CN114299758A - Vehicle control method and apparatus, device, medium, and product - Google Patents
Vehicle control method and apparatus, device, medium, and product Download PDFInfo
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- CN114299758A CN114299758A CN202111650840.9A CN202111650840A CN114299758A CN 114299758 A CN114299758 A CN 114299758A CN 202111650840 A CN202111650840 A CN 202111650840A CN 114299758 A CN114299758 A CN 114299758A
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Abstract
The present disclosure provides a vehicle control method and apparatus, device, medium, and product, which relate to the field of artificial intelligence, and in particular to the technical field of information processing and intelligent transportation. The specific implementation scheme comprises the following steps: in response to detecting that an obstacle exists in a current lane where the target vehicle is located to block traveling, determining a traveling scene type where the target vehicle is located according to vehicle state information and traveling scene information associated with the target vehicle; determining whether an adjacent lane corresponding to the current lane meets a preset occupation condition or not under the condition that the driving scene type indicates that the target vehicle can bypass the obstacle avoidance; in response to the fact that the adjacent lanes meet the occupation condition, performing obstacle avoidance path planning on the target vehicle based on the adjacent lanes to obtain a target driving path; and controlling the target vehicle to run based on the target running path so that the target vehicle bypasses the obstacle.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence, and more particularly to the field of information processing and intelligent transportation technologies, which can be applied in a vehicle control scenario.
Background
During the running of the vehicle, a problem may occur in that the obstacle blocks the vehicle from traveling. The vehicle is controlled to reasonably avoid the obstacle, and the normal and safe running of the vehicle is guaranteed. However, in some scenes, when a vehicle is controlled to avoid an obstacle, the phenomena of low escaping efficiency and strong manual dependence exist.
Disclosure of Invention
The present disclosure provides a vehicle control method and apparatus, device, medium and product.
According to an aspect of the present disclosure, there is provided a vehicle control method including: in response to detecting that an obstacle exists in a current lane where a target vehicle is located to block traveling, determining a traveling scene type where the target vehicle is located according to vehicle state information and traveling scene information associated with the target vehicle; determining whether an adjacent lane corresponding to the current lane meets a preset occupation condition or not under the condition that the driving scene type indicates that the target vehicle can bypass to avoid the obstacle; in response to the fact that the adjacent lane meets the occupation condition, performing obstacle avoidance path planning on the target vehicle based on the adjacent lane to obtain a target driving path; and controlling the target vehicle to run based on the target running path so that the target vehicle bypasses the obstacle.
According to another aspect of the present disclosure, there is provided a vehicle control apparatus including: the system comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is used for responding to the detection that an obstacle exists in a current lane where a target vehicle is located to block the traveling, and determining the type of a traveling scene where the target vehicle is located according to vehicle state information and traveling scene information which are related to the target vehicle; the second processing module is used for determining whether an adjacent lane corresponding to the current lane meets a preset occupation condition or not under the condition that the driving scene type indicates that the target vehicle can detour to avoid the obstacle; the third processing module is used for responding to the fact that the adjacent lane meets the occupation condition, conducting obstacle avoidance path planning on the target vehicle based on the adjacent lane, and obtaining a target driving path; and the fourth processing module is used for controlling the target vehicle to run based on the target running path so as to enable the target vehicle to detour and avoid 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 vehicle control method according to an embodiment of the disclosure.
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 to execute a vehicle control method according to an embodiment of the present disclosure.
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 vehicle control method according to an embodiment of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 schematically illustrates a system architecture of vehicle controls and devices according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a vehicle control method according to an embodiment of the present disclosure;
FIG. 3 schematically shows a schematic diagram of a vehicle control method according to another embodiment of the present disclosure;
FIG. 4A schematically illustrates a schematic view of a target driving area according to an embodiment of the present disclosure;
FIG. 4B schematically shows a schematic view of a first target sub-region according to an embodiment of the present disclosure;
FIG. 4C schematically illustrates a schematic view of a second target sub-region according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a vehicle control apparatus according to an embodiment of the present disclosure; and
fig. 6 schematically shows a block diagram of an electronic device for performing vehicle control according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those 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.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Embodiments of the present disclosure provide a vehicle control method. The vehicle control method includes: the method comprises the steps of responding to the fact that an obstacle body obstructs the traveling of a current lane where a target vehicle is located, determining the type of the traveling scene where the target vehicle is located according to vehicle state information and traveling scene information which are related to the target vehicle, determining whether an adjacent lane corresponding to the current lane meets a preset occupation condition or not under the condition that the traveling scene type indicates that the target vehicle can bypass the obstacle avoidance, responding to the fact that the adjacent lane meets the occupation condition, conducting obstacle avoidance path planning based on the adjacent lane on the target vehicle to obtain a target traveling path, and controlling the target vehicle to travel based on the target traveling path so that the target vehicle bypasses the obstacle body.
Fig. 1 schematically illustrates a system architecture of vehicle controls and devices according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
The system architecture 100 according to this embodiment may include data collection terminals 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the data collection terminals 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few. The server 105 may be an independent physical server, a server cluster or a distributed system including a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud computing, web services, and middleware services.
The data collection terminals 101, 102, 103 may be various electronic devices for collecting vehicle driving related data, including but not limited to laser radar, inertial measurement unit, speed sensor, vision sensor, acceleration sensor, millimeter wave radar, ultrasonic radar, positioning terminal, etc. The data acquisition terminals 101, 102, 103 may also acquire data from a preset database related to vehicle driving, for example, may acquire high-precision map data from a map database.
The server 105 may be a background processing server (for example only) that processes the vehicle driving related data provided by the data collection terminals 101, 102, 103. The background processing server can process the received vehicle driving related data and generate vehicle control instructions based on the processing result.
For example, in response to detecting that there is an obstacle obstructing the travel in the current lane where the target vehicle is located, the server 105 determines the type of the traveling scene where the target vehicle is located, based on the vehicle state information and the traveling scene information associated with the target vehicle, which are acquired by the data acquisition terminals 101, 102, 103. Under the condition that the driving scene type indicates that the target vehicle can bypass to avoid the obstacle, the server 105 determines whether an adjacent lane corresponding to the current lane meets a preset occupation condition, performs obstacle avoidance path planning based on the adjacent lane on the target vehicle in response to the fact that the adjacent lane meets the occupation condition, obtains a target driving path, and controls the target vehicle to drive based on the target driving path so as to bypass to avoid the obstacle.
It should be noted that the vehicle control method provided by the embodiment of the present disclosure may be executed by the server 105. Accordingly, the vehicle control apparatus provided by the embodiment of the present disclosure may be provided in the server 105. The vehicle control method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster that is different from the server 105 and is capable of communicating with the data collection terminals 101, 102, 103 and/or the server 105. Correspondingly, the vehicle control device provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster that is different from the server 105 and can communicate with the data collection terminals 101, 102, 103 and/or the server 105.
It should be understood that the number of data collection terminals, networks, and servers in fig. 1 is merely illustrative. There may be any number of data collection terminals, networks, and servers, as desired for implementation.
The embodiment of the present disclosure provides a vehicle control method, and a vehicle control method according to an exemplary embodiment of the present disclosure is described below with reference to fig. 2 to 3, and fig. 4A, 4B, and 4C in conjunction with the system architecture of fig. 1.
Fig. 2 schematically shows a flow chart of a vehicle control method according to an embodiment of the present disclosure.
As shown in fig. 2, the vehicle control method 200 of the embodiment of the present disclosure may include, for example, operations S210 to S240.
In operation S210, in response to detecting that there is an obstacle obstructing travel in the current lane in which the target vehicle is located, a travel scene type in which the target vehicle is located is determined according to the vehicle state information and the travel scene information associated with the target vehicle.
In operation S220, in case that the driving scene type indicates that the target vehicle may circumvent the obstacle avoidance, it is determined whether an adjacent lane corresponding to the current lane satisfies a preset occupancy condition.
In operation S230, in response to determining that the adjacent lane satisfies the occupation condition, the obstacle avoidance path planning based on the adjacent lane is performed on the target vehicle, so as to obtain a target driving path.
In operation S240, the target vehicle is controlled to travel based on the target travel path so as to detour around the obstacle.
An example flow of each operation of the vehicle control method of the embodiment is explained below by way of example.
Illustratively, the vehicle state information may include vehicle positioning information, vehicle speed information, steering wheel angle, yaw acceleration, and the like. The driving scene information may include obstacle status information, lane line information, road topology information, traffic indication signals, and the like. The obstacle state information may include obstacle location information, obstacle velocity information, obstacle acceleration information, and the like.
The vehicle state information, obstacle state information, traffic indication signals may be sensory information collected by sensors, which may include, for example, lidar, inertial measurement units, millimeter wave radar, ultrasonic radar, vision sensors, speed sensors, and the like. The lane line information and the road topology information may be obtained from high-precision map data.
The lane line information may include a lane number, a lane boundary line type, a lane boundary line distance, and the like, the lane boundary line type may include a white solid line, a white dotted line, a yellow solid line, a yellow dotted line, and the like, the lane boundary distance may include a distance of the vehicle from a left boundary line of the lane, a distance of the vehicle from a right boundary line of the lane, and the like, and the left and right may be determined according to a traveling direction of the vehicle. The road topology information may include road numbers, road types, road connection relationships, etc., and the road types may include, for example, straight running, left turn, right turn, etc.
And in response to detecting that the obstacle exists in the current lane where the target vehicle is located to block the traveling, determining the type of the traveling scene where the target vehicle is located according to the vehicle state information and the traveling scene information which are associated with the target vehicle.
In one example, the vehicle state information and the driving scenario information may be encoded to obtain the driving scenario characteristics. And taking the driving scene characteristics as input data of the scene classification model to obtain a prediction result associated with each driving scene type in the at least one driving scene type. And determining the driving scene type of the target vehicle according to the prediction result associated with each driving scene type.
The driving scene types may include, for example, a normal driving scene and an abnormal driving scene, the normal driving scene may include, for example, a queuing scene, a traffic light normal waiting scene, and the like, and the abnormal driving scene may include, for example, an accident scene, an illegal parking scene, a traffic light abnormal waiting scene, and the like.
And the prediction result associated with the driving scene type indicates the probability value of the target vehicle currently in the corresponding driving scene type. And according to the probability value associated with each driving scene type in the at least one driving scene type, taking the driving scene type corresponding to the maximum probability value as the driving scene type where the target vehicle is located currently.
The scene classification model may include an encoding layer for processing the input driving scene features into uniform-dimension vectors, and a clustering layer for clustering the uniform-dimension vectors to determine prediction results associated with the respective driving scene types based on the clustering results.
Aiming at a normal driving scene, an obstacle body obstructs the target vehicle to move and belongs to a normal condition, and the target vehicle is determined to be free from avoiding obstacles by bypassing. And aiming at the abnormal driving scene, the situation that the target vehicle is prevented from moving by an obstacle belongs to an abnormal situation, and the target vehicle can be determined to avoid the obstacle by detouring.
And under the condition that the driving scene type indicates that the target vehicle can bypass the obstacle avoidance, determining whether the adjacent lane corresponding to the current lane meets a preset occupation condition. For example, a target driving area required for the target vehicle to circumvent the obstacle avoidance may be determined. A first target sub-area located in an adjacent lane within the target driving area is determined. Determining whether the detour is allowed in the first target sub-area according to the vehicle state information and the driving scene information, and determining that the adjacent lane meets the occupation condition in response to determining that the detour is allowed in the first target sub-area.
In addition, whether the adjacent lanes meet the occupation condition can be determined according to whether the allowable driving direction of the adjacent lanes is consistent with the obstacle avoidance direction of the target vehicle or whether the lane boundary line type of the adjacent lanes allows the occupied lane to detour.
And in response to the fact that the adjacent lanes meet the occupation condition, performing obstacle avoidance path planning on the target vehicle based on the adjacent lanes to obtain a target driving path. For example, obstacle avoidance path planning based on the target driving area may be performed on the target vehicle, so as to obtain a target driving path for bypassing obstacle avoidance. And controlling the target vehicle to run based on the target running path so that the target vehicle bypasses the obstacle.
According to the embodiment of the disclosure, in response to the detection that the obstacle body obstructs the travel of the target vehicle in the current lane, the travel scene type of the target vehicle is determined according to the vehicle state information and the travel scene information associated with the target vehicle, in the case that the travel scene type indicates that the target vehicle can detour the obstacle avoidance, whether the adjacent lane corresponding to the current lane meets the preset occupation condition is determined, in response to the determination that the adjacent lane meets the occupation condition, obstacle avoidance path planning based on the adjacent lane is performed on the target vehicle, a target travel path is obtained, and the target vehicle is controlled to travel based on the target travel path, so that the target vehicle detours the obstacle body.
Whether the adjacent lane corresponding to the current lane meets the preset occupation condition or not is determined, and the obstacle avoidance path planning based on the adjacent lane is carried out on the target vehicle under the condition that the adjacent lane meets the occupation condition, so that the safe and efficient autonomous obstacle avoidance function is favorably realized, the manual dependence of vehicle obstacle avoidance control can be effectively reduced, and the technical effect of automatic escaping of the vehicle can be effectively realized.
Fig. 3 schematically shows a schematic diagram of a vehicle control method according to another embodiment of the present disclosure.
As shown in fig. 3, operation S220 may include, for example, operations S310 to S330.
In operation S310, a target driving area required for the target vehicle to circumvent the obstacle avoidance is determined.
In operation S320, a first target sub-area located in an adjacent lane within the target driving area is determined.
In operation S330, it is determined whether the detour is allowed within the first target sub-area according to the vehicle state information and the driving scene information.
In operation S340, in response to determining that the occupied detour is allowed within the first target sub-area, it is determined that the adjacent lane satisfies the occupancy condition.
An example flow of each operation of the vehicle control method of the embodiment is explained below by way of example.
For example, when the target vehicle is determined to be a target driving area required for obstacle avoidance, width occupation information of the target driving area may be determined according to the obstacle boundary information, the width of the target vehicle, and a preset safe distance. And determining width constraint information of a track coverage area of a center point of the target vehicle when the obstacle avoidance is carried out by the vehicle according to the width and the width occupation information of the target vehicle. And determining the target driving area according to at least one of the vehicle state information, the driving scene information, the width occupation information and the width constraint information.
The target driving area can be an area through which the target vehicle passes when the target vehicle bypasses the obstacle avoidance, the width of the target driving area can be the width of the target vehicle, and the left boundary and the right boundary of the target driving area can be determined according to the boundary of the obstacle body, the width of the target vehicle and the preset safety distance. For example, a boundary line whose distance from the boundary of the obstacle is a preset safe distance may be used as the right boundary (left boundary) of the target travel area. A boundary line whose distance from the boundary of the obstacle is the sum of the target vehicle width and the preset safe distance may be set as the left boundary (right boundary) of the target travel area.
And determining width constraint information of a track coverage area of a center point of the target vehicle when the obstacle avoidance is carried out by the vehicle according to the width and the width occupation information of the target vehicle. The width constraint information indicates a second target sub-area within the target driving area. When the obstacle avoidance is carried out in a bypassing manner, the track of the center point of the target vehicle passes through the second target subarea, so that the distance between the target vehicle and the boundary of the obstacle body can be effectively ensured to be larger than the preset safety distance. The distance between the boundary of the second target subregion and the target driving region on the same side can be the half width of the target vehicle.
The length occupation information of the target travel region may be determined according to at least one of vehicle state information, travel scene information, and width constraint information. For example, at least one detour obstacle avoidance candidate path of the target vehicle may be determined according to the vehicle positioning information, the vehicle speed information, the obstacle positioning information, the obstacle speed information, and the width constraint information of the track coverage area of the center point of the target vehicle. And determining length occupation information of the target driving area according to at least one bypassing obstacle avoidance candidate path.
Illustratively, the length occupation information of the target driving area can be further determined according to the boundary of the obstacle (for example, the boundary of the front and the back of the obstacle), the length of the target vehicle and the preset safe distance. And determining a target driving area for the target vehicle to pass through according to the length occupation information and the width occupation information of the target driving area. The target driving area conforming to the kinematic constraint of the vehicle is determined, so that the safe and reliable autonomous obstacle avoidance function is realized.
The target driving area may include a partial sub-area located in the current lane and a partial sub-area located in the adjacent lane, and the partial sub-area located in the adjacent lane is taken as the first target sub-area. And determining whether the first target subregion allows the lane-taking detour by determining whether the possibility of vehicle-crossing exists in the first target subregion according to the vehicle state information and the driving scene information. In response to determining that there is no possibility of a vehicle crossing within the first target sub-region, determining that the adjacent lane satisfies the occupancy condition.
When determining whether the first target sub-area allows the detour, the first estimated time for the target vehicle to enter the first target sub-area may be determined according to the vehicle state information. For example, the first estimated time for the target vehicle to enter the first target sub-area may be determined according to the vehicle positioning information and the vehicle speed information of the target vehicle.
And determining a second estimated time for the obstacle in the adjacent lane to enter the first target subregion according to the driving scene information, wherein the driving scene information can comprise obstacle state information in the adjacent lane. The earliest time of the obstacle in the adjacent lane entering the first target sub-area from the current time can be determined according to the obstacle state information in the adjacent lane, and the earliest time is used as the second estimated time.
And determining whether the lane change is allowed in the first target subregion according to the first estimated time and the second estimated time. For example, a time difference between the first estimated time and the second estimated time is determined, and in the case that the time difference is greater than a preset threshold, it is determined that there is no possibility of a vehicle crossing in the first target sub-area, that is, the first target sub-area is allowed to detour.
And under the condition that the time difference value is smaller than or equal to the preset threshold value, determining a first estimated speed when the target vehicle enters the first target sub-region according to the vehicle state information. And determining a second estimated speed when the corresponding obstacle in the adjacent lane enters the first target sub-area according to the driving scene information. And determining that the first target sub-area is allowed to bypass under the condition that the speed difference value between the first estimated speed and the second estimated speed is larger than a preset threshold value.
By determining whether the first target sub-area allows the detour, and under the condition that the first target sub-area allows the detour, the obstacle avoidance path planning based on the adjacent lane is carried out, which is beneficial to realizing the local detailed obstacle avoidance path guidance, and the safety of the target vehicle when the obstacle avoidance is carried out can be effectively ensured.
In response to determining that the detour is permitted within the first target sub-area, a second target sub-area within the target travel area indicated by the width constraint information is determined. And carrying out obstacle avoidance path planning based on the target area on the target vehicle so as to enable the center point of the target vehicle to pass through the second target subarea to obtain a target driving path. The center point of the target vehicle passes through the second target subarea, so that the interval between the boundary of the target vehicle and the boundary of the obstacle can be effectively ensured to meet the preset safety distance.
By means of local and careful obstacle avoidance path planning, safe and reliable automatic obstacle avoidance of the vehicle is facilitated, dependence of vehicle obstacle avoidance control on manual taking over control can be effectively reduced, and the autonomous obstacle avoidance function of the unmanned vehicle is facilitated.
Fig. 4A schematically illustrates a schematic view of a target driving area according to an embodiment of the present disclosure.
As shown in fig. 4A, in response to detecting that an obstacle 402 obstructing travel of the target vehicle 401 exists in the current lane, the type of the travel scene in which the target vehicle 401 is currently located is determined based on the vehicle state information and the travel scene information associated with the target vehicle 401. In the case where the travel scene type indicates that the target vehicle 401 can circumvent the obstacle avoidance, a target travel region 403 required for the target vehicle 401 to circumvent the obstacle avoidance is determined.
The distance between the right boundary 403b of the target driving area 403 and the left boundary of the obstacle 402 is a preset safe distance buffer, and the distance between the left boundary 403a and the right boundary 403b may be the sum of the width of the target vehicle 401 and the safe distance buffer. The target travel area 403 may be an area through which the target vehicle 401 passes while circumventing the obstacle avoidance.
Figure 4B schematically shows a schematic view of a first target sub-region according to an embodiment of the present disclosure.
As shown in fig. 4B, the target driving region includes a partial sub-region located in the current lane and a partial sub-region located in the adjacent lane, and the partial sub-region located in the adjacent lane constitutes the first target sub-region 404. It may be determined whether the adjacent lane meets the preset occupancy condition by determining whether there is a possibility of a vehicle merging within the first target sub-region 404.
Figure 4C schematically illustrates a schematic view of a second target sub-region according to an embodiment of the present disclosure.
As shown in fig. 4C, the spacing between the second target sub-area 405 and the same side boundary of the target driving area may be the vehicle width of the target vehicle 401. The second target sub-area 405 may be determined according to the width constraint information of the track coverage area of the center point of the target vehicle 401 when the obstacle avoidance is performed. When the obstacle is avoided in the bypassing process, the track of the center point of the target vehicle 401 passes through the second target sub-region 405, so that the distance between the target vehicle 401 and the boundary of the obstacle body 402 can be effectively ensured to be larger than the preset safety distance, and the safety of the obstacle bypassing in the cross lane can be effectively ensured.
Fig. 5 schematically shows a block diagram of a vehicle control apparatus according to an embodiment of the present disclosure.
As shown in fig. 5, the vehicle control apparatus 500 of the embodiment of the present disclosure includes, for example, a first processing module 510, a second processing module 520, a third processing module 530, and a fourth processing module 540.
The first processing module 510 is configured to, in response to detecting that an obstacle exists in a current lane where the target vehicle is located to obstruct traveling, determine a traveling scene type where the target vehicle is located according to vehicle state information and traveling scene information associated with the target vehicle; the second processing module 520 is configured to determine whether an adjacent lane corresponding to the current lane meets a preset occupation condition or not when the driving scene type indicates that the target vehicle can detour the obstacle avoidance; the third processing module 530 is configured to perform obstacle avoidance path planning on the target vehicle based on the adjacent lane to obtain a target driving path in response to determining that the adjacent lane meets the occupation condition; and a fourth processing module 540, configured to control the target vehicle to travel based on the target travel path, so that the target vehicle bypasses the obstacle.
According to the embodiment of the disclosure, in response to detecting that an obstacle exists in a current lane where a target vehicle is located to obstruct traveling, determining a traveling scene type where the target vehicle is located according to vehicle state information and traveling scene information associated with the target vehicle; determining whether an adjacent lane corresponding to the current lane meets a preset occupation condition or not under the condition that the driving scene type indicates that the target vehicle can bypass the obstacle avoidance; in response to the fact that the adjacent lanes meet the occupation condition, performing obstacle avoidance path planning on the target vehicle based on the adjacent lanes to obtain a target driving path; and controlling the target vehicle to run based on the target running path so that the target vehicle bypasses the obstacle.
Whether the adjacent lane corresponding to the current lane meets the preset occupation condition or not is determined, and the obstacle avoidance path planning based on the adjacent lane is carried out on the target vehicle under the condition that the adjacent lane meets the occupation condition, so that the safe and efficient autonomous obstacle avoidance function is favorably realized, the manual dependence of vehicle obstacle avoidance control can be effectively reduced, and the technical effect of automatic escaping of the vehicle can be effectively realized.
According to an embodiment of the present disclosure, the second processing module includes: the first processing submodule is used for determining that the target vehicle is a target driving area required by the bypassing obstacle avoidance; the second processing submodule is used for determining a first target subarea which is positioned in an adjacent lane in the target driving area; the third processing submodule is used for determining whether the first target subregion allows the lane to detour according to the vehicle state information and the driving scene information; and the fourth processing submodule is used for responding to the determination that the occupation of the road is allowed to detour in the first target sub-area, and determining that the adjacent lane meets the occupation condition.
According to an embodiment of the present disclosure, the first processing submodule includes: the first processing unit is used for determining width occupation information of a target driving area according to the boundary information of the obstacle, the width of the target vehicle and a preset safety distance; the second processing unit is used for determining width constraint information of a track coverage area of a center point of the target vehicle when the obstacle avoidance is carried out in a bypassing way according to the width and the width occupation information of the target vehicle; and a third processing unit for determining a target travel area based on at least one of the vehicle state information, the travel scene information, the width occupancy information, and the width constraint information.
According to an embodiment of the present disclosure, the third processing submodule includes: the fourth processing unit is used for determining first estimated time for the target vehicle to enter the first target sub-area according to the vehicle state information; the fifth processing unit is used for determining second estimated time for the obstacle in the adjacent lane to enter the first target sub-area according to the driving scene information; and the sixth processing unit is used for determining whether the first target subarea is allowed to occupy the track and detour according to the first estimated time and the second estimated time.
According to an embodiment of the present disclosure, the sixth processing unit includes: a first processing subunit, configured to determine a time difference between the first estimated time and the second estimated time; and the second processing subunit is used for determining that the occupied track detour is allowed in the first target sub-area under the condition that the time difference value is greater than a preset threshold value.
According to an embodiment of the present disclosure, the sixth processing unit further includes: the third processing subunit is used for determining a first estimated speed of the target vehicle when the target vehicle enters the first target sub-region according to the vehicle state information under the condition that the time difference value is smaller than or equal to the preset threshold value; the fourth processing subunit is used for determining a second estimated speed when the obstacle in the adjacent lane enters the first target sub-region according to the driving scene information; and the fifth processing subunit is used for determining that the occupation detour is allowed in the first target sub-area under the condition that the speed difference value between the first estimated speed and the second estimated speed is greater than a preset threshold value.
According to an embodiment of the present disclosure, the third processing module includes: a fifth processing submodule for determining a second target sub-area within the target travel area indicated by the width constraint information; and the sixth processing submodule is used for carrying out obstacle avoidance path planning on the target vehicle based on the target area so as to enable the center point of the target vehicle to pass through the second target subarea to obtain a target driving path.
According to an embodiment of the present disclosure, a first processing module includes: the seventh processing submodule is used for coding the vehicle state information and the driving scene information to obtain the driving scene characteristics; the eighth processing submodule is used for taking the driving scene characteristics as input data of the scene classification model to obtain a prediction result associated with each driving scene type in at least one driving scene type; and the ninth processing submodule is used for determining the type of the running scene where the target vehicle is located according to the prediction result associated with each running scene type.
According to an embodiment of the present disclosure, the vehicle state information includes vehicle positioning information and vehicle speed information, and the driving scenario information includes at least one of the following information: the system comprises obstacle state information, lane line information, road topology information and traffic indication signals, wherein the obstacle state information comprises obstacle positioning information and obstacle speed information.
It should be noted that in the technical solutions of the present disclosure, the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the related information are all in accordance with the regulations of the related laws and regulations, and do not violate the customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
Fig. 6 schematically shows a block diagram of an electronic device for performing vehicle control according to an embodiment of the present disclosure.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. The electronic device 600 is 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 executes the respective methods and processes described above, such as the vehicle control method. For example, in some embodiments, the vehicle control method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the vehicle control method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the vehicle control method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. 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. A 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 an object, 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 an object; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which objects can provide input to the computer. Other kinds of devices may also be used to provide for interaction with an object; for example, feedback provided to the subject can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the object may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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., an object computer having a graphical object interface or a web browser through which objects can interact with an implementation of the systems and techniques described here), or any combination of such back-end, 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 clients and servers. A client and server are generally 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 with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (21)
1. A vehicle control method comprising:
in response to detecting that an obstacle exists in a current lane where a target vehicle is located to block traveling, determining a traveling scene type where the target vehicle is located according to vehicle state information and traveling scene information associated with the target vehicle;
determining whether an adjacent lane corresponding to the current lane meets a preset occupation condition or not under the condition that the driving scene type indicates that the target vehicle can bypass to avoid the obstacle;
in response to the fact that the adjacent lane meets the occupation condition, performing obstacle avoidance path planning on the target vehicle based on the adjacent lane to obtain a target driving path; and
and controlling the target vehicle to run based on the target running path so that the target vehicle bypasses the obstacle.
2. The method of claim 1, wherein the determining whether an adjacent lane corresponding to the current lane satisfies a preset occupancy condition comprises:
determining that the target vehicle is a target driving area required by detouring obstacle avoidance;
determining a first target sub-area located in the adjacent lane within the target driving area;
determining whether the first target subregion allows for detour according to the vehicle state information and the driving scene information; and
determining that the adjacent lane satisfies the occupancy condition in response to determining that the taking detour is allowed within the first target sub-region.
3. The method of claim 2, wherein the determining that the target vehicle is a target driving area required for detour obstacle avoidance comprises:
determining width occupation information of the target driving area according to the boundary information of the obstacle, the width of the target vehicle and a preset safety distance;
determining width constraint information of a track coverage area of a center point of the target vehicle when the target vehicle bypasses the obstacle avoidance according to the width of the target vehicle and the width occupation information; and
and determining the target driving area according to at least one of the vehicle state information, the driving scene information, the width occupation information and the width constraint information.
4. The method of claim 2, wherein the determining whether to allow for detour within the first target sub-region based on the vehicle state information and the driving scenario information comprises:
determining first estimated time for the target vehicle to enter the first target sub-area according to the vehicle state information;
determining a second estimated time for the obstacle in the adjacent lane to enter the first target sub-area according to the driving scene information; and
and determining whether the first target subregion allows the lane to bypass or not according to the first estimated time and the second estimated time.
5. The method of claim 4, wherein said determining whether to allow for detour within the first target sub-region based on the first and second estimated times comprises:
determining a time difference between the first pre-estimated time and the second pre-estimated time;
and determining that the occupied track detour is allowed in the first target sub-area under the condition that the time difference value is larger than a preset threshold value.
6. The method of claim 5, further comprising:
under the condition that the time difference value is smaller than or equal to a preset threshold value, determining a first estimated speed when the target vehicle enters the first target sub-region according to the vehicle state information;
determining a second estimated speed when the obstacle in the adjacent lane enters the first target sub-area according to the driving scene information; and
and determining that the first target sub-area is allowed to detour when the speed difference value between the first estimated speed and the second estimated speed is larger than a preset threshold value.
7. The method of claim 3, wherein the step of planning an obstacle avoidance path based on the adjacent lane for the target vehicle to obtain a target driving path comprises:
determining a second target sub-area within the target driving area indicated by the width constraint information; and
and planning an obstacle avoidance path of the target vehicle based on the target area so that the center point of the target vehicle passes through the second target subarea to obtain the target driving path.
8. The method of claim 1, wherein the determining a driving scenario type in which the target vehicle is located according to the vehicle state information and the driving scenario information associated with the target vehicle comprises:
coding the vehicle state information and the driving scene information to obtain driving scene characteristics;
taking the driving scene characteristics as input data of a scene classification model to obtain a prediction result associated with each driving scene type in at least one driving scene type; and
and determining the driving scene type of the target vehicle according to the prediction result associated with each driving scene type.
9. The method of any one of claims 1 to 8,
the vehicle state information includes vehicle positioning information and vehicle speed information, and the driving scenario information includes at least one of the following information: obstacle state information, lane line information, road topology information, and traffic indication signals,
the obstacle state information comprises obstacle positioning information and obstacle speed information.
10. A vehicle control apparatus comprising:
the system comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is used for responding to the detection that an obstacle exists in a current lane where a target vehicle is located to block the traveling, and determining the type of a traveling scene where the target vehicle is located according to vehicle state information and traveling scene information which are related to the target vehicle;
the second processing module is used for determining whether an adjacent lane corresponding to the current lane meets a preset occupation condition or not under the condition that the driving scene type indicates that the target vehicle can detour to avoid the obstacle;
the third processing module is used for responding to the fact that the adjacent lane meets the occupation condition, conducting obstacle avoidance path planning on the target vehicle based on the adjacent lane, and obtaining a target driving path;
and the fourth processing module is used for controlling the target vehicle to run based on the target running path so as to enable the target vehicle to detour and avoid the obstacle.
11. The apparatus of claim 10, wherein the second processing module comprises:
the first processing submodule is used for determining that the target vehicle is a target driving area required by the bypassing obstacle avoidance;
the second processing submodule is used for determining a first target subarea which is positioned in the adjacent lane in the target driving area;
the third processing submodule is used for determining whether the first target subregion allows the lane to bypass or not according to the vehicle state information and the driving scene information; and
a fourth processing submodule, configured to determine that the adjacent lane satisfies the occupancy condition in response to determining that the occupied detour is allowed in the first target sub-area.
12. The apparatus of claim 11, wherein the first processing submodule comprises:
the first processing unit is used for determining width occupation information of the target driving area according to the boundary information of the obstacle, the width of the target vehicle and a preset safety distance;
the second processing unit is used for determining width constraint information of a track coverage area of a center point of the target vehicle when the obstacle avoidance is carried out in a bypassing way according to the width of the target vehicle and the width occupation information; and
a third processing unit, configured to determine the target driving area according to at least one of the vehicle state information, the driving scene information, the width occupancy information, and the width constraint information.
13. The apparatus of claim 11, wherein the third processing sub-module comprises:
the fourth processing unit is used for determining first estimated time for the target vehicle to enter the first target sub-area according to the vehicle state information;
the fifth processing unit is used for determining second estimated time for the obstacle in the adjacent lane to enter the first target sub-area according to the driving scene information; and
and the sixth processing unit is used for determining whether the first target subregion allows the lane detour according to the first estimated time and the second estimated time.
14. The apparatus of claim 13, wherein the sixth processing unit comprises:
a first processing subunit, configured to determine a time difference between the first estimated time and the second estimated time;
and the second processing subunit is used for determining that the occupation of the track is allowed to detour in the first target sub-area under the condition that the time difference value is greater than a preset threshold value.
15. The apparatus of claim 14, the sixth processing unit further comprising:
the third processing subunit is configured to, when the time difference is smaller than or equal to a preset threshold, determine, according to the vehicle state information, a first estimated speed of the target vehicle when the target vehicle enters the first target sub-region;
the fourth processing subunit is configured to determine, according to the driving scene information, a second estimated speed when the obstacle in the adjacent lane enters the first target sub-region; and
and the fifth processing subunit is used for determining that the first target sub-area allows the occupied road to bypass under the condition that the speed difference value between the first estimated speed and the second estimated speed is greater than a preset threshold value.
16. The apparatus of claim 12, wherein the third processing module comprises:
a fifth processing submodule for determining a second target sub-area within the target travel area indicated by the width constraint information; and
and the sixth processing submodule is used for carrying out obstacle avoidance path planning on the target vehicle based on the target area so as to enable the center point of the target vehicle to pass through the second target subarea to obtain the target driving path.
17. The apparatus of claim 10, wherein the first processing module comprises:
the seventh processing submodule is used for coding the vehicle state information and the driving scene information to obtain the driving scene characteristics;
the eighth processing submodule is used for taking the driving scene characteristics as input data of a scene classification model to obtain a prediction result associated with each driving scene type in at least one driving scene type; and
and the ninth processing submodule is used for determining the type of the running scene where the target vehicle is located according to the prediction result associated with each running scene type.
18. The apparatus of any one of claims 10 to 17,
the vehicle state information includes vehicle positioning information and vehicle speed information, and the driving scenario information includes at least one of the following information: obstacle state information, lane line information, road topology information, and traffic indication signals,
the obstacle state information comprises obstacle positioning information and obstacle speed information.
19. An electronic device, comprising:
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 the method of any one of claims 1-9.
20. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any of claims 1-9.
21. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 9.
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