15, wherein The motion determination submodule includes: a second road segment determining unit, configured to determine a second target road segment from a plurality of target path points based on the current position of the obstacle, wherein the plurality of target path points are determined based on planned path information of the autonomous driving vehicle; and A motion determination unit is configured to determine a motion speed of the obstacle in the direction of the planned path based on the motion speeds of the second target road segment and the obstacle in a world coordinate system.

18. The device according to claim 12, wherein The path point determination unit includes: a waypoint determination subunit, configured to determine a waypoint from the planned path information, wherein a path distance between the waypoint and a current trajectory point of the autonomous driving vehicle is greater than an obstacle detection distance threshold; and The target determination subunit is used to determine the target path point based on the current trajectory point of the autonomous driving vehicle and the path point.

19. 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 9.

20. 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 9.

21. 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 9.

22. An autonomous driving vehicle comprising: The electronic device as claimed in claim 19.

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