Method, apparatus, device, storage medium and program product for controlling an autonomous vehicle

Abstract

Embodiments of this disclosure relate to methods, apparatus, devices, storage media, and program products for controlling autonomous vehicles. The proposed method includes: determining a first attribute of a target object based on perceived information from the autonomous vehicle; creating a virtual region associated with the target object in response to a difference between the first attribute and a second attribute indicated by map data; triggering the autonomous vehicle to plan a detour route associated with the virtual region; sending an assistance request associated with the target object to a remote device in response to detour route planning failure; and controlling the autonomous vehicle based on feedback messages received from the remote device. In this manner, embodiments of this disclosure can improve the efficiency of controlling autonomous vehicles.

Description

Technical Field

[0001] The exemplary embodiments disclosed herein generally relate to the field of computers, and particularly to methods, apparatus, devices, computer-readable storage media, and computer program products for controlling autonomous vehicles. Background Technology

[0002] With the development of computer technology, autonomous driving and driver assistance technologies have emerged to reduce the demands on drivers and free them from certain driving responsibilities. Typically, vehicles can assist drivers by using road condition information collected by onboard sensors or other sensing devices, or they can use control units to process this information and directly control the vehicle. Therefore, how to generate accurate vehicle control strategies based on road condition information is a crucial area of ​​focus. Summary of the Invention

[0003] In a first aspect of this disclosure, a method for controlling an autonomous vehicle is provided. The method includes: determining a first attribute of a target object based on perception information from the autonomous vehicle; creating a virtual region associated with the target object in response to a difference between the first attribute of the target object and a second attribute indicated by map data; triggering the autonomous vehicle to plan a detour route associated with the virtual region; sending an assistance request associated with the target object to a remote device in response to a failure of detour route planning; and controlling the autonomous vehicle based on feedback messages received from the remote device.

[0004] In a second aspect of this disclosure, an apparatus for controlling an autonomous vehicle is provided. The apparatus includes: a determining module configured to determine a first attribute of a target object based on perception information of the autonomous vehicle; a creating module configured to create a virtual region associated with the target object in response to a difference between the first attribute of the target object and a second attribute indicated by map data; a triggering module configured to trigger the autonomous vehicle to plan a detour route associated with the virtual region; a sending module configured to send an assistance request associated with the target object to a remote device in response to a failure of detour route planning; and a control module configured to control the autonomous vehicle based on feedback messages received from the remote device.

[0005] In a third aspect of this disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the device to perform the method of the first aspect.

[0006] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program that can be executed by a processor to implement the method of the first aspect.

[0007] In a fifth aspect of this disclosure, a computer program product is provided. The computer program product includes computer-executable instructions that, when executed by a processor, implement the method of the first aspect.

[0008] It should be understood that the content described in this content section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0009] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram is shown of an example environment in which embodiments of the present disclosure may be implemented;

[0011] Figure 2 A flowchart illustrating an example process for processing a road change scenario according to some embodiments of this disclosure is shown;

[0012] Figure 3 A schematic diagram illustrating an example process for handling traffic light change scenarios according to some embodiments of the present disclosure is shown;

[0013] Figure 4 A schematic diagram of a complex road change scenario according to some embodiments of the present disclosure is shown;

[0014] Figure 5 A flowchart illustrating an example process for controlling an autonomous vehicle according to some embodiments of the present disclosure is shown;

[0015] Figure 6 A schematic structural block diagram of an example device for controlling an autonomous vehicle according to some embodiments of the present disclosure is shown; and

[0016] Figure 7 A block diagram of an electronic device capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should be noted that the headings of any section / subsection provided herein are not limiting. Various embodiments are described throughout this document, and embodiments of any type may be included under any section / subsection. Furthermore, embodiments described in any section / subsection may be combined in any way with any other embodiments described in the same section / subsection and / or different sections / subsections.

[0019] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below. The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0020] The embodiments of this disclosure may involve user data, data acquisition, and / or use. All of these aspects comply with applicable laws, regulations, and relevant provisions. In the embodiments of this disclosure, all data collection, acquisition, processing, manipulation, forwarding, and use are conducted with the user's knowledge and confirmation. Accordingly, in implementing the embodiments of this disclosure, the type, scope of use, and usage scenarios of any data or information that may be involved should be communicated to the user and their authorization obtained in accordance with relevant laws and regulations through appropriate means. The specific methods of notification and / or authorization may vary depending on the actual situation and application scenario, and the scope of this disclosure is not limited in this respect.

[0021] In this specification and the embodiments, any processing of personal information will be carried out only under the premise of legality (such as obtaining the consent of the personal information subject, or being necessary for the performance of a contract), and will only be carried out within the scope stipulated or agreed upon. A user's refusal to process personal information other than that necessary for basic functions will not affect the user's use of basic functions.

[0022] As mentioned above, with the development of computer technology, autonomous driving and driver assistance technologies have emerged to reduce the demands on drivers and free them from certain driving responsibilities. Typically, vehicles can assist drivers by collecting road condition information from onboard sensors or other sensing devices, or they can use control units to process this information and directly control the vehicle. Therefore, generating accurate vehicle control strategies based on road condition information is a crucial issue. However, traditional methods can only generate vehicle control strategies based on simple road condition information and cannot meet the needs of generating strategies for complex road conditions. Therefore, traditional methods are unable to handle complex road conditions, seriously affecting the safety and efficiency of autonomous driving.

[0023] Embodiments of this disclosure propose a scheme for controlling an autonomous vehicle. According to this scheme, a first attribute of a target object can be determined based on the autonomous vehicle's perception information; in response to a difference between the first attribute of the target object and a second attribute indicated by map data, a virtual region associated with the target object is created; the autonomous vehicle is triggered to plan a detour route associated with the virtual region; in response to detour route planning failure, an assistance request associated with the target object is sent to a remote device; and the autonomous vehicle is controlled based on feedback messages received from the remote device.

[0024] In this way, embodiments of this disclosure can determine the difference between the attributes of a detected target object and the attributes indicated by map data based on the vehicle's perception information, and determine the corresponding vehicle control strategy. Therefore, embodiments of this disclosure determine the corresponding strategy for controlling the autonomous vehicle based on changes in the attributes of the target object, thereby ensuring the stability and safety of the autonomous vehicle's operation and meeting the requirements of autonomous driving.

[0025] The following section provides a detailed description of various example implementations of this scheme, with reference to the accompanying drawings.

[0026] Example Environment

[0027] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. For example... Figure 1 As shown, in environment 100, assistant 110 can interact directly with remote device 120, or via an attachment device of remote device 120. Remote device 120 can present user interface 130 to assistant 110, allowing assistant 110 to view vehicle information or perform route planning, etc.

[0028] The remote device 120 may include, for example, a cloud device or an edge computing device. In some embodiments, such a remote device 120 may be configured to provide the autonomous vehicle 101 with supplemental perception information about the traffic environment and / or provide guidance for the stranded autonomous vehicle 101 to extricate itself from a difficult situation.

[0029] Environment 100 may include an autonomous vehicle 101. In some embodiments, the autonomous vehicle 101 may be any type of vehicle capable of carrying people and / or objects and moving via a power system such as an engine, including but not limited to cars, trucks, buses, electric vehicles, motorhomes, etc. The autonomous vehicle 101 may be an automated driving vehicle (also referred to as an autonomous vehicle) that integrates functions such as environmental perception, planning and decision-making, and multi-level assisted driving.

[0030] like Figure 1 As shown, the autonomous vehicle 101 is equipped with electronic equipment 140, which can communicate with remote equipment 120. For example, electronic equipment 140 can communicate with remote equipment 120 via appropriate wireless communication methods. Electronic equipment 140 can be any device with computing capabilities, which can generate driving routes or execute corresponding commands based on path planning information issued by remote equipment 120.

[0031] It should be understood that the structure and function of environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0032] Example of controlling an autonomous vehicle

[0033] The following description will continue with reference to the accompanying drawings, which will provide some exemplary embodiments of this disclosure. Figure 2 A flowchart of an example process 200 for processing a road change scenario according to some embodiments of the present disclosure is shown. Process 200 can be implemented at an electronic device 140. Reference is made below. Figure 1 Describe the process 200.

[0034] like Figure 2 As shown, electronic device 140 can determine corresponding response strategies based on different road change scenarios 210 to control autonomous vehicle 101. As an example, electronic device 140 can make corresponding vehicle control strategies for road change scenarios based on the real-time detection capabilities of autonomous vehicle 101.

[0035] As an example, the real-time detection capability of autonomous vehicle 101 may include real-time detection capability meeting the requirements 220, real-time detection capability not meeting the requirements 230, and no real-time detection capability 240.

[0036] As an example, if the recall data fails (e.g., data is not successfully obtained) when the real-time detection capability of autonomous vehicle 101 meets the requirement 220 or does not meet the requirement, autonomous vehicle 101 can be processed as having no real-time detection capability 240.

[0037] For example, if the autonomous vehicle 101 lacks real-time detection capability 240, the electronic device 140 can generate a virtual area offline for subsequent confirmation. The electronic device 140 can also request offline investigation (e.g., on-site confirmation based on map collection vehicles or manual labor) or remote device processing (e.g., manual remote control).

[0038] The following will provide an exemplary description of the situations where the real-time detection capability of the autonomous vehicle 101 meets requirement 220 and the real-time detection capability does not meet requirement 230 but the detection data can be recalled.

[0039] As an example, electronic device 140 can determine a first attribute of a target object based on perception information from an autonomous vehicle. As an example, electronic device 140 can determine perception information based on sensors mounted on the autonomous vehicle. As an example, sensors mounted on the autonomous vehicle may include, for example, onboard cameras and / or radar sensors (e.g., lidar, millimeter-wave radar, etc.).

[0040] In some embodiments, the perception information may include data determined based on data acquired via sensors. As an example, the perception information may include image features corresponding to image data acquired by an onboard camera. As an example, the perception information may include point cloud features corresponding to point cloud data acquired by an onboard radar sensor.

[0041] For example, target objects may include traffic light objects (e.g., traffic light objects), road objects (e.g., lane lines, lanes, stop lines, observation lines, and sidewalks), target objects associated with hard edges of drivable areas (e.g., medians, fences (or barriers), sidewalks, posts, stone balls, and other hard boundaries without area attributes, roundabouts, and construction zones), and planar spaces.

[0042] As an example, the electronic device 140 can also determine a second attribute of the target object based on map data (e.g., offline map data). As an example, the first and / or second attributes of the target object may indicate, for example, the target object's location, size, shape, and whether the target object exists.

[0043] As an example, electronic device 140 can create a virtual region associated with the target object in response to a difference between a first attribute of the target object and a second attribute indicated by map data. As an example, the virtual region can indicate the edge or outline of the target object. As an example, the virtual region can be implemented as a polygon (e.g., a polygon). As an example, the polygon can be a three-dimensional polygon or a two-dimensional polygon.

[0044] Alternatively or additionally, before creating a virtual region associated with the target object, the electronic device 140 may determine a confidence level corresponding to the perceived information. As an example, the confidence level of the perceived information may be determined based on the hardware capabilities of the sensor (e.g., device accuracy, etc.). As an example, the confidence level of the perceived information may also be determined based on the processing capabilities of a preset model used to process the sensor data (e.g., the accuracy of the preset model, etc.).

[0045] As an example, electronic device 140 may control autonomous vehicle 101 based on a first attribute indicated by the perceived information in response to a confidence level corresponding to the perceived information being higher than a second threshold. As an example, electronic device 140 may determine that the real-time detection capability of autonomous vehicle 101 meets requirement 220 in response to a confidence level corresponding to the perceived information being higher than the second threshold. For example, electronic device 140 may plan a route based on the first attribute to control autonomous vehicle 101 (e.g., detour, stop, etc.). This disclosure is not intended to limit the specific value of the second threshold.

[0046] As an example, electronic device 140 may create a virtual region associated with a target object in response to a confidence level corresponding to the perceived information being less than a first threshold. As an example, electronic device 140 may determine that the real-time detection capability of autonomous vehicle 101 does not meet requirement 230 in response to a confidence level corresponding to the perceived information being less than the first threshold. This disclosure is not intended to limit the specific value of the first threshold.

[0047] Additionally, the electronic device 140 can determine the object type of the target object. As an example, the electronic device 140 can determine the object type of the target object based on perceptual information. The electronic device 140 can determine the change type based on the object type and the difference between the first attribute and the second attribute. Further, the electronic device 140 can create a virtual region associated with the target object based on the change type meeting preset conditions.

[0048] In some embodiments, such as Figure 3 As shown, Figure 3A schematic diagram of an example process 300 for processing a traffic light change scenario according to some embodiments of the present disclosure is shown. The target object is, for example, a traffic light object. In a traffic light road change scenario 305, the preset conditions satisfied by the change type may include the addition of a traffic light object (e.g., adding a new light 320). As an example, the electronic device 140 may determine that the preset condition satisfied by the change type is the addition of a traffic light object in response to a second attribute of the traffic light object corresponding to the map data indicating that there is no traffic light object at the current location, and a first attribute corresponding to the perception information indicating that there is a traffic light object at the current location.

[0049] As an example, the preset conditions satisfied by the change type may include the removal of traffic light objects (e.g., removal of light type 350). As an example, electronic device 140 may determine that the preset condition satisfied by the change type is the removal of traffic light objects in response to a second attribute of the traffic light object corresponding to the map data indicating that a traffic light object exists at the current location, and a first attribute corresponding to the perception information indicating that a traffic light object does not exist at the current location.

[0050] As an example, the preset conditions satisfied by the change type may include a change in the position of the traffic light object (e.g., position movement 330). As an example, the electronic device 140 may determine that the preset condition satisfied by the change type is a change in the position of the traffic light object in response to a difference between the first position of the traffic light object indicated by the second attribute of the traffic light object corresponding to the map data and the second position of the traffic light object indicated by the first attribute corresponding to the perception information.

[0051] As an example, the preset conditions satisfied by the change type may include a change in the type of the traffic light object (e.g., attribute change 310 and / or state change class 340). As an example, electronic device 140 may determine that the preset condition satisfied by the change type is a change in the type of the traffic light object in response to a difference between the attributes of the traffic light object indicated by the second attribute (e.g., size, shape, etc.) and the attributes of the traffic light object indicated by the first attribute (e.g., size, shape, etc.). As an example, electronic device 140 may determine that the preset condition satisfied by the change type is a change in the type of the traffic light object in response to a difference between the state of the traffic light object indicated by the second attribute (e.g., on / off state, good / bad state) and the state of the traffic light object indicated by the first attribute.

[0052] In some embodiments, the target object may be, for example, a road object. In a change scenario associated with a road object, the preset condition for the change type to be satisfied may include a change in the position of lane lines in the road object. As an example, the electronic device 140 may determine that the preset condition for the change type to be satisfied is a change in the position of the road object in response to a difference between the first position of the lane line indicated by the second attribute of the road object corresponding to the map data and the second position of the lane line indicated by the first attribute corresponding to the perception information.

[0053] Alternatively, the preset conditions for the change type to be satisfied may include a reduction in the number of lane line objects in the road object. As an example, the electronic device 140 may determine that the preset condition for the change type to be satisfied is a reduction in the number of lane line objects in the road object in response to the fact that the first number of lane line objects indicated by the second attribute of the road object corresponding to the map data is less than the second number of lane line objects indicated by the first attribute corresponding to the perception information.

[0054] Alternatively, the preset conditions for a change type to be satisfied may include lane lines in a road object being under construction. For example, electronic device 140 may determine that the preset condition for a change type to be satisfied is that lane lines in a road object are under construction, in response to a second attribute of the road object corresponding to map data indicating that the lane line object is in a normal state, and a first attribute corresponding to the perceived information indicating that the lane line object is under construction.

[0055] In some embodiments, the electronic device 140 may determine a virtual region based on lanes that change in a road object.

[0056] In some embodiments, the target object may be associated with, for example, a hard edge of a drivable area. In a change scenario associated with a hard edge of a drivable area, the preset conditions for the change type may include at least one of the following: addition, change of position, or change of shape of the target object. Target objects associated with a hard edge of a drivable area may include, for example, hard boundaries without area attributes such as medians, fences (or barriers), sidewalks, posts, stone balls, roundabouts, and construction zones.

[0057] In some embodiments, the electronic device 140 may determine a virtual region based on the range of changes in the drivable area. For example, the electronic device 140 may determine a virtual region based on the range of a median strip in response to the addition of a median strip to the drivable area.

[0058] Additionally, the electronic device 140 can trigger the autonomous vehicle 101 to plan a detour route associated with a virtual area. As an example, the electronic device 140 can plan a detour route based on a virtual area corresponding to one target object or multiple virtual areas corresponding to at least two target objects. Further, the electronic device 140 can control the autonomous vehicle 101 based on the detour route. For example, the electronic device 140 can control the autonomous vehicle 101 to move or stop along the detour route.

[0059] As an example, electronic device 140 can determine whether there is a road that bypasses the virtual area.

[0060] For example, electronic device 140 can respond to the existence of roads that bypass virtual areas and plan detour routes based on those roads.

[0061] As an example, electronic device 140 may determine whether there is a lane that bypasses the virtual area in response to determining that there is no road that bypasses the virtual area.

[0062] For example, electronic device 140 may plan a detour route based on a lane where a virtual detour area exists. Furthermore, electronic device 140 may determine that detour route planning has failed if a lane where a virtual detour area does not exist.

[0063] As an example, electronic device 140 may send an assistance request associated with the target object to a remote device in response to a failure of detour route planning. Furthermore, electronic device 140 may control autonomous vehicle 101 from feedback messages received from the remote device regarding the assistance request. For example, electronic device 140 may control autonomous vehicle 101 to move or stop along a specified route.

[0064] As an example, the electronic device 140 may control the autonomous vehicle 101 to perform preset operations (e.g., "turn on hazard lights" and / or "pull over") in response to a failure of detour planning.

[0065] As an example, electronic device 140 may respond to the creation of a virtual region associated with a target object (e.g., the target object undergoes a polygonal change) and determine other attribute changes associated with the target object based on perception information (e.g., a traffic light is broken). Then, based on the determined response strategy associated with the virtual region, the other attribute changes are ignored. As an example, for a single light in the same group (e.g., a traffic light), if the change type of the same light in the same group satisfies multiple preset conditions—both a polygonal-level change and a geometric strategy change—then it is handled using a polygonal strategy (e.g., the response logic for the virtual region), and perception does not request remote assistance or initiate a change. For example, if the same light is both broken and relocated, a polygon is marked, and electronic device 140 controls autonomous vehicle 101 to execute the response logic for the virtual region.

[0066] As an example, consider a group of multiple lights. For instance, if some lights undergo polygonal changes, some undergo geometric changes, and some remain unchanged, the polygonal strategy is used, and the system neither requests remote assistance nor initiates changes. For example, if a straight lane has three straight lights, one light is faulty and not marked on the map, another light is marked as faulty on the map, and the third light moves, a virtual area is created for the location change. Electronic device 140 controls autonomous vehicle 101 to execute response logic for the virtual area.

[0067] As an example, regarding repeated remote assistance requests: For the same intersection, if the sensor initiates a remote assistance request for traffic light assistance first, the remote assistance will no longer accept subsequent traffic light requests for that intersection. The electronic device 140 controls the autonomous vehicle 101 to ignore the traffic light polygon and proceed based on the sensor's result. For the same intersection, if the electronic device 140 controls the autonomous vehicle 101 to initiate a traffic light polygon request first, the remote assistance will no longer accept subsequent traffic light requests for that intersection. The electronic device 140 controls the autonomous vehicle 101 to ignore the sensor's result and proceed based on the remote assistance result. As an example, for the same intersection, if a traffic light polygon is requested only once, the electronic device 140 controls the autonomous vehicle 101 to proceed based on the result of the first request, ignoring all subsequent traffic light polygons at that intersection. For example: if a location change is detected and a remote assistance request is made, and then a light removal is detected, no new request is initiated.

[0068] Additionally, the lateral distance constraint corresponding to the virtual region is determined based on the object type of the target object. As an example, the electronic device 140 may determine a first lateral distance corresponding to the virtual region based on the object type of the target object being associated with a hard edge (e.g., a fence) to ensure a safe driving distance for the autonomous vehicle 101 (preventing collisions with the target object). As an example, the electronic device 140 may determine a second lateral distance constraint of 0 corresponding to the virtual region based on the object type of the target object being a traffic light object (i.e., the autonomous vehicle 101 can move within the virtual region).

[0069] As an example, electronic device 140 determines that at least a portion of the virtual area is also associated with attribute changes related to an attached object. Further, electronic device 140 can determine a response strategy for at least a portion of the area based on a first change type corresponding to the target object and a second change type corresponding to the attached object.

[0070] In some embodiments, such as Figure 4 As shown, Figure 4 A schematic diagram 400 of a road change complex scenario according to some embodiments of the present disclosure is shown.

[0071] As an example, in some scenarios, electronic device 140 can identify at least two target objects based on the perception information of autonomous vehicle 101. Furthermore, electronic device 140 can determine a virtual region based on at least two target objects.

[0072] As an example, electronic device 140 can identify a target object A and an additional object B. In some embodiments, electronic device 140 can determine a virtual region based on the intersection and / or lane (e.g., target object A) associated with the traffic light object, in response to the additional object (e.g., additional object B) being a traffic light object. For example, electronic device 140 can determine a first virtual region 410 associated only with target object A. Electronic device 140 can determine a second virtual region 420 (e.g., an overlapping region) associated with both target object A and additional object B. Electronic device 140 can determine a third virtual region 430 associated only with additional object B.

[0073] As an example, electronic device 140 determines a response strategy for second virtual area 420 based on a first change type of target object A and a second change type corresponding to supplementary object B. As an example, electronic device 140 can plan a detour route or parking route for second virtual area 420 based on the first change type and the second change type.

[0074] Based on the process described above, embodiments of this disclosure can determine the difference between the attributes of a detected target object and the attributes indicated by map data based on the vehicle's perception information, and determine the corresponding vehicle control strategy. Therefore, embodiments of this disclosure determine a corresponding strategy for controlling the autonomous vehicle based on changes in the attributes of the target object, thereby ensuring the stability and safety of the autonomous vehicle's operation and meeting the requirements of autonomous driving.

[0075] Example process

[0076] Figure 5 A flowchart illustrating an example process 500 for controlling an autonomous vehicle according to some embodiments of the present disclosure is shown. Process 500 can be implemented at an electronic device 140. Reference is made below. Figure 1 Describe the process 500.

[0077] In box 510, electronic device 140 determines the first attribute of the target object based on the perception information of the autonomous vehicle.

[0078] In box 520, electronic device 140 creates a virtual region associated with the target object in response to a first attribute of the target object being different from a second attribute indicated by the map data.

[0079] In box 530, electronic device 140 triggers autonomous vehicle planning of detour routes associated with the virtual area.

[0080] In box 540, in response to the failure of detour route planning, electronic device 140 sends an assistance request associated with the target object to a remote device.

[0081] In frame 550, electronic device 140 controls the autonomous vehicle based on feedback messages received from a remote device.

[0082] In some embodiments, creating a virtual region associated with a target object in response to a first attribute of the target object being different from a second attribute indicated by map data includes: determining a confidence level corresponding to the perceived information; and creating a virtual region associated with the target object in response to a confidence level being less than a first threshold.

[0083] In some embodiments, process 500 further includes: controlling the autonomous vehicle based on a first attribute indicated by the perception information in response to a confidence level higher than a second threshold.

[0084] In some embodiments, creating a virtual region associated with a target object in response to a difference between a first attribute of the target object and a second attribute indicated by map data includes: determining the object type of the target object; determining a change type based on the object type and the difference between the first and second attributes; and creating a virtual region associated with the target object in response to the change type meeting preset conditions.

[0085] In some embodiments, the target object is a traffic light object, and a preset condition indicates at least one of the following: the addition of a traffic light object; the removal of a traffic light object; a change in the position of a traffic light object; or a change in the type of a traffic light object.

[0086] In some embodiments, the virtual area is determined based on the intersection and / or lane associated with the traffic light object.

[0087] In some embodiments, the target object is a road object, and the preset conditions indicate at least one of the following: a change in the position of lane lines in the road object; a decrease in the number of lane line objects in the road object; or the lane lines in the road object are under construction.

[0088] In some embodiments, the virtual region is determined based on lanes that change within a road object.

[0089] In some embodiments, the target object is associated with a hard edge of the drivable area, and preset conditions indicate at least one of the following: an increase in the target object; a change in the position of the target object; or a change in the shape of the target object.

[0090] In some embodiments, the target object includes: a safety barrier, a fence, or a sidewalk.

[0091] In some embodiments, the virtual region is associated with the range of variation of the drivable region.

[0092] In some embodiments, process 500 further includes: determining whether there is a road that bypasses the virtual area; in response to determining that there is no road that bypasses the virtual area, determining whether there is a lane that bypasses the virtual area; and in response that there is no lane that bypasses the virtual area, determining that the detour route planning has failed.

[0093] In some embodiments, the lateral distance constraint corresponding to the virtual region is determined based on the object type of the target object.

[0094] In some embodiments, process 500 further includes: determining that at least a portion of the virtual region is also associated with an attribute change associated with an attached object; and determining a response strategy for at least a portion of the region based on a first change type corresponding to the target object and a second change type corresponding to the attached object.

[0095] Example devices and equipment

[0096] Embodiments of this disclosure also provide corresponding apparatus for implementing the above methods or processes. Figure 6 A schematic structural block diagram of an example device 600 for controlling an autonomous vehicle according to certain embodiments of the present disclosure is shown. Device 600 may be implemented as or included in electronic device 140. Various modules / components in device 600 may be implemented by hardware, software, firmware, or any combination thereof.

[0097] like Figure 6 As shown, the device 600 includes a determination module 610 configured to determine a first attribute of a target object based on perception information from an autonomous vehicle; a creation module 620 configured to create a virtual region associated with the target object in response to a difference between the first attribute of the target object and a second attribute indicated by map data; a trigger module 630 configured to trigger the autonomous vehicle to plan a detour route associated with the virtual region; a sending module 640 configured to send an assistance request associated with the target object to a remote device in response to a failure of detour route planning; and a control module 650 configured to control the autonomous vehicle based on feedback messages received from the remote device.

[0098] In some embodiments, the creation module 620 is further configured to: determine a confidence level corresponding to the perceived information; and, in response to a confidence level less than a first threshold, create a virtual region associated with the target object.

[0099] In some embodiments, the device 600 further includes a first determination module, which is configured to control the autonomous vehicle based on a first attribute indicated by the perception information in response to a confidence level higher than a second threshold.

[0100] In some embodiments, the creation module 620 is further configured to: determine the object type of the target object; determine the change type based on the object type and the difference between the first attribute and the second attribute; and create a virtual region associated with the target object in response to the change type meeting a preset condition.

[0101] In some embodiments, the target object is a traffic light object, and a preset condition indicates at least one of the following: the addition of a traffic light object; the removal of a traffic light object; a change in the position of a traffic light object; or a change in the type of a traffic light object.

[0102] In some embodiments, the virtual area is determined based on the intersection and / or lane associated with the traffic light object.

[0103] In some embodiments, the target object is a road object, and the preset conditions indicate at least one of the following: a change in the position of lane lines in the road object; a decrease in the number of lane line objects in the road object; or the lane lines in the road object are under construction.

[0104] In some embodiments, the virtual region is determined based on lanes that change within a road object.

[0105] In some embodiments, the target object is associated with a hard edge of the drivable area, and preset conditions indicate at least one of the following: an increase in the target object; a change in the position of the target object; or a change in the shape of the target object.

[0106] In some embodiments, the target object includes: a safety barrier, a fence, or a sidewalk.

[0107] In some embodiments, the virtual region is associated with the range of variation of the drivable region.

[0108] In some embodiments, the device 600 further includes a second determination module, which is configured to: determine whether there is a road that bypasses the virtual area; in response to determining that there is no road that bypasses the virtual area, determine whether there is a lane that bypasses the virtual area; and in response to determining that there is no lane that bypasses the virtual area, determine that the detour route planning has failed.

[0109] In some embodiments, the lateral distance constraint corresponding to the virtual region is determined based on the object type of the target object.

[0110] In some embodiments, the apparatus 600 further includes a third determination module, which is configured to: determine that at least a portion of the virtual region is also associated with an attribute change associated with an attached object; and determine a response strategy for at least a portion of the region based on a first change type corresponding to the target object and a second change type corresponding to the attached object.

[0111] The modules included in device 600 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units can be implemented using software and / or firmware, such as machine-executable instructions stored on a storage medium. In addition to or as an alternative to machine-executable instructions, some or all of the modules in device 600 can be implemented at least partially by one or more hardware logic components. By way of example, and not limitation, exemplary types of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0112] Figure 7 A block diagram of an electronic device 700 in which one or more embodiments of the present disclosure may be implemented is shown. It should be understood that... Figure 7 The electronic device 700 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. Figure 7 The electronic device 700 shown can be used to achieve Figure 1 140 electronic devices.

[0113] like Figure 7 As shown, electronic device 700 is in the form of a general-purpose electronic device. Components of electronic device 700 may include, but are not limited to, one or more processors or processing units 710, memory 720, storage device 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760. Processing unit 710 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 720. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 700.

[0114] Electronic device 700 typically includes multiple computer storage media. Such media can be any accessible media that is accessible to electronic device 700, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 720 can be volatile memory (e.g., registers, cache, random access memory (Remote Assist M)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 730 can be removable or non-removable media and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data and can be accessed within electronic device 700.

[0115] Electronic device 700 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 7 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 720 may include computer program product 725 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0116] The communication unit 740 enables communication with other electronic devices via a communication medium. Additionally, the functionality of the components of the electronic device 700 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, the electronic device 700 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0117] Input device 750 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 760 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 700 can also communicate with one or more external devices (not shown) via communication unit 740 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 700, or with any device that enables electronic device 700 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0118] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0119] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0120] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0121] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0122] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0123] Various implementations of this disclosure have been described above. The foregoing description is exemplary and not exhaustive, nor is it limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for controlling an autonomous vehicle, comprising: Based on the perception information of autonomous vehicles, the first attribute of the target object is determined; In response to the first attribute of the target object being different from the second attribute indicated by the map data, a virtual region associated with the target object is created; The autonomous vehicle is triggered to plan a detour route associated with the virtual area; In response to the failure of the detour route planning, an assistance request associated with the target object is sent to a remote device; and The autonomous vehicle is controlled based on feedback messages received from the remote device.

2. The method of claim 1, wherein creating a virtual region associated with the target object in response to the first attribute of the target object differing from the second attribute indicated by the map data comprises: Determine the confidence level corresponding to the perceived information; as well as In response to the confidence level being less than a first threshold, the virtual region associated with the target object is created.

3. The method according to claim 2, further comprising: In response to the confidence level being higher than a second threshold, the autonomous vehicle is controlled based on the first attribute indicated by the perceived information.

4. The method of claim 1, wherein creating a virtual region associated with the target object in response to a difference between the first attribute of the target object and a second attribute indicated by map data comprises: Determine the object type of the target object; as well as The change type is determined based on the object type and the difference between the first attribute and the second attribute; as well as In response to the change type meeting preset conditions, the virtual region associated with the target object is created.

5. The method of claim 4, wherein the target object is a traffic light object, and the preset condition indicates at least one of the following: The number of traffic light objects has increased; Remove the traffic light object; The position of the traffic light object changes; Type changes of traffic light objects.

6. The method of claim 5, wherein the virtual region is determined based on the intersection and / or lane associated with the traffic light object.

7. The method of claim 4, wherein the target object is a road object, and the preset condition indicates at least one of the following: Changes in the position of lane lines within a road object; The number of lane line objects in road objects has been reduced; The lane lines in the road object are under construction.

8. The method of claim 7, wherein the virtual region is determined based on lanes that change within the road object.

9. The method of claim 4, wherein the target object is associated with a hard edge of a drivable area, and the preset condition indicates at least one of the following: The increase of the target object; The position of the target object changes; The shape change of the target object.

10. The method of claim 9, wherein the target object comprises: Police cordon, fence, or sidewalk.

11. The method of claim 9, wherein the virtual region is associated with the range of variation of the drivable region.

12. The method according to claim 1, further comprising: Determine if there are roads that bypass the virtual area; In response to determining that there are no roads bypassing the virtual area, determine whether there are lanes bypassing the virtual area; as well as If no lane exists that detours the virtual area, the detour route planning is determined to have failed.

13. The method of claim 1, wherein the lateral distance constraint corresponding to the virtual region is determined based on the object type of the target object.

14. The method according to claim 1, further comprising: It is determined that at least a portion of the virtual region is also associated with attribute changes related to the attached object; as well as Based on the first change type corresponding to the target object and the second change type corresponding to the additional object, a response strategy is determined for the at least some regions.

15. A device for controlling an autonomous vehicle, comprising: The determination module is configured to determine the first attribute of the target object based on the perception information of the autonomous vehicle; A creation module is configured to create a virtual region associated with the target object in response to a difference between the first attribute of the target object and the second attribute indicated by the map data. The triggering module is configured to trigger the autonomous vehicle to plan a detour route associated with the virtual area; The sending module is configured to send an assistance request associated with the target object to a remote device in response to the failure of the detour route planning; as well as The control module is configured to control the autonomous vehicle based on feedback messages received from the remote device.

16. An electronic device comprising: At least one processing unit; as well as At least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 14 when executed by the at least one processing unit.

17. A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of claims 1 to 14.

18. A computer program product comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 14.

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