CN116486374A

CN116486374A - Risk obstacle determination method, automatic driving vehicle, electronic device and medium

Info

Publication number: CN116486374A
Application number: CN202310437167.3A
Authority: CN
Inventors: 李俊慧
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2023-04-21
Filing date: 2023-04-21
Publication date: 2023-07-25

Abstract

The disclosure provides a risk obstacle determining method, a risk obstacle determining device, an automatic driving vehicle, electronic equipment and a storage medium, and relates to the technical field of computers, in particular to the automatic driving field. The specific implementation scheme is as follows: determining an obstacle with uncertainty of existence; performing primary collision detection on the obstacle to obtain a primary collision detection result; under the condition that the primary collision detection result is used for representing the collision risk between the automatic driving vehicle and the obstacle, carrying out secondary collision detection on the obstacle to obtain a secondary collision detection result; and determining the obstacle as a target risk obstacle under the condition that the secondary collision detection result is determined to be used for representing the collision risk between the automatic driving vehicle and the obstacle. The scheme improves the processing capability aiming at the collision risk of the obstacle, and particularly effectively improves the safety of the automatic driving vehicle under the condition of multi-sensor fusion.

Description

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

Technical Field

The present disclosure relates to the field of computer technology, and in particular to the field of autopilot, and in particular to a risk obstacle determination method, apparatus, autopilot vehicle, electronic device, storage medium, and program product.

Background

During the running of an autonomous vehicle, a problem of collision with an obstacle may occur. Therefore, the autonomous vehicle generally determines whether the autonomous vehicle and the obstacle have collision risk according to the motion information of the autonomous vehicle and the motion information of the obstacle, and re-plans the driving track of the autonomous vehicle, so that the autonomous vehicle can safely drive.

Disclosure of Invention

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

According to an aspect of the present disclosure, there is provided a risk obstacle determining method including:

determining an obstacle with uncertainty of existence;

performing primary collision detection on the obstacle to obtain a primary collision detection result;

under the condition that the primary collision detection result is used for representing the collision risk between the automatic driving vehicle and the obstacle, carrying out secondary collision detection on the obstacle to obtain a secondary collision detection result; and

and determining the obstacle as a target risk obstacle under the condition that the secondary collision detection result is determined to be used for representing the collision risk between the automatic driving vehicle and the obstacle.

According to another aspect of the present disclosure, there is provided a risk obstacle determining apparatus including:

an obstacle determination module for determining an obstacle whose presence is uncertain;

the first-stage detection module is used for carrying out first-stage collision detection on the obstacle to obtain a first-stage collision detection result;

the secondary detection module is used for carrying out secondary collision detection on the obstacle under the condition that the primary collision detection result is used for representing the collision risk between the automatic driving vehicle and the obstacle, so as to obtain a secondary collision detection result; and

and the risk determination module is used for determining the obstacle as a target risk obstacle under the condition that the secondary collision detection result is used for representing the collision risk between the automatic driving vehicle and the obstacle.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as disclosed herein.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer as described above to perform a method as disclosed herein.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements a method as disclosed herein.

According to another aspect of the present disclosure, an autonomous vehicle is provided comprising an electronic device as disclosed herein.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1A schematically illustrates a scenario in which a risk obstacle determination method and apparatus may be applied according to an embodiment of the present disclosure;

FIG. 1B schematically illustrates a block diagram of an autonomous vehicle according to an embodiment of the present disclosure;

fig. 2 schematically illustrates a flow chart of a risk obstacle determination method according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a flow chart of determining a target area according to an embodiment of the disclosure;

FIG. 4 schematically illustrates a schematic diagram of primary collision detection in accordance with an embodiment of the present disclosure;

FIG. 5 schematically illustrates a schematic diagram of secondary collision detection according to an embodiment of the present disclosure;

FIG. 6 schematically illustrates a flow chart of a risk obstacle determination method according to another embodiment of the disclosure;

fig. 7 schematically shows a block diagram of a risk obstacle determining device according to an embodiment of the disclosure; and

fig. 8 schematically illustrates a block diagram of an electronic device adapted to implement a method of risk obstacle determination according to an embodiment of the disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing, applying and the like of the personal information of the user all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public order harmony is not violated.

In the technical scheme of the disclosure, the authorization or consent of the user is obtained before the personal information of the user is obtained or acquired.

In the public road environment, in order to enable an autonomous vehicle to run safely and comfortably, a plurality of sensors of different types are generally disposed, and the sensing data of the plurality of sensors are fused to realize the sensing of the surrounding environment of the autonomous vehicle. Thereby determining the obstacles faced during the travel of the autonomous vehicle. However, in special weather such as night, rain and fog, or in the face of irregular obstacles, or in the face of a scene such as a blocked obstacle, detection omission or false detection of the obstacle is caused, thereby affecting the accuracy and precision of obstacle detection. Therefore, for the obstacle with uncertain existence, such as the obstacle with unstable existence or abnormal existence, the cloud server can be reported to monitor, remotely control the automatic driving vehicle, and reduce the occurrence of safety accidents.

At present, the detection range of the sensor is improved, so that the surrounding environment perceived by an automatic driving vehicle is generally panoramic, and the detected obstacles with uncertain existence are relatively more, so that the reporting frequency is increased, and the remote processing capacity of the obstacles with collision risks is affected.

In view of this, an embodiment of the present disclosure provides a risk obstacle determining method, including: determining an obstacle with uncertainty of existence; performing primary collision detection on the obstacle to obtain a primary collision detection result; under the condition that the primary collision detection result is used for representing the collision risk between the automatic driving vehicle and the obstacle, carrying out secondary collision detection on the obstacle to obtain a secondary collision detection result; and determining the obstacle as a target risk obstacle under the condition that the secondary collision detection result is determined to be used for representing the collision risk between the automatic driving vehicle and the obstacle.

Fig. 1A schematically illustrates a scenario in which a risk obstacle determination method and apparatus may be applied according to an embodiment of the present disclosure.

It should be noted that fig. 1A illustrates only an example of a system architecture in which embodiments of the present disclosure may be applied to assist those skilled in the art in understanding the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios. For example, in another embodiment, an exemplary system architecture to which the risk obstacle determining method and apparatus may be applied may include a terminal device, but the terminal device may implement the risk obstacle determining method and apparatus provided by the embodiments of the present disclosure without interaction with a server.

As shown in fig. 1A, a system architecture 100 according to this embodiment may include an autonomous vehicle 101, a network 102, and a server 103. Autonomous vehicle 101 may be communicatively coupled to one or more servers 103 through 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 cluster of servers, such as a cloud server, an application server, a backend 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, a location server, or the like. For example, the server 103 receives obstacle information transmitted from the automated guided vehicle 101, and remotely controls the travel of the automated guided vehicle 101 or avoidance of an obstacle based on the obstacle information.

The autonomous vehicle 101 may be a vehicle that is configured to operate in an autonomous mode. But is not limited thereto. The autonomous vehicle may also operate in a manual mode, in a full-automatic driving mode, or in a partially automatic driving mode.

It should be understood that the number of autonomous vehicles, networks, and servers in fig. 1A is merely illustrative. There may be any number of autonomous vehicles, networks, and servers, as desired for implementation.

Fig. 1B schematically illustrates a block diagram of an autonomous vehicle according to an embodiment of the present disclosure. As shown in fig. 1B, the autonomous vehicle 101 may include: an in-vehicle terminal 1011, a perception module 1012, a collision detection module 1013, and a path planning module 1014. The autonomous vehicle 101 may also include common components included in common vehicles, such as: engines, wheels, steering wheels, transmissions, etc. The common components may be controlled by the vehicle-mounted terminal and the vehicle control module using various communication instructions, such as: acceleration instructions, deceleration instructions, steering instructions, braking instructions, and the like.

The various modules in autonomous vehicle 101 may be communicatively coupled to each other via an interconnect, bus, network, or combination thereof. For example, they may 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.

The in-vehicle 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 regarding the location of the autonomous vehicle. The IMU unit may sense position and orientation changes of the autonomous vehicle based on inertial acceleration. The radar unit may represent a system that utilizes radio signals to sense obstacles within the surrounding environment of the autonomous vehicle. In addition to sensing an obstacle, the radar unit may additionally sense the speed and/or heading of the obstacle. The LIDAR unit may use a laser to sense obstacles in the environment of the autonomous vehicle. 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 surroundings of the autonomous vehicle. The camera may be a still camera and/or a video camera. The camera may be mechanically movable, for example, by mounting the camera on a rotating or tilting platform.

The in-vehicle terminal 1011 may also include other sensors such as: sonar sensors, infrared sensors, steering sensors, speed sensors, throttle sensors, brake sensors, and audio sensors (e.g., microphones). The audio sensor may be configured to collect sound from an environment surrounding the autonomous vehicle. The steering sensor may be configured to sense a steering angle of a steering wheel, wheels of an autonomous vehicle, or a combination thereof. The throttle sensor and the brake sensor sense a throttle position and a brake position of the autonomous vehicle, respectively. In some cases, the throttle sensor and the brake sensor may be integrated as an integrated throttle/brake sensor.

The vehicle-mounted terminal 1011 can acquire the automated driving vehicle information, obstacle information, map information, and surrounding sensing data such as a signal lamp or a sign of the automated driving vehicle 101 itself.

The perception module 1012 may receive a plurality of perception data of different types, such as image data, point cloud data, etc., from the in-vehicle terminal 1011. And fusion processing can be carried out on the plurality of perception data to obtain the target area and the obstacle information. An obstacle whose existence is uncertain may also be determined from the species based on the obstacle information and the target area.

The collision detection module 1013 may receive the obstacle information of the obstacle whose existence is uncertain, which is transmitted from the sensing module 1012, and determine whether there is a collision risk between the obstacle and the autonomous vehicle based on the obstacle information of the obstacle whose existence is uncertain. And reporting the obstacle information to a cloud server under the condition that the collision risk is determined. In the event that no collision risk is determined, the obstacle information is transmitted to the path planning module 1014.

The path planning module 1014 may receive the obstacle information of the obstacle determined by the existence sent by the sensing module 1012, and perform path planning based on the obstacle information of the obstacle determined by the existence, to obtain planned path information.

It should be noted that the autonomous vehicle 101 may further include a vehicle control module and a wireless communication module.

The vehicle control modules may include, but are not limited to, a steering unit, a throttle unit (also referred to as an acceleration unit), and a braking unit. The vehicle control module may receive the planned path information of the path planning module and control the steering unit, the throttle unit, the braking unit, and the like 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 and thus the speed and acceleration of the autonomous vehicle. The brake unit decelerates the autonomous vehicle by providing friction to slow down the wheels or tires of the autonomous vehicle.

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 may communicate wirelessly with one or more devices directly or via a communication network, e.g., with a server over a network. The wireless communication module may use any cellular communication network or Wireless Local Area Network (WLAN), for example, using WiFi, to communicate with another component or module. The user interface module may be part of peripheral devices implemented within the autonomous vehicle including, for example, a keyboard, a touch screen display device, a microphone, a speaker, and the like.

It should be noted that the sequence numbers of the respective operations in the following methods are merely representative of the operations for the purpose of description, and should not be construed as representing the order of execution of the respective operations. The method need not be performed in the exact order shown unless explicitly stated.

Fig. 2 schematically shows a flowchart of a risk obstacle determination method according to an embodiment of the present disclosure.

As shown in fig. 2, the method includes operations S210 to S240.

In operation S210, an obstacle whose existence is uncertain is determined.

In operation S220, a first-stage collision detection is performed on the obstacle, and a first-stage collision detection result is obtained.

In operation S230, in the case where it is determined that the primary collision detection result is used to characterize the collision risk between the autonomous vehicle and the obstacle, the secondary collision detection is performed on the obstacle, and a secondary collision detection result is obtained.

In operation S240, in the case where it is determined that the secondary collision detection result is used to characterize the collision risk between the autonomous vehicle and the obstacle, the obstacle is determined to be a target risk obstacle.

According to embodiments of the present disclosure, an obstacle whose presence is uncertain characterizes an obstacle whose presence is unstable or whose presence is abnormal, such as a vehicle with open doors, a profiled vehicle, a semitrailer, or a vehicle with cargo pulled, etc. For an obstacle with an uncertain existence, it is necessary to further judge whether there is a risk of collision between the autonomous vehicle and the obstacle. If collision risk exists, the obstacle information can be reported to the cloud server. The cloud server receives the obstacle information, and remotely controls the automatic driving vehicle to perform obstacle avoidance processing so that the automatic driving vehicle can safely and comfortably run. For an obstacle with a determined existence, such as an automatic driving vehicle in running, the automatic driving system can directly plan the running path of the automatic driving vehicle based on the obstacle information of the obstacle, so as to control the automatic driving vehicle to avoid the obstacle, and enable the automatic driving vehicle to run safely.

According to embodiments of the present disclosure, after determining that an obstacle with an uncertain presence is present, the autonomous vehicle may not directly report to the cloud server. But rather performs a first order collision detection of the obstacle. Performing a first-level collision detection on the obstacle, the obtaining a first-level collision detection result may include: the respective speeds of the autonomous vehicle and the obstacle are determined. A primary collision detection result is determined based on the respective speeds of the autonomous vehicle and the obstacle. But is not limited thereto. The primary collision detection result may also be determined based on the acceleration and the speed of the autonomous vehicle and the obstacle, respectively.

According to an embodiment of the present disclosure, a secondary collision detection is performed on an obstacle in case it is determined that a primary collision detection result is used to characterize a collision risk between an autonomous vehicle and the obstacle. Determining whether there is a risk of collision between the autonomous vehicle and the obstacle based on the distance between the autonomous vehicle and the obstacle, and if the distance is less than a threshold value, indicating that the obstacle is close to the autonomous vehicle, a collision may occur. And therefore, determining the obstacle as a target risk obstacle, storing obstacle information of the target risk obstacle in a list, and reporting the list to a cloud server.

According to an embodiment of the present disclosure, in a case where it is determined that the primary collision detection result or the secondary collision detection result is used to characterize that there is no collision risk between the autonomous vehicle and the obstacle, respectively, it may be determined that the obstacle is a non-target risk obstacle. The obstacle information for the obstacle may be transmitted to a path planning layer of the autonomous vehicle. And carrying out obstacle avoidance processing on the obstacle based on the obstacle information by the path planning layer.

According to the embodiment of the disclosure, two-stage collision risk detection is performed for the obstacle with uncertain existence, and reporting is performed again under the condition that the obstacle is determined to be the target risk obstacle, so that reporting frequency is reduced, data processing capacity of the cloud server is reduced, remote processing capacity for the obstacle is improved, and safety of an automatic driving vehicle is effectively improved.

According to an embodiment of the present disclosure, determining an obstacle whose existence is uncertain may include the following operations with respect to operation S210 as shown in fig. 2.

For example, a target area is determined based on the planned path information. An initial obstacle with an uncertainty in its presence is determined. In the case where it is determined that the initial obstacle is located within the target area, the obstacle is determined based on the initial obstacle.

According to an embodiment of the present disclosure, a target area is determined based on planned path information. The target area may characterize an area where the autonomous vehicle may be at risk of collision. The initial obstacles include obstacles whose presence within the target area is uncertain, and obstacles whose presence on or outside the target area is uncertain. In the case where it is determined that the initial obstacle is located within the target area, the initial obstacle may be regarded as an obstacle. From this, an obstacle for which collision detection is required is determined. Initial obstacles outside the target area and on the target area are filtered out through initial screening of the initial obstacles, initial obstacles in the target area are left, the initial obstacles are used as the obstacles for subsequent primary collision detection and secondary collision detection, data processing capacity is reduced to a certain extent, and detection efficiency is effectively improved.

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

For example, a target path point is determined based on the planned path information. 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 perceived data. The prescribed road boundary information is acquired from map data. The target boundary information is determined from the perceived road boundary information, the prescribed road boundary information, and the predetermined safety boundary information. Based on the target boundary information, a target region is determined.

According to an embodiment of the present disclosure, determining a target path point based on planned path information may include the operations of: the waypoints are determined from the planned path information. The path distance between the path point and the current track point of the automatic driving vehicle is greater than the obstacle detection distance threshold. And determining a target path point based on the current track point and the path point of the automatic driving vehicle.

According to an embodiment of the present disclosure, determining a target path point based on a current track point, a path point of an autonomous vehicle may include: and carrying out interpolation processing on the path points and the current track points of the automatic driving vehicle to obtain target path points. Because the distance between the path points and the current track points of the automatic driving vehicle in the planning path information is generally larger than the obstacle detection distance threshold value, interpolation processing is carried out on the path points so that the path points are dense, and the distance between the path points is approximate to or smaller than the obstacle detection distance threshold value. The target path points can accurately and effectively divide the target area.

According to other embodiments of the present disclosure, in a case where a plurality of route points in the planned route information are denser, for example, in a case where a route distance between the plurality of route points and a current trajectory point of the autonomous vehicle is smaller than an obstacle detection distance threshold, the route point may be directly taken as the target route point. And will not be described in detail herein.

According to an embodiment of the present disclosure, the perceived road boundary information is obtained from perceived data acquired by a sensor, the perceived data including obstacle information of the road edge. For example: the sensed data includes position information of a fence or a guardrail provided at the edge of the road. The prescribed road boundary information is acquired from map data. The prescribed road boundary information includes, for example, a boundary line, a lane line, and the like of road travel determined based on traffic rules. The predetermined safety boundary information is determined based on the width of the autonomous vehicle itself, and may be determined by forming two boundary lines based on one half vehicle width or one vehicle width with the planned path information as a center line.

According to an embodiment of the present disclosure, boundary information nearest to a target waypoint is determined from among perceived road boundary information, prescribed road boundary information, and predetermined safety boundary information, and is determined as target boundary information. The target area is partitioned based on the target boundary information. And the boundary information closest to the automatic driving vehicle is selected as target boundary information, so that the automatic driving vehicle is more accurate in the collision detection process, and the screening precision of the obstacle is effectively improved.

Fig. 3 schematically illustrates a flow chart of determining a target area according to an embodiment of the disclosure.

As shown in FIG. 3, the method includes operations S310-S350.

In operation S310, interpolation processing is performed on the current track point of the autonomous vehicle and the path point in the planned path information to obtain a target path point.

In operation S320, predetermined safety boundary information about each target waypoint is determined.

In operation S330, perceived road boundary information of each target route point is determined, boundary information closest to the target route point is determined from the predetermined safety boundary information and the perceived road boundary information, and stored.

In operation S340, map road boundary information of each target waypoint is determined, and boundary information closest to the target waypoint is determined from the map road boundary information and the stored boundary information, thereby obtaining target boundary information.

In operation S350, a target region is determined based on the target boundary information.

According to embodiments of the present disclosure, determining an initial obstacle whose presence is uncertain may include the following operations.

For example, the type of obstacle to be identified is determined based on the historical perceived data sequence. The type is used to characterize whether the obstacle to be identified is an obstacle whose presence is uncertain. Based on the type, an initial obstacle is determined from the obstacles to be identified.

According to an embodiment of the present disclosure, the history sensing data sequence includes data obtained by arranging sensing data of a plurality of history times in time series. The sensing data for each historical moment may include data collected by a plurality of sensors. For example, the perception data includes image data and point cloud data. In the case where the type of the obstacle to be identified is determined to be an obstacle whose existence is not determined, the obstacle to be identified is determined to be an initial obstacle, and in the case where the type of the obstacle to be identified is determined to be an obstacle whose existence is determined, the obstacle to be identified may be determined to be a known obstacle. And sending the known obstacle to a 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 perceived data sequence may include the following operations.

For example, based on the obstacle category sequence, a category detection result is determined. Based on the position sequence, a position detection result is determined. Based on the velocity sequence, a velocity detection result is determined. And determining the type of the obstacle to be identified based on the category detection result, the position detection result and the speed detection result.

According to embodiments of the present disclosure, the historical awareness data sequence may include an obstacle category sequence, a location sequence, and a speed sequence. But is not limited thereto. May also include at least one or both of the following: a sequence of obstacle categories, a sequence of positions, and a sequence of speeds.

According to the embodiment of the disclosure, for the obstacle category sequence, image information of the obstacle to be identified can be acquired through monitoring equipment and input into a trained image identification neural network, and the category of the obstacle to be identified is determined. And storing the identification result corresponding to each historical moment into the obstacle category sequence. For the position sequence, the position of the obstacle to be identified can be tracked through the radar, so that the position information of the obstacle to be identified at each historical moment is obtained, and the position information is stored in the position sequence. For the speed sequence, the speed sensor is used for collecting the movement speed corresponding to each historical moment of the obstacle to be identified, and the movement speed is stored in the speed sequence.

According to embodiments of the present disclosure, a category detection result may be determined based on the obstacle category sequence. For example, based on the category corresponding to each history time in the obstacle category sequence, the category stability is determined, and the category stability is used as the category detection result. Specifically, the category stability is determined by determining the difference between the categories of obstacles corresponding to each historical moment. For example: 10:00 recognition obstacle category is dog, 10:01 recognize that the obstacle type is umbrella, confirm that the type stability of obstacle is lower.

According to embodiments of the present disclosure, a location detection result may be determined based on a location sequence. For example, the position stability is determined based on the positions corresponding to the respective history times in the position sequence, 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 value of the position at each historical time. For example: 10:00 and 10:01 is 1 m, 10;01 and 10: the difference between 02 is also 1 meter; 10:02 and 10: the difference value between 03 is 1 meter, which indicates that the movement process of the obstacle is stable and the stability is higher. A position fluctuation threshold may be set to measure position stability, for example, a fluctuation between differences of up to 5 meters is set, and the position stability of the obstacle is determined to be low.

According to embodiments of the present disclosure, the speed detection result may be determined based on the speed sequence. For example, the speed stability is determined based on the speeds corresponding to the respective historic times in the speed sequence, and the speed stability is used as the speed detection result. Specifically, by determining the movement speed of the obstacle at each history, for example: 10: the movement speed of 00 obstacle is 48km/h,10: the movement speed of the 01 obstacle is also 48km/h,10: and the movement speed of the 03 obstacle is 48km/h, so that the movement stability of the obstacle is higher. A speed fluctuation threshold value may be set to measure the position stability, for example, the fluctuation value of the speed between two adjacent moments reaches a threshold value of 1km/h, and the position stability of the obstacle is determined to be low.

According to an embodiment of the present disclosure, determining the type of the obstacle to be identified based on the category detection result, the position detection result, and the speed detection result may include: determining the target stability based on the category stability, the position stability, and the speed stability determines the type of obstacle to be identified based on the target stability. For example, the category stability, the position stability, and the speed stability are weighted and summed, respectively, to obtain the target stability. For example, a higher stability value is assigned to 1, a lower stability value is assigned to-1, a corresponding weight is set for each importance of stability, and the weights are summed to obtain the target stability. The higher the target stability is, the more stable the obstacle is, the more the obstacle to be identified is determined to be the obstacle with the determined existence if the target stability is larger than the preset threshold value, and the more the obstacle to be identified is determined to be the obstacle with the uncertain existence if the target stability is smaller than the preset threshold value.

For example: setting the weight of category stability as 6, the weight of position stability as 2 and the weight of speed stability as 2. Assuming that the preset threshold is 5, determining that the class stability is assigned 1, the speed stability is assigned-1 and the position stability is assigned-1 based on the class stability, determining that the corresponding assigned value of the target stability is 2, and determining that the obstacle to be identified is an obstacle with uncertain existence if the assigned value is smaller than the preset threshold.

According to the embodiment of the disclosure, the type of the obstacle to be identified is determined based on the combination of the obstacle category sequence, the position sequence and the speed sequence in the historical perception data sequence, so that the multi-perception data can be fused to a certain extent, and multi-perception fusion data can be obtained. Thereby improving the accuracy of determining whether the obstacle to be identified is an initial obstacle whose existence is uncertain.

According to the embodiment of the disclosure, the risk obstacle determination method improves the processing capability of collision risk of the obstacle, and particularly effectively improves the safety of an automatic driving vehicle under the condition of multi-sensor fusion.

According to the embodiments of the present disclosure, in the case where an initial obstacle is determined to be an obstacle whose existence within a target area is uncertain, the obstacle may be initially determined to be an obstacle whose attribute is unstable. There is a certain false detection problem. A first level collision detection may be performed on such an obstacle to thereby determine whether there is a risk of collision between the obstacle and the autonomous vehicle. Therefore, the reporting frequency of reporting to the cloud server is reduced, and the safety of automatic driving is improved.

According to an embodiment of the present disclosure, for operation S220 as shown in fig. 2, the first-stage collision detection of the obstacle, to obtain a first-stage collision detection result, may include the following operations.

For example, the speed of movement of the obstacle in the direction of the planned path is determined. And obtaining a primary collision detection result based on the running speed of the automatic driving vehicle and the movement speed of the obstacle.

According to the embodiment of the disclosure, when the running speed of the autonomous vehicle is less than or equal to the movement speed of the obstacle, the obtained primary collision detection result is used for representing that no collision risk exists between the autonomous vehicle and the obstacle, the obstacle can be used as a known obstacle, and the obstacle information of the obstacle can be transmitted to the path planning module. And under the condition that the running speed of the automatic driving vehicle is greater than the movement speed of the obstacle, the obtained primary collision detection result is used for representing that the collision risk exists between the automatic driving vehicle and the obstacle, and the secondary collision detection is carried out on the obstacle.

According to an embodiment of the present disclosure, determining a movement speed of an obstacle in a planned path direction may include: the movement speed of the obstacle is decomposed into a movement speed parallel to the direction of the planned path and a movement speed perpendicular to the direction of the planned path. Therefore, the obstacle and the automatic driving vehicle can be compared in the same movement direction uniformly, and the obtained primary collision detection result is visual, simple and effective.

According to an embodiment of the present disclosure, determining a movement speed of an obstacle in a planned path direction may include the operations of: a second target road segment is determined from the plurality of target waypoints based on the current location of the obstacle. The plurality of target waypoints are determined based on planned path information of the autonomous vehicle. The movement speed of the obstacle in the direction of the planned path is determined based on the second target road segment and the movement speed of the obstacle in the world coordinate system.

Fig. 4 schematically illustrates a schematic diagram of primary collision detection according to an embodiment of the present disclosure.

As shown in fig. 4, the route AB is a planned route determined based on planned route information of the autonomous vehicle, the left and right two dotted lines of the planned route are target boundaries determined based on target boundary information of the autonomous vehicle, and the region located within the target boundaries is a target region. Based on the historical perceptual data sequence, an obstacle located within the target region is determined to be an obstacle P whose presence is uncertain.

The second target road segment may be determined from a plurality of target path points based on the current position of the obstacle P. As shown in fig. 4, the solid points on the route AB are targetsAnd (5) a path point. Determining two target path points d closest to the obstacle P based on the current position of the obstacle P ₁ And d ₂ Will d ₁ And d ₂ The road section in between serves as a second target road section. The driving direction between the second target road sections is the planned path direction.

As shown in fig. 4, the obstacle P moves at a velocity v ₁ In world coordinate system, for example, in X-axis and Y-axis coordinate system, is (v) _x ，v _y ). Can be to v ₁ Performing a decomposition into velocities v in a direction parallel to the planned path _p And velocity v in a direction perpendicular to the planned path _m . Thereby determining the movement velocity v of the obstacle P in the direction of the planned path _p The conversion formula may be as follows:

according to an embodiment of the present disclosure, for operation S230 shown in fig. 2, the secondary collision detection is performed on the obstacle, and a secondary collision detection result is obtained, which may include the following operations.

For example, a longitudinal spacing in the direction of the planned path between the autonomous vehicle and the obstacle after a predetermined reaction period is determined. The length of time that can be travelled in the direction of the planned path is determined on the basis of the longitudinal distance, the speed of travel of the autonomous vehicle and the speed of movement of the obstacle. Based on the drivable time period, a target position of the obstacle is determined. And obtaining a secondary collision detection result based on the target position of the obstacle.

According to an embodiment of the present disclosure, the predetermined reaction time period of the autonomous vehicle is determined according to the reaction sensitivity of the autonomous vehicle, and may be set to 1s, for example. The automatic driving vehicle still runs along the direction of the planned path and the planned running speed within the preset reaction time, and the longitudinal distance between the automatic driving vehicle and the obstacle in the path planning direction after the preset reaction time is run is determined. The travelable duration is determined based on the longitudinal distance, the travel speed and the movement speed of the obstacle. The drivable time period can be understood as a time period when the autonomous vehicle is flush with the obstacle in the planned path direction under the condition that the autonomous vehicle is decelerating after the predetermined reaction time period. The autonomous vehicle travels at a reduced speed for a period of time during which the longitudinal distance between the autonomous vehicle and the obstacle in the direction of the planned path gradually decreases until it is longitudinally level. The target position of the obstacle is thus determined based on the length of time that can be travelled, and it is further determined based on the target position whether there is a risk of collision of the autonomous vehicle with the obstacle.

According to the embodiment of the disclosure, in the process of secondary collision detection, the preset reaction time length and the drivable time length in the direction of the planned path are considered, so that the processing precision can be improved while the actual real situation is combined.

According to an embodiment of the present disclosure, determining a length of time that can be exercised in a planned path direction based on a longitudinal distance, a travel speed of an autonomous vehicle, and a movement speed of an obstacle may include the operations of: based on the longitudinal distance, the travel speed of the autonomous vehicle and the movement speed of the obstacle, a maximum acceleration of the autonomous vehicle for braking is determined. In the case where it is determined that the maximum acceleration is greater than the acceleration threshold value, the exercisable duration is determined based on the acceleration threshold value and the longitudinal spacing.

According to the embodiment of the present disclosure, the determined maximum acceleration is an acceleration required to be able to stop a collision not occurring in the planned path direction, based on the longitudinal pitch, the running speed of the autonomous vehicle, and the movement speed of the obstacle. The acceleration threshold is then the ultimate acceleration within the range of the device performance of the autonomous vehicle. When the maximum acceleration is greater than the acceleration threshold, a travelable time period 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 value, the travelable period may be determined based on the maximum acceleration and the longitudinal distance. The length of the exercisable period may include: the quotient of the longitudinal distance and the maximum acceleration or acceleration threshold value.

According to the embodiment of the disclosure, the maximum acceleration and the acceleration threshold value are compared, so that the drivable time length is determined based on the smaller value, and the detection accuracy of the secondary collision detection is improved in combination with the actual detection.

According to an embodiment of the present disclosure, obtaining a secondary collision detection result based on a target position of an obstacle may include the following operations: based on the target position of the obstacle, a target sub-position of each of a plurality of edge points of the obstacle is determined. For each target sub-position, a first target road segment is determined from a plurality of target path points, and a plurality of first target road segments are obtained. The plurality of target waypoints are determined based on planned path information of the autonomous vehicle. A plurality of lateral distances is determined based on the plurality of first target segments and the plurality of target sub-locations. The lateral distance is the vertical distance between the target sub-position and the first target segment that matches the target sub-position. And obtaining a secondary collision detection result based on the transverse distances and the transverse distance threshold values.

According to embodiments of the present disclosure, the movement speed of the obstacle may be decomposed into a planned path direction and a direction perpendicular to the planned path direction. In case the obstacle and the autonomous vehicle reach flush in the direction of the planned path, it is possible that the lateral distance between the obstacle and the autonomous vehicle in the direction perpendicular to the planned path is already far apart, above the lateral distance threshold, in which case there is no risk of collision between the obstacle and the autonomous vehicle. And under the condition that the obstacle and the automatic driving vehicle reach the level in the direction of the planned path and the transverse distance between the obstacle and the automatic driving vehicle in the direction perpendicular to the planned path is smaller than or equal to the transverse distance threshold value, the collision risk exists between the obstacle and the automatic driving vehicle.

According to other embodiments of the present disclosure, the lateral spacing between the target position of the obstacle and the target position of the autonomous vehicle may be determined based on the two. But is not limited thereto. The center position of the obstacle may also be determined based on the target position of the obstacle, and the lateral distance may be determined based on the center position and the first target segment that matches the center position. The first target road segment matched with the center position is a road segment formed by two target road points closest to the center position among the plurality of target road points.

According to the embodiment of the disclosure, the plurality of edge points of the obstacle are all calculated, so that the plurality of transverse distances are determined, and the secondary collision detection result can be comprehensive and accurate. In addition, by using a plurality of target path points as estimated positions of the automatic driving vehicle, the data processing amount can be reduced and the processing efficiency can be improved while the secondary collision detection precision is ensured.

Fig. 5 schematically illustrates a schematic diagram of a secondary collision detection according to an embodiment of the present disclosure.

As shown in fig. 5, a route AB is a planned route determined based on planned route information of an autonomous vehicle, and the autonomous vehicle travels to a route point d0 to perform secondary collision detection on an obstacle P traveling to a position S. The longitudinal distance L between the autonomous vehicle and the obstacle P in the direction of the planned path after the predetermined reaction time period is determined. The length of time that can be exercised in the direction of the planned path is determined based on the longitudinal distance, the travel speed of the autonomous vehicle, and the movement speed of the obstacle P. Based on the drivable time period, the target position E of the obstacle P is determined.

As shown in fig. 5, the target sub-positions of the four edge points of the obstacle P are determined based on the target position E where the obstacle P is located. And determining a first target segment that matches each of the four edge points. For example, a first target segment formed by target path points d3 and d4, which matches edge point P1. Based on the target sub-position and the first target segment matching the target sub-position, a lateral distance, e.g. a lateral distance H between the edge point P1 and the first target segment, is determined. A plurality of lateral pitches are obtained. The minimum lateral spacing is determined from the plurality of lateral spacings. The minimum lateral spacing is the lateral spacing H. And comparing the transverse distance H with a transverse distance threshold, and determining that no collision risk exists between the obstacle and the automatic driving vehicle under the condition that the transverse distance H is larger than the transverse distance threshold. In the event that the lateral spacing H is determined to be less than or equal to the lateral spacing threshold, a risk of collision between the obstacle and the autonomous vehicle is determined.

In accordance with an embodiment of the present disclosure, assuming a longitudinal spacing of L,based on the speed v of the autonomous vehicle _c And the movement velocity v of the obstacle _p The maximum acceleration A of the automatic driving vehicle running at the current speed is determined, and the calculation formula is as follows:

According to an embodiment of the present disclosure, it is determined whether the maximum acceleration a is greater than an acceleration threshold Ta, the setting of which is related to the braking performance of the autonomous vehicle. If the acceleration is larger than the threshold value Ta, the maximum acceleration of the automatic driving vehicle for stopping is determined. If not, the maximum acceleration of the autonomous vehicle for braking is A.

According to an embodiment of the present disclosure, in the case where it is determined that the maximum acceleration is greater than the acceleration threshold value, the exercisable duration is determined based on the acceleration threshold value and the longitudinal spacing. The calculation formula is as follows:

wherein Ta represents an acceleration threshold value, T _r Indicating a predetermined reaction time period, T, of the autonomous vehicle _c Representing the length of time that can be exercised.

According to the embodiment of the present disclosure, the position of the obstacle (x ₀ ，y ₀ ) Can determine the target position (x _n ，y _n ) The calculation formula is as follows:

fig. 6 schematically illustrates a flowchart of a risk obstacle determination method according to another embodiment of the present disclosure.

As shown in fig. 6, the method includes the following operations S601 to S610.

In operation S601, an obstacle whose existence is uncertain is determined.

In operation S602, it is determined whether the movement speed of the obstacle in the direction of the planned path is less than the running speed of the autonomous vehicle. In the case where the moving speed is less than the traveling speed, operation S603 is performed, and conversely, operation S610 is performed.

In operation S603, a longitudinal distance in a planned path direction between the autonomous vehicle and the obstacle after a predetermined reaction period is determined.

In operation S604, a maximum acceleration of the autonomous vehicle for braking is determined based on the longitudinal distance, the traveling speed of the autonomous vehicle, and the movement speed of the obstacle.

In operation S605, it is determined whether the maximum acceleration is greater than an acceleration threshold value. In the case where the maximum acceleration is greater than the acceleration threshold value, operation S6061 is performed. In the case where the maximum acceleration is less than or equal to the acceleration threshold value, operation S6062 is performed.

In operation S6061, the exercisable duration is determined based on the acceleration threshold value and the longitudinal spacing.

In operation S6062, the travelable duration is determined based on the maximum acceleration and the longitudinal distance.

In operation S607, the target position of the obstacle is determined based on the exercisable duration.

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. In the case where it is determined that the lateral spacing is less than or equal to the lateral spacing threshold, operation S609 is performed. Otherwise, operation S610 is performed.

In operation S609, it is determined that there is a risk of collision between the obstacle and the automatically driven vehicle.

In operation S610, it is determined that there is no risk of collision between the obstacle and the automatically driven vehicle.

Fig. 7 schematically shows a block diagram of a risk obstacle determination device according to an embodiment of the disclosure.

As shown in fig. 7, a risk obstacle determining apparatus 700 of this embodiment includes an obstacle determining module 710, a primary detecting module 720, a secondary detecting module 730, and a risk determining module 740.

The obstacle determination module 710 is configured to determine an obstacle whose presence is uncertain. In an embodiment, the obstacle determining module 710 may be configured to perform the operation S210 described above, which is not described herein.

The primary detection module 720 is configured to perform primary collision detection on the obstacle, so as to obtain a primary collision detection result. In an embodiment, the primary detection module 720 may be configured to perform the operation S220 described above, which is not described herein.

The secondary detection module 730 is configured to perform secondary collision detection on the obstacle to obtain a secondary collision detection result when it is determined that the primary collision detection result is used to represent a collision risk between the autonomous vehicle and the obstacle. In an embodiment, the secondary detection module 730 may be used to perform the operation S230 described above, which is not described herein.

The risk determination module 740 is configured to determine that the obstacle is a target risk obstacle if it is determined that the secondary collision detection result is used to characterize a collision risk between the autonomous vehicle and the obstacle. In an embodiment, the risk determination module 740 may be configured to perform the operation S240 described above, which is not described herein.

According to an embodiment of the present disclosure, the obstacle determination module 710 includes a region determination sub-module, and an obstacle determination sub-module.

And the area determination submodule is used for determining a target area based on the planned path information.

An initial determination sub-module for determining an initial obstacle for which the presence is uncertain.

And an obstacle determination sub-module for determining an obstacle based on the initial obstacle in the case where it is determined that the initial obstacle is located within the target area.

According to an embodiment of the present disclosure, the region determination submodule includes a path point determination unit, an information determination unit, a boundary determination unit, and a region determination unit.

And a path point determining unit for determining a target path point based on the planned path information.

And an information determination unit configured to determine perceived road boundary information, which is acquired from perceived data, prescribed road boundary information, which is acquired from map data, and predetermined safety boundary information based on the target path point.

And a boundary determining unit for determining target boundary information from the perceived road boundary information, the prescribed road boundary information, and the predetermined safety boundary information.

And a region determining unit configured to determine a target region based on the target boundary information.

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

And the type determining unit is used for determining the type of the obstacle to be identified based on the historical perception data sequence, wherein the type is used for representing whether the obstacle to be identified is an obstacle with uncertain existence.

An initial determination unit configured to determine an initial obstacle from among the obstacles to be identified based on the type.

According to an embodiment of the present disclosure, the type determining unit includes a category determining subunit, a position determining subunit, a speed determining subunit, and a type determining subunit.

And the category determining subunit is used for determining a category detection result based on the obstacle category sequence.

And a position determining subunit for determining a position detection result based on the position sequence.

A speed determination subunit for determining a speed detection result based on the speed sequence.

And a type determining subunit for determining the type of the obstacle to be identified based on the category detection result, the position detection result and the speed detection result.

According to an embodiment of the present disclosure, the primary detection module 720 includes a motion determination sub-module and a first result determination sub-module.

And the motion determination submodule is used for determining the motion speed of the obstacle in the direction of the planned path.

The first result determining submodule is used for obtaining a first-stage collision detection result based on the running speed of the automatic driving vehicle and the movement speed of the obstacle.

According to an embodiment of the present disclosure, the secondary detection module 730 includes a longitudinal determination sub-module, a duration determination sub-module, a location determination sub-module, and a second result determination sub-module.

And the longitudinal determination submodule is used for determining the longitudinal distance between the automatic driving vehicle and the obstacle in the direction of the planned path after the preset reaction time.

And the duration determination submodule is used for determining the available duration in the direction of the planned path based on the longitudinal distance, the running speed of the automatic driving vehicle and the movement speed of the obstacle.

And the position determining submodule is used for determining the target position of the obstacle based on the driving duration.

And the second result determining submodule is used for obtaining a second-level collision detection result based on the target position of the obstacle.

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

And a brake determining unit for determining a maximum acceleration of the autonomous vehicle for braking based on the longitudinal distance, the traveling speed of the autonomous vehicle, and the movement speed of the obstacle.

And the duration determining unit is used for determining the exercisable duration based on the acceleration threshold value and the longitudinal distance under the condition that the maximum acceleration is determined to be larger than the acceleration threshold value.

According to an embodiment of the present disclosure, the second result determination submodule includes a child position determination unit, a first path segment determination unit, a pitch determination unit, and a second result determination unit.

A sub-position determining unit for determining a target sub-position of each of a plurality of edge points of the obstacle based on the target position of the obstacle.

And the first route segment determining unit is used for determining a first route segment from a plurality of target route points for each target sub-position to obtain a plurality of first target route segments, wherein the plurality of target route points are determined based on planning route information of the automatic driving vehicle.

And the distance determining unit is used for determining a plurality of transverse distances based on the plurality of first target road segments and the plurality of target sub-positions, wherein the transverse distances are vertical distances between the target sub-positions and the first target road segments matched with the target sub-positions.

And the second result determining unit is used for obtaining a secondary collision detection result based on the transverse distances and the transverse distance threshold values.

According to an embodiment of the present disclosure, the motion determination sub-module includes a second road segment determination unit and a motion determination unit.

And a second link determination unit configured to determine a second target link from a plurality of target route points based on the current position of the obstacle, wherein the plurality of target route points are determined based on planned route information of the autonomous vehicle.

And a movement determination unit for determining the movement speed of the obstacle in the planned path direction based on the second target link and the movement speed of the obstacle in the world coordinate system.

According to an embodiment of the present disclosure, the path point determination unit includes a path point determination subunit and a target determination subunit.

And the path point determining subunit is used for determining a path point from the planned path information, wherein the path distance between the path point and the current track point of the automatic driving vehicle is larger than an obstacle detection distance threshold value.

And the target determining subunit is used for determining a target path point based on the current track point and the path point of the automatic driving vehicle.

Any number of modules, sub-modules, units, sub-units, or at least some of the functionality of any number of the sub-units according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented as split into multiple modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as hardware circuitry, such as a field programmable gate array (Field Programmable Gate Array, FPGA), a programmable logic array (Programmable Logic Arrays, PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or in any other reasonable manner of hardware or firmware that integrates or encapsulates circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be at least partially implemented as computer program modules, which when executed, may perform the corresponding functions.

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 combined in one module/unit/sub-unit or any of them may be split into a plurality of modules/units/sub-units. Alternatively, at least some of the functionality of one or more of these modules/units/sub-units may be combined with at least some of the functionality of other modules/units/sub-units and implemented in one 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 implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging the circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware. 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 computer program modules that, when executed, perform the corresponding functions.

It should be noted that, in the embodiments of the present disclosure, the risk obstacle determining device portion corresponds to the risk obstacle determining method portion in the embodiments of the present disclosure, and the description of the risk obstacle determining device portion specifically refers to the risk obstacle determining method portion, which is not described herein.

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

According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as in an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a method as in an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a computer program product comprising a computer program which, when executed by a processor, implements a method as an embodiment of the present disclosure.

According to the embodiment of the present disclosure, an autonomous vehicle configured with the above-described electronic device, which when executed by a processor thereof, can implement the risk obstacle determination method described in the above-described embodiment.

Fig. 8 schematically illustrates a block diagram of an electronic device adapted to implement a method of risk obstacle determination according to an embodiment of the disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 8, the apparatus 800 includes a computing unit 801 that 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. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.

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

The computing unit 801 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of 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 calculation unit 801 performs the respective methods and processes described above, such as a risk obstacle determination method. For example, in some embodiments, the risk obstacle determination method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 800 via ROM 802 and/or 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 may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the risk obstacle determination method by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here 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 a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, 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: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server 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 of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A risk obstacle determination method, comprising:

determining an obstacle with uncertainty of existence;

under the condition that the primary collision detection result is used for representing collision risk between the automatic driving vehicle and the obstacle, carrying out secondary collision detection on the obstacle to obtain a secondary collision detection result; and

2. The method of claim 1, wherein the determining the presence of the uncertain obstacle comprises:

determining a target area based on the planned path information;

determining an initial obstacle with uncertainty in presence; and

in the 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 of claim 2, wherein the determining a target area based on planned path information comprises:

determining a target path point based on the planned path information;

determining perceived road boundary information, prescribed road boundary information, and predetermined safety boundary information based on the target route points, wherein the perceived road boundary information is acquired from perceived data, and the prescribed road boundary information is acquired from map data;

determining target boundary information from the perceived road boundary information, the prescribed road boundary information, and the predetermined safety boundary information; and

And determining the target area based on the target boundary information.

4. The method of claim 2, wherein the determining an initial obstacle of uncertainty of presence comprises:

determining a type of an obstacle to be identified based on a historical perception data sequence, wherein the type is used for representing whether the obstacle to be identified is an obstacle with uncertain existence; and

based on the type, the initial obstacle is determined from the obstacles to be identified.

5. The method of claim 4, wherein the historical sensory data sequence comprises an obstacle category sequence, a location sequence, a speed sequence;

the determining the type of the obstacle to be identified based on the historical perception data sequence comprises the following steps:

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

and determining the type of the obstacle to be identified 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 the first-stage collision detection on the obstacle to obtain a first-stage collision detection result includes:

Determining a movement speed of the obstacle in the direction of the planned path; and

and obtaining the primary collision detection result based on the running speed of the automatic driving vehicle and the movement speed of the obstacle.

7. The method according to any one of claims 1 to 6, wherein performing secondary collision detection on the obstacle to obtain a secondary collision detection result, comprises:

determining a longitudinal distance between the autonomous vehicle and the obstacle in a planned path direction after a predetermined reaction time period;

determining a length of exercisable time in the direction of the planned path based on the longitudinal distance, a travel speed of the autonomous vehicle, and a movement speed of the obstacle;

determining a target position of the obstacle based on the drivable time period; and

and obtaining the secondary collision detection result based on the target position of the obstacle.

8. The method of claim 7, wherein the determining a length of exercisable in the planned path direction based on the longitudinal spacing, a travel speed of the autonomous vehicle, and a speed of movement of the obstacle comprises:

determining a maximum acceleration of the autonomous vehicle for braking based on the longitudinal distance, a travel speed of the autonomous vehicle, and a movement speed of the obstacle; and

In the case where it is determined that the maximum acceleration is greater than an acceleration threshold, the exercisable duration is determined based on the acceleration threshold and the longitudinal spacing.

9. The method according to claim 7 or 8, wherein the obtaining the secondary collision detection result based on the target position of the obstacle includes:

determining a target sub-position of each of a plurality of edge points of the obstacle based on the target position of the obstacle;

determining a first target road section from a plurality of target path points for each target sub-position to obtain a plurality of first target road sections, wherein the plurality of target path points are determined based on planning path information of the automatic driving vehicle;

determining a plurality of lateral distances based on the plurality of first target road segments and the plurality of target sub-positions, wherein the lateral distances are vertical distances between the target sub-positions and the first target road segments matched with the target sub-positions; and

and obtaining the secondary collision detection result based on the transverse distances and the transverse distance threshold values.

10. The method of claim 6, wherein the determining a speed of movement of the obstacle in the planned path direction comprises:

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 vehicle; and

a speed of movement of the obstacle in the direction of the planned path is determined based on the second target segment and the speed of movement of the obstacle in the world coordinate system.

11. A method according to claim 3, wherein said determining a target path point based on said planned path information comprises:

determining a path point from the planned path information, wherein a path distance between the path point and a current track point of the automatic driving vehicle is larger than an obstacle detection distance threshold; and

and determining the target path point based on the current track point of the automatic driving vehicle and the path point.

12. A risk obstacle determination device, comprising:

the first-level detection module is used for carrying out first-level collision detection on the obstacle to obtain a first-level collision detection result;

the secondary collision detection module is used for carrying out secondary collision detection on the obstacle under the condition that the primary collision detection result is used for representing collision risk between the automatic driving vehicle and the obstacle, so as to obtain a secondary collision detection result; and

13. The apparatus of claim 12, wherein the obstacle determination module comprises:

the area determination submodule is used for determining a target area based on the planned path information;

an initial determination submodule for determining an initial obstacle whose existence is uncertain; and

and the obstacle determination submodule is used for determining the obstacle based on the initial obstacle when the initial obstacle is determined to be positioned in the target area.

14. The apparatus of claim 13, wherein the region determination submodule comprises:

a path point determining unit configured to determine a target path point based on the planned path information;

an information determination 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 acquired from perceived data, and the prescribed road boundary information is acquired from map data;

A boundary determining unit configured to determine target boundary information from the perceived road boundary information, the prescribed road boundary information, and the predetermined safety boundary information; and

and the area determining unit is used for determining the target area based on the target boundary information.

15. The apparatus of claim 13, wherein the initial determination submodule comprises:

a type determining unit, configured to determine a type of an obstacle to be identified based on a historical perception data sequence, where the type is used to characterize whether the obstacle to be identified is an obstacle whose existence is uncertain; and

and the initial determining unit is used for determining the initial obstacle from the obstacles to be identified based on the type.

16. The apparatus of claim 15, wherein the type determining unit comprises:

a category determination subunit, configured to determine a category detection result based on the obstacle category sequence;

a position determining 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

and the type determining subunit is used for determining the type of the obstacle to be identified based on the category detection result, the position detection result and the speed detection result.

17. The apparatus of any of claims 12 to 16, the primary detection module comprising:

a movement determination sub-module for determining a movement speed of the obstacle in the planned path direction; and

and the first result determining submodule is used for obtaining the primary collision detection result based on the running speed of the automatic driving vehicle and the movement speed of the obstacle.

18. The apparatus of any of claims 12 to 17, the secondary detection module comprising:

a longitudinal determination submodule for determining a longitudinal distance between the autonomous vehicle and the obstacle in a planned path direction after a predetermined reaction period;

a duration determination submodule for determining a exercisable duration in the planned path direction based on the longitudinal distance, the running speed of the autonomous vehicle, and the movement speed of the obstacle;

a position determination sub-module for determining a target position of the obstacle based on the drivable time period; and

and the second result determining submodule is used for obtaining the secondary collision detection result based on the target position of the obstacle.

19. The apparatus of claim 18, wherein the duration determination submodule comprises:

A brake determining unit for determining a maximum acceleration of the autonomous vehicle for braking based on the longitudinal distance, a running speed of the autonomous vehicle, and a moving speed of the obstacle; and

20. The apparatus of claim 18 or 19, wherein the second result determination submodule comprises:

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 a target position of the obstacle;

a first route segment determining unit, configured to determine, for each of the target sub-positions, a first target route segment from a plurality of target route points, to obtain a plurality of first target route segments, where the plurality of target route points are determined based on planned route information of the autonomous vehicle;

a distance determining unit, configured to determine a plurality of lateral distances based on the plurality of first target road segments and the plurality of target sub-positions, where the lateral distances are vertical distances between the target sub-positions and first target road segments matched with the target sub-positions; and

And the second result determining unit is used for obtaining the secondary collision detection result based on the transverse intervals and the transverse interval threshold values.

21. The apparatus of claim 17, wherein the motion determination submodule comprises:

a second link determination unit configured to determine a second target link from a plurality of target route points based on a current position of the obstacle, wherein the plurality of target route points are determined based on planned route information of the autonomous vehicle; and

and the motion determining unit is used for determining the motion speed of the obstacle in the planned path direction based on the second target road section and the motion speed of the obstacle in the world coordinate system.

22. The apparatus of claim 14, wherein the waypoint determination unit comprises:

a path point determining subunit, configured to determine a path point from the planned path information, where a path distance between the path point and a current track point of the autonomous vehicle is greater than an obstacle detection distance threshold; and

and the target determining subunit is used for determining the target path point based on the current track point of the automatic driving vehicle and the path point.

23. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 11.

24. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1 to 11.

25. 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 11.

26. An autonomous vehicle comprising: the electronic device of claim 23.