CN112859829B - Vehicle control method and device, electronic equipment and medium - Google Patents
Vehicle control method and device, electronic equipment and medium Download PDFInfo
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- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
- G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
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- G05D1/02—Control of position or course in two dimensions
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- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G05D1/02—Control of position or course in two dimensions
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- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
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Abstract
The embodiment of the application discloses a vehicle control method, a vehicle control device, electronic equipment and a medium, and relates to the technical field of automatic driving. The vehicle control method includes: determining whether the vehicle is in a special scene or not in response to the acquired positioning data of the vehicle and the perception data of the vehicle; and if the vehicle is in the special scene, planning a target running track of the vehicle passing through the special scene according to the perception data of the vehicle. Through the technical scheme of the embodiment of the application, the safe and stable running of the automatic driving vehicle on the special road can be accurately ensured.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to the technical field of automatic driving, and specifically relates to a vehicle control method, a vehicle control device, electronic equipment and a medium.
Background
In the actual operation and use process of the automatic driving vehicle, various road environments can be encountered, wherein narrow roads are very special road conditions. Particularly, in a closed or semi-closed scene, for example, in the process of tasks such as automatic driving logistics distribution, automatic driving commodity selling, automatic driving safety patrol, automatic driving road cleaning and the like of a low-speed automatic driving logistics car in a park, an industrial park, a campus, a large enterprise factory and the like.
However, currently, most of the automatic driving vehicles are limited to travel on a road with a specific width, and the following two processing methods are adopted when the vehicle travels to a narrow road: 1) Directly parking; 2) Driving with difficulty in a manner that is highly uncertain in terms of safety.
However, the following disadvantages exist in both of these ways: 1) The first method is a safe processing method, but is not a reasonable method in terms of operation requirements and development of automatic driving technology; 2) The second mode is that the vehicle may be stuck by a road boundary or an obstacle at any time or an unexpected risk may occur at any time. It is therefore important to provide a solution that ensures safe driving of autonomous vehicles on narrow roads.
Disclosure of Invention
The embodiment of the application discloses a vehicle control method, a vehicle control device, electronic equipment and a medium, which can accurately ensure that an automatic driving vehicle can safely and stably run on a special road.
In a first aspect, an embodiment of the present application discloses a vehicle control method, including:
responding to the acquired positioning data of the vehicle, the acquired perception data of the vehicle and the map data, and determining whether the vehicle is in a special scene;
and if the vehicle is in the special scene, planning a target running track of the vehicle passing through the special scene based on the perception data of the vehicle.
One embodiment in the above application has the following advantages or benefits: whether the vehicle is in a special scene or not can be determined by analyzing the comprehensive map data, the positioning data of the vehicle and the perception data of the vehicle; and when the vehicle is in a special scene, the target driving track of the vehicle passing through the special scene can be planned according to the perception data of the vehicle, and then the vehicle can be controlled to drive along the planned target driving track in the special scene, so that a new idea is provided for safe and stable driving of the automatic driving vehicle on a special road, and the automatic driving scene is expanded.
Optionally, determining whether the vehicle is in a special scene in response to the acquired positioning data of the vehicle, the acquired perception data of the vehicle, and the map data, includes:
determining the actual width of the road where the vehicle is located according to the map data and the positioning data of the vehicle;
determining obstacle attribute information according to the perception data of the vehicle;
determining the passable width of the road where the vehicle is located at present according to the actual width and the obstacle attribute information;
and determining whether the vehicle is currently in a special scene or not according to the passable width of the road where the vehicle is currently located.
The above alternative has the following advantages or benefits: by integrating the map data, the positioning data of the vehicle, the perception data of the vehicle and other data, the passable width of the road where the vehicle is located can be determined, and then whether the vehicle is in a special scene or not is determined, and a mode is provided for accurately determining whether the vehicle is in the special scene or not.
Optionally, planning a target driving track of the vehicle through the special scene based on the perception data of the vehicle, including:
determining obstacle attribute information according to the perception data of the vehicle;
and constructing an obstacle boundary according to the obstacle attribute information, and planning a target running track of the vehicle passing through the special scene according to the obstacle boundary.
The above alternative has the following advantages or benefits: the method comprises the steps of fully considering the obstacle attribute information of the scene where the vehicle is located, constructing the obstacle boundary according to the obstacle attribute information, and then planning the target running track of the vehicle passing through the special scene according to the constructed obstacle boundary, so that the planned target running track is ensured to be consistent with the actual scene, and the safe and stable running of the automatic driving vehicle on the special road can be accurately ensured.
Optionally, planning a target driving track of the vehicle through the special scene based on the perception data of the vehicle, including:
determining obstacle attribute information according to the perception data of the vehicle;
planning a candidate driving track of the vehicle passing through the special scene;
and selecting a target running track of the vehicle passing through the special scene from the candidate running tracks according to the obstacle attribute information.
The above alternative has the following advantages or benefits: by means of the obstacle attribute information determined according to the perception data of the vehicle, a target driving track of the vehicle passing through a special scene can be selected from a plurality of candidate driving tracks of the planned vehicle passing through the special scene, and a mode for determining the optimal track is provided.
In a second aspect, an embodiment of the present application discloses a vehicle control apparatus, including:
the special scene determining module is used for responding to the acquired positioning data of the vehicle, the acquired perception data of the vehicle and the map data and determining whether the vehicle is in a special scene;
and the target track planning module is used for planning a target running track of the vehicle passing through the special scene based on the perception data of the vehicle if the vehicle is in the special scene.
In a third aspect, an embodiment of the present application further discloses an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
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 any embodiment of the present application.
In a fourth aspect, embodiments of the present application further disclose a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute a vehicle control method according to any of the embodiments of the present application.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a flow chart of a vehicle control method provided in accordance with a first embodiment of the present application;
FIG. 2A is a flow chart of a vehicle control method provided in accordance with a second embodiment of the present application;
FIGS. 2B and 2C are schematic diagrams of a special scenario provided in accordance with a second embodiment of the present application;
FIG. 2D is a schematic view of an obstacle and a vehicle in SL coordinates according to a second embodiment of the present application;
FIG. 2E is a schematic diagram of the boundary of an obstacle at SL coordinates according to a second embodiment of the application;
FIG. 3A is a flowchart of a vehicle control method provided in accordance with a third embodiment of the present application;
FIG. 3B is a schematic view of an irregular shape of a planar polygon of an obstacle according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of a vehicle control apparatus provided in accordance with a fourth embodiment of the present application;
fig. 5 is a block diagram of an electronic device for implementing the vehicle control method according to the embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. 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 application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
First embodiment
Fig. 1 is a flowchart of a vehicle control method according to a first embodiment of the present application, which is applicable to a situation how to ensure safe driving of a vehicle on a special road, where the vehicle may be a general vehicle driving on the road or an autonomous vehicle. The method may be performed by a vehicle control device, which may be implemented in software and/or hardware, and may be integrated on an autonomous vehicle, further integrated on a decision-making planning module of the autonomous vehicle. As shown in fig. 1, the vehicle control method provided by the present embodiment may include:
and S110, responding to the acquired positioning data of the vehicle, the acquired perception data of the vehicle and the map data, and determining whether the vehicle is in a special scene.
Optionally, a positioning system, a sensing device, a high-precision map module and the like may be integrated in the vehicle. The sensing device includes, but is not limited to, a laser radar, an image collector, and the like. Furthermore, the positioning data of the vehicle in this embodiment may be position data of the vehicle collected by a positioning system in the vehicle, and may include latitude and longitude information; the perception data of the vehicle can comprise point cloud data collected by a laser radar in the vehicle, image data collected by an image collector in the vehicle and the like; the map data may be data of a scene where the vehicle is loaded by the high-precision map module, for example, if the vehicle travels in a certain enterprise campus, the map data may be map data of a corresponding campus loaded by the high-precision map module.
Optionally, in the running process of the vehicle, positioning data of the vehicle may be collected in real time by a positioning system in the vehicle, sensing data of a road environment where the vehicle is located may be collected by sensing equipment in the vehicle, and map data of a scene where the vehicle is located, such as a certain park, may be loaded by the high-precision map module; then, the vehicle is mapped in the map data according to the positioning data of the vehicle, the map information of the current position of the vehicle and the front road can be obtained, and whether the vehicle is in a special scene or not can be determined by combining the map information of the current position of the vehicle and the front road and the perception data of the vehicle. The special scene is that the width of a road where the vehicle can pass is narrow; alternatively, the criterion of the narrower road width may be that the road width that the vehicle can pass through is within a preset range.
For example, in response to the acquired positioning data of the vehicle and the perception data of the vehicle, and the map data, determining whether the vehicle is in a special scene may include:
A. determining the actual width of the road where the vehicle is located according to the map data and the positioning data of the vehicle;
in this embodiment, the actual width of the road where the vehicle is currently located is the width marked on the map of the road where the vehicle is currently located. Specifically, the vehicle is mapped in the map data according to the positioning data of the vehicle, so that the map information of the current position and the front road of the vehicle can be obtained, and the actual width of the current road of the vehicle can be obtained according to the current position and the map information of the front road of the vehicle.
B. Determining obstacle attribute information according to perception data of the vehicle;
in this embodiment, the obstacle attribute information may include, but is not limited to, the type of obstacle (such as a pedestrian, a vehicle, or a tree), the type of obstacle (such as a static obstacle and a dynamic obstacle), the position, the size, the moving direction, the relative moving speed, and the like. Optionally, a sensing module may be further integrated in the vehicle, and configured to perform detection and analysis on sensing data of the vehicle, such as point cloud data and/or image data, acquired by the sensing device, so as to determine obstacle attribute information in a safe driving space of the vehicle in the driving direction of the vehicle.
C. Determining the passable width of the road where the vehicle is located at present according to the actual width and the attribute information of the obstacles;
optionally, the passable width of the road where the vehicle is currently located is smaller than or equal to the actual width of the road where the vehicle is currently located. For example, if there are no obstacles on both sides of the road where the vehicle is currently located, it may be determined that the passable width of the road where the vehicle is currently located is equal to the actual width of the road where the vehicle is currently located; otherwise, determining that the passable width of the road where the vehicle is located is smaller than the actual width of the road where the vehicle is located. Specifically, after determining the actual width of the road on which the vehicle is currently located and the obstacle attribute information, the passable width of the road on which the vehicle is currently located may be determined according to the actual width of the road on which the vehicle is currently located and the obstacle attribute information (such as the position and size of the obstacle).
D. And determining whether the vehicle is currently in a special scene or not according to the passable width of the road where the vehicle is currently located.
Specifically, if the passable width of the road where the vehicle is currently located is within a preset special width range, it is determined that the vehicle is currently located in a special scene. The lower limit value of the special width range is the sum of the vehicle width and a first threshold value, the upper limit value of the special width range is the sum of the vehicle width and a second threshold value, the first threshold value and the second threshold value can be set according to actual conditions, and the second threshold value is larger than the first threshold value. Alternatively, the second threshold may be a buffer value where the vehicle traffic is relatively safe, and the first threshold may be a minimum value of the vehicle traffic.
And S120, if the vehicle is in the special scene, planning a target running track of the vehicle passing through the special scene based on the perception data of the vehicle.
Specifically, if it is determined that the vehicle is in the special scene, the target driving track of the vehicle passing through the special scene may be planned according to the perception data of the vehicle. For example, if it is determined that the vehicle is within a preset distance range forward along the driving direction on the current road according to the sensing data of the vehicle and no obstacles are on two sides of the road, it is determined that the special scene where the vehicle is located is the first special scene; in this case, the road reference line of the road where the vehicle is located may be used as the target driving track of the vehicle passing through the special scene, and the vehicle may be controlled to drive along the target driving track in the special scene. The road reference line is a standard for vehicle trajectory planning, and is defaulted to be a central line of a road geometric lane.
If the static and/or dynamic obstacles exist on the road where the vehicle is located according to the perception data of the vehicle, determining that the special scene where the vehicle is located is a second special scene; the second special scene is a scene that the road is wide enough and the passable width formed by static and/or dynamic barriers is within the preset special width range; at this time, a target travel track of the vehicle through the special scene may be planned according to the obstacle attribute information determined by the perception data of the vehicle.
For example, after the target driving track of the vehicle passing through the special scene is planned, the vehicle may be controlled to drive along the planned target driving track at the planned speed in the special scene. Alternatively, a conventional speed planning method may be used for speed planning. Further, if the special scene where the vehicle is located is the first special scene, the vehicle may be controlled to travel along the planned target travel track in the first special scene under the condition that the vehicle is less than or equal to the maximum vehicle speed according to the control error of the system, the road width (that is, the actual width of the road where the vehicle is currently located, and may also be the passable width of the road where the vehicle is currently located), the positioning error, the curve curvature of the road where the vehicle is currently located, and the like.
In addition, for the first special scene, if other vehicles running in the same direction exist in front of the vehicle, the vehicle can follow the vehicle at a low speed; if a vehicle encounters a crossing obstacle such as a pedestrian or other vehicle during driving or a static obstacle, a parking avoidance strategy may be adopted.
For the second special scene, the obstacle attribute information in the actual scene is fully considered when the target running track of the vehicle is planned, so that the vehicle can be directly controlled to run along the planned target running track in the second special scene.
According to the technical scheme provided by the embodiment of the application, whether the vehicle is in a special scene or not can be determined by analyzing the comprehensive map data, the positioning data of the vehicle and the perception data of the vehicle; and when the vehicle is in a special scene, the target driving track of the vehicle passing through the special scene can be planned according to the perception data of the vehicle, and then the vehicle can be controlled to drive along the planned target driving track in the special scene, so that a new idea is provided for safe and stable driving of the automatic driving vehicle on a special road, and the automatic driving scene is expanded.
Second embodiment
Fig. 2A is a flowchart of a vehicle control method according to a second embodiment of the present application, and this embodiment provides a scheme for planning a target travel track of a vehicle through a special scene according to perception data of the vehicle on the basis of the above embodiment. As shown in fig. 2A, the vehicle control method provided by the present embodiment may include:
and S210, responding to the acquired positioning data of the vehicle, the acquired perception data of the vehicle and the map data, and determining whether the vehicle is in a special scene.
And S220, if the vehicle is in a special scene, determining the attribute information of the obstacle according to the perception data of the vehicle.
And S230, constructing an obstacle boundary according to the obstacle attribute information, and planning a target running track of the vehicle passing through a special scene according to the obstacle boundary.
Optionally, for a second scene where the road itself is wide enough and is formed by static and/or dynamic obstacles, an obstacle boundary may be constructed according to the obstacle attribute information determined by the perception data of the vehicle, and then a target driving track of the vehicle passing through the special scene may be planned according to the constructed obstacle boundary. It should be noted that, due to the moving characteristics of the dynamic obstacle, the embodiment is more suitable for a scene where the road itself is wide enough and the passable width formed by the static obstacle is within the preset special width range. For example, as shown in fig. 2B, the road is wide enough, and a large number of static obstacles obs exist on one side of the road, so as to form a scene with a passable width within a preset special width range; or as shown in fig. 2C, the road is wide enough, and irregular road boundaries are formed due to flowers, plants, deposits and the like extending from both sides of the road, so that a scene with a passable width within a preset special width range is formed.
In this embodiment, the planning of the target driving trajectory of the vehicle is dynamically planned in a section during the driving process of the vehicle, and the same is true for constructing the boundary of the obstacle. The length of each planned road section can be determined according to the speed of the vehicle and the set duration. Optionally, the building of the obstacle boundary according to the obstacle attribute information in each planning process may be: obtaining the attribute information of the obstacles in the length of the planned road section, and then connecting the coordinates of the obstacles close to the inner side of the vehicle driving direction according to the sequence of the obstacles from near to far from the vehicle, so as to obtain the boundary of the obstacles. The method specifically comprises the following steps: 1) And determining the longitudinal distance between the obstacle and the vehicle and the transverse offset between the obstacle and the road reference line according to the obstacle attribute information. The road reference line is a standard for vehicle trajectory planning, and is defaulted to be a central line of a road geometric lane.
Specifically, the obstacle may be mapped in the map data according to the position of the obstacle in the obstacle attribute information; and then, according to the current position of the vehicle in the map data, the position of the obstacle and the position of the road reference line, determining the longitudinal distance between the obstacle and the vehicle and the transverse offset between the obstacle and the road reference line. The longitudinal distance between the obstacle and the vehicle can be directly determined according to the positioning data of the vehicle, the position of the obstacle in the obstacle attribute information and the like; then, the obstacle can be placed under an SL coordinate system according to the information of the position, the size and the like of the obstacle in the obstacle attribute information and the longitudinal distance between the obstacle and the vehicle; meanwhile, the vehicle is also placed under an SL coordinate system according to the positioning data of the vehicle, as shown in FIG. 2D, at this time, the road reference line is coincident with the S axis; the lateral offset between the obstacle and the road reference line can then be determined visually in SL coordinates. It should be noted that, by default, in the SL coordinate system, the lateral offset of a point on the left boundary of the road is + L, the lateral offset of a point on the right boundary of the road is-L, and the lateral offset of a point on the reference line of the road is 0.
2) The obstacles are sorted according to the determined longitudinal distance.
Specifically, after the longitudinal distance between the obstacle and the vehicle is determined, the obstacles may be sorted from near to far according to the longitudinal distance between the obstacle and the vehicle, that is, the position relationship between the obstacle and the vehicle in the real scene is followed. In the method of intuitively determining the lateral offset between the obstacle and the road reference line in the SL coordinate system, the obstacles in the SL coordinate system are sorted according to the determined longitudinal distance.
3) And constructing the boundary of the obstacles according to the transverse offset between the sequenced obstacles and the road reference line.
Specifically, the obstacle boundary may be constructed according to the lateral offset between the sorted obstacles and the road reference line. For example, the obstacles in the SL coordinate system may be sequentially connected according to the lateral offset between the sorted obstacles and the road reference line, so as to obtain the obstacle boundary, as shown in fig. 2E. Optionally, when obstacles exist on both sides of a road on which the vehicle is located, sorting the obstacles on each side according to the determined longitudinal distance; and simultaneously, constructing the boundary of the obstacles on the side according to the transverse offset between the sequenced obstacles on the side and the road reference line.
In this embodiment, after constructing the obstacle boundary according to the obstacle attribute information, in order to enable the vehicle to run smoothly in the situation as shown in fig. 2B or 2C, for example, the target running track of the vehicle through the special scene according to the obstacle boundary may be: reconstructing a road reference line according to the boundary of the obstacle and/or the boundary of the road where the vehicle is located; and planning a target running track of the vehicle passing through a special scene according to the reconstructed road reference line.
The description will be given taking the vehicle in the scene shown in fig. 2B as an example. The constructed barrier boundary can be used for replacing a real road boundary on one side of the road to form a virtual road boundary; and then selecting a point at a middle position between the virtual road boundary and the real road boundary at the other side of the road as a new road reference line control point, performing curve fitting, smoothing and other processing on a series of new road reference line control points, and further generating a new road reference line serving as a reference for trajectory planning and used for guiding trajectory planning. Optionally, in this embodiment, after reconstructing the road reference line, a target driving trajectory of the vehicle through the special scene may be planned by using a conventional trajectory planning manner, such as a dynamic trajectory planning manner or a secondary trajectory planning manner, according to the reconstructed road reference line.
For example, the target driving track of the vehicle through the special scene according to the obstacle boundary may also be: solving a track function according to the positioning data of the obstacle boundary and the vehicle; and determining the target running track of the vehicle passing through the special scene according to the solving result.
In this embodiment, the trajectory function may be a polynomial function, such as a polynomial function of degree 5.
The vehicle is illustrated in the scenario shown in fig. 2C as an example. For the planned driving track of one section of road each time, the constructed boundaries of the left and right side obstacles can be placed under an SL coordinate system, and the vehicle is placed under the SL coordinate system according to the positioning data of the vehicle; and then taking the constructed boundaries of the left and right obstacles, the current position of the vehicle and the length of the road section as constraint conditions of a track function, and solving an optimal track of the target by a quadratic programming method, namely determining the target running track of the vehicle passing through a special scene.
According to the technical scheme, the obstacle attribute information of the scene where the vehicle is located is fully considered, the obstacle boundary is constructed according to the obstacle attribute information, the target driving track of the vehicle passing through the special scene is planned according to the constructed obstacle boundary, the planned target driving track is ensured to be consistent with the actual scene, and the safe and stable driving of the automatic driving vehicle on the special road can be accurately ensured.
Third embodiment
Fig. 3A is a flowchart of a vehicle control method according to a third embodiment of the present application, and this embodiment provides a scheme for planning a target driving trajectory of a vehicle passing through a special scene according to perception data of the vehicle on the basis of the above embodiments. As shown in fig. 3A, the vehicle control method provided by the present embodiment may include:
and S310, responding to the acquired positioning data of the vehicle, the acquired perception data of the vehicle and the map data, and determining whether the vehicle is in a special scene.
And S320, if the vehicle is in a special scene, determining the attribute information of the obstacle according to the perception data of the vehicle.
And S330, planning the candidate running track of the vehicle passing through the special scene.
In this embodiment, a plurality of candidate driving trajectories may be planned in a conventional trajectory planning manner, such as a dynamic trajectory planning manner or a secondary trajectory planning manner.
And S340, selecting a target running track of the vehicle passing through a special scene from the candidate running tracks according to the obstacle attribute information.
Alternatively, the present embodiment may be applied to a second special scene formed by static and/or dynamic obstacles, which is wide enough for the road itself, and is particularly applicable to a scene formed by scattered static obstacles and/or dynamic obstacles, which has a passable width within a preset special width range. Further, when the scene has scattered static obstacles, the static obstacles may be regarded as dynamic obstacles. Due to the moving characteristics of the dynamic obstacle, the present embodiment may determine the collision risk that may occur when the vehicle travels along each candidate travel track according to the type, position, size, and the like of the obstacle in the obstacle attribute information, and further, according to the determined collision risk, use the travel track with the lowest collision risk in the candidate travel tracks as the target travel track of the vehicle through the special scene.
For example, selecting a target travel track of the vehicle through the special scene from the candidate travel tracks according to the obstacle attribute information may include: 1) And determining the obstacle species coefficient and the transverse distance between the obstacle and the vehicle according to the obstacle attribute information.
In this embodiment, the obstacle species may be a pedestrian, a vehicle, or a tree, etc.; optionally, the types of obstacles are different, and the coefficients of the types of obstacles are different.
Specifically, the type of the obstacle can be determined according to the attribute information of the obstacle, and then the obstacle type coefficient can be determined according to the type of the obstacle; meanwhile, the transverse distance between the obstacle and the vehicle can be determined according to the positioning data of the vehicle, the position and the size of the obstacle in the obstacle attribute information and the like. For example, the obstacle may be placed under the SL coordinate system according to the position, size, and the like of the obstacle in the obstacle attribute information; meanwhile, the vehicle is also placed under the SL coordinate system according to the positioning data of the vehicle, and then the transverse offset between the obstacle and the vehicle can be visually determined in the SL coordinate.
2) And determining a cost value of each candidate driving track based on the cost function according to the obstacle species coefficient and the transverse distance between the obstacle and the vehicle.
In this embodiment, the Cost function Cost (adc, obs) is used to represent the collision Cost between the vehicle adc and the obstacle obs, and the collision Cost is proportional to the obstacle species coefficient P1 and inversely proportional to the lateral distance d between the obstacle obs and the vehicle adc. In addition, the cost function is also proportional to the vehicle type coefficient P2. Further, the cost function can be expressed as: cost (adc, obs) = G × P1 × P2/(d × d). Wherein G is a basic weight parameter, and G, P and P2 in this embodiment can be set according to actual situations.
Specifically, for each candidate driving track, the obstacle included in the candidate driving track may be determined first; then, based on the cost function, sequentially calculating the collision cost between each obstacle and the vehicle contained in the candidate driving track according to the obstacle species coefficient and the transverse distance between the obstacle and the vehicle; and then adding the collision cost between each obstacle contained in the candidate running track and the vehicle, and taking the addition result as the cost value of the candidate running track.
For example, determining the cost value of each candidate driving trajectory based on the cost function according to the obstacle species coefficient and the lateral distance between the obstacle and the vehicle may include: determining collision cost between each obstacle and the vehicle contained in each candidate driving track based on a cost function according to the obstacle species coefficient and the transverse distance between the obstacle and the vehicle; determining a cost value of each candidate driving track according to the determined collision cost between each obstacle and the vehicle.
It should be noted that the cost function adopted in this embodiment can further refine and distinguish collision costs of different types of obstacles, and is further more favorable for a vehicle to safely pass through even though the vehicle is very close to the obstacle.
In an actual scene, due to the fact that the parking pose of the obstacle and the sensing result are unstable, the situation that the shape of the plane polygon of the obstacle is irregular can occur. In this case, if the collision cost between the obstacle and the vehicle is calculated by directly using the obstacle as one obstacle, the accuracy of the calculated collision cost is low. For example, as shown in fig. 3B, the obstacle ob1 has an irregular planar polygon shape, the frame M of the obstacle ob1 is larger than the actual obstacle ob1 in the SL coordinate system, and the larger the obstacle is, the larger the influence is, the more adverse for the vehicle owner to approach the obstacle at a close distance to pass through. Therefore, in order to allow the vehicle to approach the obstacle in a short distance for passing under a special scene, for example, determining the collision cost between each obstacle and the vehicle included in each candidate driving trajectory based on the cost function according to the obstacle species coefficient and the lateral distance between the obstacle and the vehicle may include: for each obstacle, if the plane polygon shape of the obstacle is irregular, the obstacle can be divided into a plurality of small obstacles, then frames are independently added to each small obstacle, the collision cost between each small obstacle and the vehicle is independently calculated, and finally the calculated collision cost between each small obstacle and the vehicle is added to be used as the collision cost between the obstacle and the vehicle.
3) And determining the target running track of the vehicle passing through the special scene according to the cost value of each candidate running track.
Specifically, the least expensive driving track in the candidate driving tracks may be used as the target driving track of the vehicle passing through the special scene, and the vehicle may be controlled to drive along the target driving track in the special scene.
According to the technical scheme provided by the embodiment of the application, the target running track of the vehicle passing through the special scene can be selected from a plurality of candidate running tracks of the planned vehicle passing through the special scene through the obstacle attribute information determined according to the vehicle perception data, and a mode for determining the optimal track is provided.
Fourth embodiment
Fig. 4 is a schematic structural diagram of a vehicle control device according to a fourth embodiment of the present application, which is capable of executing the vehicle control method provided in any embodiment of the present application, and has corresponding functional modules and beneficial effects. Optionally, the apparatus may be implemented in software and/or hardware, and may be integrated on an autonomous vehicle, and further may be integrated on a decision planning module of the autonomous vehicle. As shown in fig. 4, the apparatus 400 may include:
a special scene determining module 410, configured to determine whether the vehicle is in a special scene in response to the acquired positioning data of the vehicle and the perception data of the vehicle, and the map data;
and the target track planning module 420 is configured to plan a target driving track of the vehicle passing through the special scene based on the perception data of the vehicle if the vehicle is in the special scene.
According to the technical scheme provided by the embodiment of the application, whether the vehicle is in a special scene or not can be determined by analyzing the comprehensive map data, the positioning data of the vehicle and the perception data of the vehicle; and when the vehicle is in a special scene, the target driving track of the vehicle passing through the special scene can be planned according to the perception data of the vehicle, and then the vehicle can be controlled to drive along the planned target driving track in the special scene, so that a new idea is provided for safe and stable driving of the automatic driving vehicle on a special road, and the automatic driving scene is expanded.
As an example, the special scenario determination module 410 may be specifically configured to:
determining the actual width of the road where the vehicle is located according to the map data and the positioning data of the vehicle;
determining obstacle attribute information according to perception data of the vehicle;
determining the passable width of the road where the vehicle is located at present according to the actual width and the obstacle attribute information;
and determining whether the vehicle is currently in a special scene or not according to the passable width of the road where the vehicle is currently located.
For example, the target trajectory planning module 420 may include:
the attribute information determining unit is used for determining the attribute information of the obstacle according to the perception data of the vehicle;
the boundary construction unit is used for constructing an obstacle boundary according to the obstacle attribute information;
and the target track planning unit is used for planning a target running track of the vehicle passing through a special scene according to the barrier boundary.
For example, the target trajectory planning module 420 may include:
the attribute information determining unit is used for determining the attribute information of the obstacle according to the perception data of the vehicle;
the candidate track planning unit is used for planning candidate running tracks of the vehicle passing through a special scene;
and the target track selection unit is used for selecting a target running track of the vehicle passing through the special scene from the candidate running tracks according to the obstacle attribute information.
Illustratively, the boundary building unit may be specifically configured to:
according to the attribute information of the obstacles, determining the longitudinal distance between the obstacles and the vehicle and the transverse offset between the obstacles and the road reference line;
sorting the obstacles according to the determined longitudinal distance;
and constructing the boundary of the obstacles according to the transverse offset between the sequenced obstacles and the road reference line.
For example, the target trajectory planning unit may be specifically configured to:
reconstructing a road reference line according to the boundary of the obstacle and/or the boundary of the road where the vehicle is located;
and planning a target running track of the vehicle passing through a special scene according to the reconstructed road reference line.
For example, the target trajectory planning unit may be further configured to:
solving a track function according to the obstacle boundary and the data of the vehicle positioning;
and determining the target running track of the vehicle passing through the special scene according to the solving result.
Illustratively, the target trajectory selection unit may be specifically configured to:
determining a barrier species coefficient and a transverse distance between the barrier and the vehicle according to the barrier attribute information;
determining a cost value of each candidate driving track based on a cost function according to the obstacle species coefficient and the transverse distance between the obstacle and the vehicle; the cost function is used for representing collision cost between the vehicle and the obstacle, and the collision cost is in direct proportion to the obstacle species coefficient and in inverse proportion to the transverse distance between the obstacle and the vehicle;
and determining the target running track of the vehicle according to the cost value of each candidate running track.
For example, the target trajectory selection unit, when determining the cost value of each candidate driving trajectory based on the cost function according to the obstacle species coefficient and the lateral distance between the obstacle and the vehicle, may specifically be configured to:
determining collision cost between each obstacle and the vehicle contained in each candidate driving track based on a cost function according to the obstacle species coefficient and the transverse distance between the obstacle and the vehicle;
determining a cost value of each candidate driving track according to the determined collision cost between each obstacle and the vehicle.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 5, is a block diagram of an electronic device of a vehicle control method according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular 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 present application that are described and/or claimed herein.
As shown in fig. 5, the electronic apparatus includes: one or more processors 501, memory 502, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display Graphical information for a Graphical User Interface (GUI) on an external input/output device, such as a display device coupled to the Interface. In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations, e.g., as a server array, a group of blade servers, or a multi-processor system. In fig. 5, one processor 501 is taken as an example.
The memory 502, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the vehicle control method in the embodiments of the present application, for example, the special scene determination module 410 and the target trajectory planning module 420 shown in fig. 4. The processor 501 executes various functional applications of the server and vehicle control, i.e., implements the vehicle control method in the above-described method embodiments, by executing non-transitory software programs, instructions, and modules stored in the memory 502.
The memory 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device used to implement the vehicle control method, and the like. Further, the memory 502 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 502 optionally includes memory located remotely from the processor 501, which may be connected via a network to electronics used to implement the vehicle control method. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device for implementing the vehicle control method may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function control of an electronic apparatus used to implement the vehicle control method, such as an input device of a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or the like. The output device 604 may include a display apparatus, an auxiliary lighting device such as a Light Emitting Diode (LED), a tactile feedback device such as a vibration motor, and the like. The Display device may include, but is not limited to, a Liquid Crystal Display (LCD), an LED Display, and a plasma Display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application Specific Integrated Circuits (ASICs), 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.
These computer programs, also known as programs, software applications, or code, include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or Device for providing machine instructions and/or data to a Programmable processor, such as a magnetic disk, optical disk, memory, programmable Logic Device (PLD), including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device for displaying information to a user, for example, a Cathode Ray Tube (CRT) or an LCD monitor; and a keyboard and a pointing device, such as a mouse or a trackball, by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here may be implemented in a computing system that includes a back-end component, e.g., as a data server; or in a computing system that includes middleware components, e.g., an application server; or in a computing system that includes a front-end component, e.g., a user computer with a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein, or in a computing system that includes 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.
According to the technical scheme of the embodiment of the application, whether the vehicle is in a special scene or not can be determined by analyzing the comprehensive map data, the positioning data of the vehicle and the perception data of the vehicle; and when the vehicle is in a special scene, the target driving track of the vehicle passing through the special scene can be planned according to the perception data of the vehicle, and then the vehicle can be controlled to drive along the planned target driving track in the special scene, so that a new idea is provided for safe and stable driving of the automatic driving vehicle on a special road, and the automatic driving scene is expanded.
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 application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. 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 application shall be included in the protection scope of the present application.
Claims (10)
1. A vehicle control method characterized by comprising:
determining whether the vehicle is in a special scene or not in response to the acquired positioning data of the vehicle and perception data of the vehicle and map data;
if the vehicle is in the special scene, planning a target running track of the vehicle passing through the special scene based on perception data of the vehicle;
wherein the planning of the target driving trajectory of the vehicle through the special scene based on the perception data of the vehicle comprises:
determining obstacle attribute information according to the perception data of the vehicle;
planning a candidate driving track of the vehicle passing through the special scene;
determining a barrier species coefficient and a transverse distance between a barrier and the vehicle according to the barrier attribute information;
determining a cost value of each candidate driving track based on a cost function according to the obstacle species coefficient and the transverse distance between the obstacle and the vehicle; wherein the cost function is used to characterize a cost of collision between the vehicle and the obstacle, the cost of collision being proportional to the obstacle class coefficient and inversely proportional to a lateral distance between the obstacle and the vehicle;
and determining a target running track of the vehicle passing through the special scene according to the cost value of each candidate running track.
2. The method of claim 1, wherein determining whether the vehicle is in a special scene in response to the acquired positioning data of the vehicle and perception data of the vehicle, and map data, comprises:
determining the actual width of the road where the vehicle is located according to the map data and the positioning data of the vehicle;
determining obstacle attribute information according to the perception data of the vehicle;
determining the passable width of the road where the vehicle is located at present according to the actual width and the obstacle attribute information;
and determining whether the vehicle is currently in a special scene or not according to the passable width of the road where the vehicle is currently located.
3. The method of claim 1, wherein planning a target travel trajectory of the vehicle through the special scene based on the perception data of the vehicle further comprises:
determining obstacle attribute information according to the perception data of the vehicle;
and constructing an obstacle boundary according to the obstacle attribute information, and planning a target running track of the vehicle passing through the special scene according to the obstacle boundary.
4. The method of claim 3, wherein constructing an obstacle boundary from the obstacle attribute information comprises:
according to the obstacle attribute information, determining a longitudinal distance between an obstacle and the vehicle and a transverse offset between the obstacle and a road reference line;
sorting the obstacles according to the determined longitudinal distance;
and constructing the boundary of the obstacles according to the transverse offset between the sequenced obstacles and the road reference line.
5. The method of claim 3, wherein planning a target travel trajectory of the vehicle through the special scene according to the obstacle boundaries comprises:
reconstructing a road reference line according to the barrier boundary and/or the road boundary where the vehicle is located;
and planning a target running track of the vehicle passing through the special scene according to the reconstructed road reference line.
6. The method of claim 3, wherein planning a target travel path of the vehicle through the special scene according to the obstacle boundaries comprises:
solving a track function according to the obstacle boundary and the positioning data of the vehicle;
and determining the target running track of the vehicle passing through the special scene according to the solving result.
7. The method of claim 1, wherein determining a cost value for each candidate driving trajectory based on a cost function based on the obstacle species coefficient and a lateral distance between the obstacle and the vehicle comprises:
determining a collision cost between each obstacle included in each candidate driving track and the vehicle based on a cost function according to the obstacle species coefficient and the transverse distance between the obstacle and the vehicle;
determining a cost value for each candidate driving trajectory according to the determined collision cost between each obstacle and the vehicle.
8. A vehicle control apparatus, characterized by comprising:
the special scene determining module is used for responding to the acquired positioning data of the vehicle, the acquired perception data of the vehicle and the map data and determining whether the vehicle is in a special scene;
the target track planning module is used for planning a target running track of the vehicle passing through the special scene based on the perception data of the vehicle if the vehicle is in the special scene;
the target trajectory planning module includes:
the attribute information determining unit is used for determining the attribute information of the obstacle according to the perception data of the vehicle;
the candidate track planning unit is used for planning candidate running tracks of the vehicle passing through the special scene;
a target track selection unit, configured to select a target travel track of the vehicle through the special scene from the candidate travel tracks according to the obstacle attribute information;
the target trajectory selection unit is specifically configured to:
determining obstacle species coefficient and transverse distance between an obstacle and the vehicle according to the obstacle attribute information;
determining a cost value of each candidate driving track based on a cost function according to the obstacle species coefficient and the transverse distance between the obstacle and the vehicle; wherein the cost function is used to characterize a cost of collision between the vehicle and the obstacle, the cost of collision being proportional to the obstacle class coefficient and inversely proportional to a lateral distance between the obstacle and the vehicle;
and determining a target running track of the vehicle passing through the special scene according to the cost value of each candidate running track.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the vehicle control method of any one of claims 1-7.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the vehicle control method according to any one of claims 1 to 7.
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| KR102408743B1 (en) * | 2017-07-27 | 2022-06-14 | 주식회사 에이치엘클레무브 | Method and system for determining whether a vehicle can enter a road |
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