CN111879330A - Method and device for planning crossing driving path, processor and automatic driving vehicle - Google Patents
Method and device for planning crossing driving path, processor and automatic driving vehicle Download PDFInfo
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Abstract
The application provides a method, a device, a processor and an automatic driving vehicle for planning a crossing driving path, wherein the method comprises the following steps: acquiring a first predicted track and a second predicted track, wherein the first predicted track is an intersection predicted track of the obstacle vehicle, and the second predicted track is an intersection predicted track of the automatic driving vehicle; determining a collision area according to the first predicted track and the second predicted track; an intersection travel path of the autonomous vehicle is determined from the collision zone. The method can plan a crossing driving path, so that the automatic driving vehicle passes through the collision area later than the obstacle vehicle, and can plan another crossing driving path, so that the automatic driving vehicle passes through the collision area earlier than the obstacle vehicle.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a method and a device for planning a crossing driving path, a computer readable storage medium, a processor and an automatic driving vehicle.
Background
With the development of autopilot, one of the difficulties recognized in the industry as autopilot left turns is part of the planning of autopilot in intersections. How to reasonably predict and plan a reasonable path at an intersection by an automatic driving vehicle is not only established on the basis of traffic rules, but also needs to be combined with the characteristics of human driving and semantic information of the intersection. The current behavior planning of the automatic driving vehicle at the intersection is generally conservative, and the improvement of the intersection passing efficiency is not facilitated. With the development of vehicle intelligence and vehicle networks (V2X), how to make intersections have more reasonable traffic efficiency becomes a hot spot in the research field of urban traffic. The reasonable passing of the intersection is an important part, and the existing automatic driving technology is fused, so that the technology that the unmanned vehicle intersection intelligent dispatching system of V2X completely replaces traffic lights is completely possible to realize. But not negligibly, V2X cannot currently achieve one-hundred percent accuracy, and false detection, network delay and the like all have a series of impacts on the decision of automatic driving. The fusion of the sensing result of the autonomous vehicle and the V2X information is also a big difficulty. It is difficult for us to select the trusting V2X or the result perceived by the host vehicle at the right moment.
In the prior art, Bayesian reasoning or deep learning is mostly adopted for the intention prediction in automatic driving prediction, and an opportunistic lane sequence generation mode is adopted for the trajectory prediction. However, these prediction methods also have certain disadvantages: 1) the probability in the intention prediction cannot be used in the current planning, and because the automatic driving is based on the safety premise, even if the probability is very small, the automatic driving can be used as a normal predicted track for planning; 2) the intention prediction cannot cover each scene according to the prediction of deep learning, and is more inaccurate particularly when the shape of the intersection changes greatly. 3) Prediction needs to be combined with not only semantic maps, but also traffic rules and other information.
The above information disclosed in this background section is only for enhancement of understanding of the background of the technology described herein and, therefore, certain information may be included in the background that does not form the prior art that is already known in this country to a person of ordinary skill in the art.
Disclosure of Invention
The main purpose of the present application is to provide a method and an apparatus for planning a driving path at an intersection, a computer-readable storage medium, a processor, and an autonomous vehicle, so as to solve the problem that the method for planning a driving path at an intersection is inflexible in the prior art.
According to an aspect of the embodiments of the present invention, there is provided a method for planning a driving path at an intersection, including: acquiring a first predicted track and a second predicted track, wherein the first predicted track is an intersection predicted track of an obstacle vehicle, and the second predicted track is an intersection predicted track of an automatic driving vehicle; determining a collision region according to the first predicted trajectory and the second predicted trajectory; and determining an intersection driving path of the automatic driving vehicle according to the collision area.
Optionally, determining a collision zone according to the first predicted trajectory and the second predicted trajectory comprises: determining a predetermined collision zone from the first predicted trajectory and the second predicted trajectory; and expanding the preset collision area in an equal proportion according to the safe distance to obtain the collision area.
Optionally, after determining the intersection travel path of the autonomous vehicle according to the collision zone, the planning method further comprises: obtaining a type of a first vehicle and a type of a second vehicle, the first vehicle being the obstacle vehicle, the second vehicle being the autonomous vehicle, the types including an emergency vehicle and a non-emergency vehicle; determining the priority of a first vehicle according to the type of the first vehicle and the first predicted track, and determining the priority of a second vehicle according to the type of the second vehicle and the second predicted track; determining an optimal intersection travel path for the autonomous vehicle based on the priority of the first vehicle and the priority of the second vehicle.
Optionally, determining the priority of the first vehicle according to the first vehicle type and the first predicted trajectory comprises: determining that the priority of the first vehicle is a first priority in case that the first vehicle type is an emergency vehicle; determining that the priority of the first vehicle is a second priority if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a straight trajectory; determining that the priority of the first vehicle is a third priority if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a right turn trajectory; determining that the priority of the first vehicle is a fourth priority if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a left turn trajectory; determining that the priority of the first vehicle is a fifth priority if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a u-turn trajectory.
Optionally, determining the priority of the second vehicle according to the second vehicle type and the second predicted trajectory comprises: determining that the priority of the second vehicle is a first priority in case that the second vehicle type is an emergency vehicle; determining that the priority of the second vehicle is a second priority in the case that the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a straight trajectory; determining that the priority of the second vehicle is a third priority in the case that the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a right turn trajectory; determining that the priority of the second vehicle is a fourth priority if the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a left turn trajectory; determining that the priority of the second vehicle is a fifth priority, if the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a u-turn trajectory.
Optionally, determining an optimal intersection travel path for the autonomous vehicle according to the first priority and the second priority, including; determining a first intersection travel path as the optimal intersection travel path if the first priority is higher than the second priority, the first intersection travel path being a path that causes the autonomous vehicle to pass through the collision zone later than the obstacle vehicle; and under the condition that the first priority is lower than the second priority, determining that a second intersection driving path is the optimal intersection driving path, wherein the second intersection driving path is a path which enables the automatic driving vehicle to pass through the collision area earlier than the obstacle vehicle.
According to another aspect of the embodiments of the present invention, there is also provided a device for planning a driving path at an intersection, including: an acquisition unit configured to acquire a first predicted trajectory and a second predicted trajectory, the first predicted trajectory being an intersection predicted trajectory of an obstacle vehicle, the second predicted trajectory being an intersection predicted trajectory of an autonomous vehicle; a first determination unit configured to determine a collision region from the first predicted trajectory and the second predicted trajectory; and the second determination unit is used for determining the crossing driving path of the automatic driving vehicle according to the collision area.
According to still another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium including a stored program, wherein the program executes any one of the methods for planning a driving path at an intersection.
According to another aspect of the embodiment of the present invention, there is further provided a processor, where the processor is configured to execute a program, where the program executes any one of the methods for planning a driving path at an intersection when running.
According to another aspect of the embodiments of the present invention, there is also provided an autonomous vehicle, including a device for planning a crossing driving path, where the device for planning a crossing driving path is configured to execute any one of the methods for planning a crossing driving path.
In the embodiment of the present invention, in the method for planning a crossing travel path, first, a first predicted trajectory and a second predicted trajectory are obtained, where the first predicted trajectory is a crossing predicted trajectory of an obstacle vehicle, and the second predicted trajectory is a crossing predicted trajectory of an autonomous vehicle, then, a collision area is determined according to the first predicted trajectory and the second predicted trajectory, and finally, a crossing travel path of the autonomous vehicle is determined according to the collision area. The method can plan a crossing driving path, so that the automatic driving vehicle passes through the collision area later than the obstacle vehicle, and can plan another crossing driving path, so that the automatic driving vehicle passes through the collision area earlier than the obstacle vehicle.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 shows a flow chart of a method of planning a crossing travel path according to an embodiment of the present application;
FIG. 2 shows a schematic view of a collision zone according to an embodiment of the present application;
FIG. 3 shows a schematic view of an expanded impact region according to an embodiment of the present application;
FIG. 4 illustrates a schematic diagram of a first predicted trajectory according to an embodiment of the present application;
FIG. 5 illustrates a schematic diagram of a second predicted trajectory according to an embodiment of the present application;
FIG. 6 illustrates a schematic view of a first intersection travel path according to an embodiment of the present application;
FIG. 7 illustrates a schematic view of a second intersection travel path according to an embodiment of the present application; and
fig. 8 shows a schematic diagram of a device for planning a crossing travel path according to an embodiment of the present application.
Wherein the figures include the following reference numerals:
1. a first trajectory; 2. a second trajectory; 3. a third trajectory; 4. a fourth trajectory; 5. a fifth trajectory; 6. a sixth trajectory; 7. a seventh trajectory; 8. an eighth track; 01. a first intersection driving path; 02. a second intersection driving path; 10. a predetermined impact region; 20. a collision zone; 30. an obstacle vehicle; 40. an autonomous vehicle.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It will be understood that when an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements may also be present. Also, in the specification and claims, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element or "connected" to the other element through a third element.
As mentioned in the background of the invention, the prior art methods for planning a driving path at an intersection are not flexible, and in order to solve the above problems, in an exemplary embodiment of the present application, a method, an apparatus, a computer-readable storage medium, a processor and an autonomous vehicle for planning a driving path at an intersection are provided.
According to the embodiment of the application, a method for planning a driving path at an intersection is provided.
Fig. 1 is a flowchart of a method for planning an intersection travel path according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
step S101, a first prediction track and a second prediction track are obtained, wherein the first prediction track is an intersection prediction track of an obstacle vehicle, and the second prediction track is an intersection prediction track of an automatic driving vehicle, wherein the obstacle vehicle is a vehicle which is possibly collided with the automatic driving vehicle at an intersection;
step S102, determining a collision area according to the first predicted track and the second predicted track;
and step S103, determining a crossing driving path of the automatic driving vehicle according to the collision area.
The method for planning the crossing driving path comprises the steps of firstly obtaining a first prediction track and a second prediction track, wherein the first prediction track is a crossing prediction track of an obstacle vehicle, the second prediction track is a crossing prediction track of an automatic driving vehicle, then determining a collision area according to the first prediction track and the second prediction track, and finally determining the crossing driving path of the automatic driving vehicle according to the collision area. The method can plan a crossing driving path, so that the automatic driving vehicle passes through the collision area later than the obstacle vehicle, and can plan another crossing driving path, so that the automatic driving vehicle passes through the collision area earlier than the obstacle vehicle.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
In a specific embodiment of the application, the driving tracks of the obstacle vehicle and the automatic driving vehicle are predicted based on the intention prediction and the track prediction of the traffic rule and the semantic map, so that the first predicted track and the second predicted track are obtained, the defects of network delay and the like of track prediction by adopting a vehicle network V2X are overcome, and the difficulty of fusion of a vehicle perception result and V2X information is avoided.
In an embodiment of the present application, determining a collision region according to the first predicted trajectory and the second predicted trajectory includes: determining a predetermined collision zone 10 based on said first predicted trajectory and said second predicted trajectory; the predetermined impact region is enlarged in equal proportion to the safe distance d to obtain the impact region 20, as shown in fig. 2. Specifically, a predetermined collision area is determined based on the first predicted trajectory and the second predicted trajectory, that is, the first predicted trajectory is projected onto the second predicted trajectory, and a collision point where the first predicted trajectory and the second predicted trajectory are obtained constitutes a predetermined collision area 10, and then the predetermined collision area is expanded in equal proportion based on the safe distance d to obtain the collision area 20.
In an embodiment of the present application, determining a collision region according to the first predicted trajectory and the second predicted trajectory further includes: expanding the collision zone in a first direction when the length of the obstacle vehicle is greater than a first predetermined value, the first direction being the length direction of the obstacle vehicle when passing through the collision zone along a first predicted trajectory; in the case where the width of the obstacle vehicle is larger than the first predetermined value, the collision region is expanded in a second direction, which is the width direction of the obstacle vehicle when passing through the collision region along the first predicted trajectory. For example, as shown in FIG. 3, the impact region 20 may extend in a first direction or in a second direction.
In an actual automatic driving process, the automatic driving vehicle may advance or yield the obstacle vehicle so that a driving path of the automatic driving vehicle does not pass through the collision area, but according to a traffic rule, when the automatic driving vehicle passes through an intersection, particularly an intersection without a traffic light, it is required to determine whether to allow the obstacle vehicle, and in an embodiment of the present application, after determining a driving path of the intersection of the automatic driving vehicle according to the collision area, the planning method further includes: acquiring the type of a first vehicle and the type of a second vehicle, wherein the first vehicle is the obstacle vehicle, the second vehicle is the automatic driving vehicle, and the types comprise an emergency vehicle and a non-emergency vehicle; determining a priority of a first vehicle according to a type of the first vehicle and the first predicted trajectory, and determining a priority of a second vehicle according to a type of the second vehicle and the second predicted trajectory; and determining the optimal crossing driving path of the automatic driving vehicle according to the priority of the first vehicle and the priority of the second vehicle. Specifically, based on traffic rules such as yielding emergency vehicles and turning straight-going yielding, the priority of the first vehicle can be determined according to the type of the first vehicle and the first predicted track, the priority of the second vehicle can be determined according to the type of the second vehicle and the second predicted track, then the priority of the first vehicle is compared with the priority of the second vehicle, the priority of the first vehicle is higher than or equal to the priority of the second vehicle, the obstacle vehicle is yielded, and the priority of the first vehicle is lower than the priority of the second vehicle, so that the obstacle vehicle is advanced, and the flexibility of automatic driving vehicle selection is improved.
In one embodiment of the present application, determining a priority of the first vehicle based on the first vehicle type and the first predicted trajectory includes: determining the priority of the first vehicle as a first priority when the first vehicle type is an emergency vehicle; determining the priority of the first vehicle as a second priority when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a straight trajectory; determining the priority of the first vehicle as a third priority in case that the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a right turn trajectory; determining the priority of the first vehicle as a fourth priority when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a left turn trajectory; and determining the priority of the first vehicle as a fifth priority when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a u-turn trajectory. Specifically, as shown in fig. 4, when the obstacle vehicle 30 and the automatically driven vehicle 40 meet at the intersection, if the first vehicle type is an emergency vehicle, that is, the obstacle vehicle 30 is an emergency vehicle, the priority of the first vehicle is the first priority, that is, the priority of the obstacle vehicle 30 is the first priority, if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a straight trajectory, that is, the obstacle vehicle 30 is a non-emergency vehicle and the first predicted trajectory is the first trajectory 1, the priority of the first vehicle is the second priority, that is, the priority of the obstacle vehicle 30 is the second priority, if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a right trajectory, that is, the obstacle vehicle 30 is a non-emergency vehicle and the first predicted trajectory is the second trajectory 2, the priority of the first vehicle is the third priority, that is, the priority of the obstacle vehicle 30 is the third priority, the priority of the first vehicle is the fourth priority, that is, the priority of the obstacle vehicle 30 is the fourth priority, when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is the left-hand trajectory, that is, the obstacle vehicle 30 is a non-emergency vehicle and the first predicted trajectory is the third trajectory 3, and the priority of the first vehicle is the fifth priority, that is, the priority of the obstacle vehicle 30 is the fifth priority, when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is the u-turn trajectory, that is, the obstacle vehicle 30 is a non-emergency vehicle and the first predicted trajectory is the fourth trajectory 4.
In one embodiment of the present application, determining the priority of the second vehicle according to the second vehicle type and the second predicted trajectory includes: determining the priority of the second vehicle as a first priority when the second vehicle type is an emergency vehicle; determining the priority of the second vehicle as a second priority when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a straight trajectory; determining that the priority of the second vehicle is a third priority in a case where the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a right-turn trajectory; determining the priority of the second vehicle as a fourth priority when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a left-turn trajectory; and determining that the priority of the second vehicle is a fifth priority when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a u-turn trajectory. Specifically, as shown in fig. 5, when the obstacle vehicle 30 and the autonomous vehicle 40 meet at the intersection, if the second vehicle type is an emergency vehicle, that is, the autonomous vehicle 40 is an emergency vehicle, the priority of the second vehicle is the first priority, that is, the priority of the autonomous vehicle 40 is the first priority, if the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a straight trajectory, that is, the autonomous vehicle 40 is a non-emergency vehicle and the second predicted trajectory is the fifth trajectory 5, the priority of the second vehicle is the second priority, that is, the priority of the autonomous vehicle 40 is the second priority, if the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a right trajectory, that is, the autonomous vehicle 40 is a non-emergency vehicle and the second predicted trajectory is the sixth trajectory 6, the priority of the second vehicle is the third priority, that is, the priority of the autonomous vehicle 40 is the third priority, the priority of the second vehicle is the fourth priority, that is, the priority of the autonomous vehicle 40 is the fourth priority, when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is the left trajectory, that is, the autonomous vehicle 40 is a non-emergency vehicle and the second predicted trajectory is the seventh trajectory 7, and the priority of the second vehicle is the fifth priority, that is, the priority of the autonomous vehicle 40 is the fourth priority, when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is the left trajectory, that is, the autonomous vehicle 40 is a non-emergency vehicle and the second predicted trajectory is the eighth trajectory 8.
In one embodiment of the present application, determining an optimal intersection travel path for the autonomous vehicle based on the first priority and the second priority comprises; determining a first intersection travel path as the optimal intersection travel path when the first priority is higher than the second priority, the first intersection travel path being a path through which the autonomous vehicle passes the collision zone later than the obstacle vehicle; when the first priority is lower than the second priority, a second intersection travel path is determined as the optimum intersection travel path, the second intersection travel path being a path through which the autonomous vehicle passes the collision area earlier than the obstacle vehicle. Specifically, as shown in fig. 6, in the case where the priority of the first vehicle is higher than or equal to the priority of the second vehicle, the shadow area is blocked so that the travel path of the autonomous vehicle does not pass through the shadow area, i.e., the autonomous vehicle gives way to the obstacle vehicle, for example, the first intersection travel path 01 may be selected as the optimal intersection travel path of the autonomous vehicle, as shown in fig. 7, in the case where the priority of the first vehicle is lower than the priority of the second vehicle, the shadow area is blocked so that the travel path of the autonomous vehicle does not pass through the shadow area, i.e., the autonomous vehicle precedes the obstacle vehicle, for example, the second intersection travel path 02 may be selected as the optimal intersection travel path of the autonomous vehicle.
The embodiment of the present application further provides a device for planning a driving path at an intersection, and it should be noted that the device for planning a driving path at an intersection according to the embodiment of the present application can be used to execute the method for planning a driving path at an intersection according to the embodiment of the present application. The following describes a device for planning a crossing travel path according to an embodiment of the present application.
Fig. 8 is a schematic diagram of a device for planning a crossing travel path according to an embodiment of the present application. As shown in fig. 8, the apparatus includes:
an acquisition unit 100 configured to acquire a first predicted trajectory that is an intersection predicted trajectory of an obstacle vehicle and a second predicted trajectory that is an intersection predicted trajectory of an autonomous vehicle;
a first determination unit 200 configured to determine a collision region based on the first predicted trajectory and the second predicted trajectory;
a second determining unit 300 for determining the crossing driving path of the autonomous vehicle according to the collision region.
In the device for planning the crossing travel path, the acquisition unit acquires a first predicted track and a second predicted track, the first predicted track is a crossing predicted track of the obstacle vehicle, the second predicted track is a crossing predicted track of the automatic driving vehicle, the first determination unit determines a collision area according to the first predicted track and the second predicted track, and the second determination unit determines the crossing travel path of the automatic driving vehicle according to the collision area. The device can plan a crossing driving path, so that the automatic driving vehicle passes through the collision area later than the obstacle vehicle, and can plan another crossing driving path, so that the automatic driving vehicle passes through the collision area earlier than the obstacle vehicle.
In a specific embodiment of the application, the driving tracks of the obstacle vehicle and the automatic driving vehicle are predicted based on the intention prediction and the track prediction of the traffic rule and the semantic map, so that the first predicted track and the second predicted track are obtained, the defects of network delay and the like of track prediction by adopting a vehicle network V2X are overcome, and the difficulty of fusion of a vehicle perception result and V2X information is avoided.
In an embodiment of the present application, the first determining unit includes a first determining module and a first processing module, wherein the first determining module is configured to determine the predetermined collision region 10 according to the first predicted trajectory and the second predicted trajectory; the first processing module is configured to expand the predetermined collision region proportionally according to the safety distance d to obtain the collision region 20, as shown in fig. 2. Specifically, a predetermined collision area is determined based on the first predicted trajectory and the second predicted trajectory, that is, the first predicted trajectory is projected onto the second predicted trajectory, and a collision point where the first predicted trajectory and the second predicted trajectory are obtained constitutes a predetermined collision area 10, and then the predetermined collision area is expanded in equal proportion based on the safe distance d to obtain the collision area 20.
In an embodiment of the present application, the first determining unit further includes a second processing module and a third processing module, wherein the second processing module is configured to expand the collision zone in a first direction when the length of the obstacle vehicle is greater than a first predetermined value, the first direction being a length direction of the obstacle vehicle when passing through the collision zone along a first predicted trajectory; the third processing module is configured to expand the collision zone in a second direction when the width of the obstacle vehicle is greater than the first predetermined value, the second direction being a width direction of the obstacle vehicle when passing through the collision zone along the first predicted trajectory. For example, as shown in FIG. 3, the impact region 20 may extend in a first direction or in a second direction.
In an actual automatic driving process, the automatic driving vehicle may advance or yield the obstacle vehicle so that a running path of the automatic driving vehicle does not pass through the collision area, but according to a traffic rule, when the automatic driving vehicle passes through an intersection, particularly an intersection without a traffic light, whether the obstacle vehicle is allowed to run or not needs to be determined, in one embodiment of the present application, after the crossing running path of the automatic driving vehicle is determined according to the collision area, the apparatus further includes a processing unit, the processing unit includes an acquisition module, a second determination module and a third determination module, wherein the acquisition module is used for acquiring a type of a first vehicle and a type of a second vehicle, the first vehicle is the obstacle vehicle, the second vehicle is the automatic driving vehicle, the above types include emergency vehicles and non-emergency vehicles; the second determining module is used for determining the priority of the first vehicle according to the type of the first vehicle and the first predicted track and determining the priority of the second vehicle according to the type of the second vehicle and the second predicted track; the third determining module is used for determining the optimal crossing driving path of the automatic driving vehicle according to the priority of the first vehicle and the priority of the second vehicle. Specifically, based on traffic rules such as yielding emergency vehicles and turning straight-going yielding, the priority of the first vehicle can be determined according to the type of the first vehicle and the first predicted track, the priority of the second vehicle can be determined according to the type of the second vehicle and the second predicted track, then the priority of the first vehicle is compared with the priority of the second vehicle, the priority of the first vehicle is higher than or equal to the priority of the second vehicle, the obstacle vehicle is yielded, and the priority of the first vehicle is lower than the priority of the second vehicle, so that the obstacle vehicle is advanced, and the flexibility of automatic driving vehicle selection is improved.
In an embodiment of the application, the second determining module includes a first determining sub-module, a second determining sub-module, a third determining sub-module, a fourth determining sub-module, and a fifth determining sub-module, wherein the first determining sub-module is configured to determine that the priority of the first vehicle is the first priority when the first vehicle type is an emergency vehicle; the second determining submodule is configured to determine that the priority of the first vehicle is a second priority when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a straight trajectory; the third determining submodule is configured to determine that the priority of the first vehicle is a third priority when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a right-turn trajectory; the fourth determining submodule is configured to determine that the priority of the first vehicle is a fourth priority when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a left-turn trajectory; the fifth determining submodule is configured to determine that the priority of the first vehicle is a fifth priority when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a u-turn trajectory. Specifically, as shown in fig. 4, when the obstacle vehicle 30 and the automatically driven vehicle 40 meet at the intersection, if the first vehicle type is an emergency vehicle, that is, the obstacle vehicle 30 is an emergency vehicle, the priority of the first vehicle is the first priority, that is, the priority of the obstacle vehicle 30 is the first priority, if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a straight trajectory, that is, the obstacle vehicle 30 is a non-emergency vehicle and the first predicted trajectory is the first trajectory 1, the priority of the first vehicle is the second priority, that is, the priority of the obstacle vehicle 30 is the second priority, if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a right trajectory, that is, the obstacle vehicle 30 is a non-emergency vehicle and the first predicted trajectory is the second trajectory 2, the priority of the first vehicle is the third priority, that is, the priority of the obstacle vehicle 30 is the third priority, the priority of the first vehicle is the fourth priority, that is, the priority of the obstacle vehicle 30 is the fourth priority, when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is the left-hand trajectory, that is, the obstacle vehicle 30 is a non-emergency vehicle and the first predicted trajectory is the third trajectory 3, and the priority of the first vehicle is the fifth priority, that is, the priority of the obstacle vehicle 30 is the fifth priority, when the first vehicle type is a non-emergency vehicle and the first predicted trajectory is the u-turn trajectory, that is, the obstacle vehicle 30 is a non-emergency vehicle and the first predicted trajectory is the fourth trajectory 4.
In an embodiment of the application, the second determining module includes a sixth determining submodule, a seventh determining submodule, an eighth determining submodule, a ninth determining submodule, and a ninth determining submodule, wherein the sixth determining submodule is configured to determine the priority of the first vehicle as the first priority when the second vehicle type is an emergency vehicle; the seventh determining submodule is configured to determine that the priority of the first vehicle is the second priority, when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a straight trajectory; the eighth determining submodule is configured to determine that the priority of the first vehicle is a third priority, when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a right-turn trajectory; the ninth determining submodule is configured to determine that the priority of the first vehicle is a fourth priority when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a left-turn trajectory; the tenth determining submodule determines that the priority of the second vehicle is a fifth priority when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a u-turn trajectory. Specifically, as shown in fig. 5, when the obstacle vehicle 30 and the autonomous vehicle 40 meet at the intersection, if the second vehicle type is an emergency vehicle, that is, the autonomous vehicle 40 is an emergency vehicle, the priority of the second vehicle is the first priority, that is, the priority of the autonomous vehicle 40 is the first priority, if the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a straight trajectory, that is, the autonomous vehicle 40 is a non-emergency vehicle and the second predicted trajectory is the fifth trajectory 5, the priority of the second vehicle is the second priority, that is, the priority of the autonomous vehicle 40 is the second priority, if the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a right trajectory, that is, the autonomous vehicle 40 is a non-emergency vehicle and the second predicted trajectory is the sixth trajectory 6, the priority of the second vehicle is the third priority, that is, the priority of the autonomous vehicle 40 is the third priority, the priority of the second vehicle is the fourth priority, that is, the priority of the autonomous vehicle 40 is the fourth priority, when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is the left trajectory, that is, the autonomous vehicle 40 is a non-emergency vehicle and the second predicted trajectory is the seventh trajectory 7, and the priority of the second vehicle is the fifth priority, that is, the priority of the autonomous vehicle 40 is the fourth priority, when the second vehicle type is a non-emergency vehicle and the second predicted trajectory is the left trajectory, that is, the autonomous vehicle 40 is a non-emergency vehicle and the second predicted trajectory is the eighth trajectory 8.
In one embodiment of the present application, the second determination unit includes a fourth determination module and a fifth determination module, wherein the fourth determination module is configured to determine, when the first priority is higher than the second priority, a first intersection travel path as the optimal intersection travel path, the first intersection travel path being a path along which the autonomous vehicle passes through the collision zone later than the obstacle vehicle; the fifth determining module is configured to determine a second intersection travel path as the optimal intersection travel path when the first priority is lower than the second priority, the second intersection travel path being a path through which the autonomous vehicle passes the collision area earlier than the obstacle vehicle. Specifically, as shown in fig. 6, in the case where the priority of the first vehicle is higher than or equal to the priority of the second vehicle, the shadow area is blocked so that the travel path of the autonomous vehicle does not pass through the shadow area, i.e., the autonomous vehicle gives way to the obstacle vehicle, for example, the first intersection travel path 01 may be selected as the optimal intersection travel path of the autonomous vehicle, as shown in fig. 7, in the case where the priority of the first vehicle is lower than the priority of the second vehicle, the shadow area is blocked so that the travel path of the autonomous vehicle does not pass through the shadow area, i.e., the autonomous vehicle precedes the obstacle vehicle, for example, the second intersection travel path 02 may be selected as the optimal intersection travel path of the autonomous vehicle.
The embodiment of the application also provides an automatic driving vehicle which comprises a planning device of the crossing driving path, wherein the planning device of the crossing driving path is used for executing any one of the planning methods of the crossing driving path.
The automatic driving vehicle comprises a planning device of a crossing driving path, an acquisition unit acquires a first prediction track and a second prediction track, the first prediction track is a crossing prediction track of the obstacle vehicle, the second prediction track is a crossing prediction track of the automatic driving vehicle, a first determination unit determines a collision area according to the first prediction track and the second prediction track, and a second determination unit determines the crossing driving path of the automatic driving vehicle according to the collision area. The device can plan a crossing driving path, so that the automatic driving vehicle passes through the collision area later than the obstacle vehicle, and can plan another crossing driving path, so that the automatic driving vehicle passes through the collision area earlier than the obstacle vehicle.
The device for planning the crossing driving path comprises a processor and a memory, wherein the acquisition unit, the first determination unit, the second determination unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the problem that a planning method of a crossing driving path in the prior art is inflexible is solved by adjusting kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The embodiment of the invention provides a computer readable storage medium, wherein a program is stored on the computer readable storage medium, and the program is executed by a processor to realize the method for planning the driving path of the intersection.
The embodiment of the invention provides a processor, which is used for running a program, wherein the program executes a planning method of a driving path of an intersection when running.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein when the processor executes the program, at least the following steps are realized:
step S101, a first prediction track and a second prediction track are obtained, wherein the first prediction track is an intersection prediction track of an obstacle vehicle, and the second prediction track is an intersection prediction track of an automatic driving vehicle;
step S102, determining a collision area according to the first predicted track and the second predicted track;
and step S103, determining a crossing driving path of the automatic driving vehicle according to the collision area.
The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program of initializing at least the following method steps when executed on a data processing device:
step S101, a first prediction track and a second prediction track are obtained, wherein the first prediction track is an intersection prediction track of an obstacle vehicle, and the second prediction track is an intersection prediction track of an automatic driving vehicle;
step S102, determining a collision area according to the first predicted track and the second predicted track;
and step S103, determining a crossing driving path of the automatic driving vehicle according to the collision area.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a computer-readable storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage media comprise: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
From the above description, it can be seen that the above-described embodiments of the present application achieve the following technical effects:
1) the method for planning the crossing driving path comprises the steps of firstly obtaining a first prediction track and a second prediction track, wherein the first prediction track is a crossing prediction track of an obstacle vehicle, the second prediction track is a crossing prediction track of an automatic driving vehicle, then determining a collision area according to the first prediction track and the second prediction track, and finally determining the crossing driving path of the automatic driving vehicle according to the collision area. The method can plan a crossing driving path, so that the automatic driving vehicle passes through the collision area later than the obstacle vehicle, and can plan another crossing driving path, so that the automatic driving vehicle passes through the collision area earlier than the obstacle vehicle.
2) In the device for planning the crossing traveling path, an acquisition unit acquires a first predicted track and a second predicted track, the first predicted track is a crossing predicted track of an obstacle vehicle, the second predicted track is a crossing predicted track of an automatic driving vehicle, a first determination unit determines a collision area according to the first predicted track and the second predicted track, and a second determination unit determines the crossing traveling path of the automatic driving vehicle according to the collision area. The device can plan a crossing driving path, so that the automatic driving vehicle passes through the collision area later than the obstacle vehicle, and can plan another crossing driving path, so that the automatic driving vehicle passes through the collision area earlier than the obstacle vehicle.
3) The automatic driving vehicle comprises a planning device of a crossing driving path, an obtaining unit obtains a first prediction track and a second prediction track, the first prediction track is a crossing prediction track of an obstacle vehicle, the second prediction track is a crossing prediction track of the automatic driving vehicle, a first determining unit determines a collision area according to the first prediction track and the second prediction track, and a second determining unit determines the crossing driving path of the automatic driving vehicle according to the collision area. The device can plan a crossing driving path, so that the automatic driving vehicle passes through the collision area later than the obstacle vehicle, and can plan another crossing driving path, so that the automatic driving vehicle passes through the collision area earlier than the obstacle vehicle.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like 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 method for planning a driving path at an intersection is characterized by comprising the following steps:
acquiring a first predicted track and a second predicted track, wherein the first predicted track is an intersection predicted track of an obstacle vehicle, and the second predicted track is an intersection predicted track of an automatic driving vehicle;
determining a collision region according to the first predicted trajectory and the second predicted trajectory;
and determining an intersection driving path of the automatic driving vehicle according to the collision area.
2. The method of claim 1, wherein determining a collision zone from the first predicted trajectory and the second predicted trajectory comprises:
determining a predetermined collision zone from the first predicted trajectory and the second predicted trajectory;
and expanding the preset collision area in an equal proportion according to the safe distance to obtain the collision area.
3. The method of claim 1, wherein after determining an intersection travel path for the autonomous vehicle from the collision zone, the planning method further comprises:
obtaining a type of a first vehicle and a type of a second vehicle, the first vehicle being the obstacle vehicle, the second vehicle being the autonomous vehicle, the types including an emergency vehicle and a non-emergency vehicle;
determining the priority of a first vehicle according to the type of the first vehicle and the first predicted track, and determining the priority of a second vehicle according to the type of the second vehicle and the second predicted track;
determining an optimal intersection travel path for the autonomous vehicle based on the priority of the first vehicle and the priority of the second vehicle.
4. The method of claim 3, wherein determining the priority of the first vehicle based on the first vehicle type and the first predicted trajectory comprises:
determining that the priority of the first vehicle is a first priority in case that the first vehicle type is an emergency vehicle;
determining that the priority of the first vehicle is a second priority if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a straight trajectory;
determining that the priority of the first vehicle is a third priority if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a right turn trajectory;
determining that the priority of the first vehicle is a fourth priority if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a left turn trajectory;
determining that the priority of the first vehicle is a fifth priority if the first vehicle type is a non-emergency vehicle and the first predicted trajectory is a u-turn trajectory.
5. The method of claim 3, wherein determining the priority of the second vehicle based on a second vehicle type and the second predicted trajectory comprises:
determining that the priority of the second vehicle is a first priority in case that the second vehicle type is an emergency vehicle;
determining that the priority of the second vehicle is a second priority in the case that the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a straight trajectory;
determining that the priority of the second vehicle is a third priority in the case that the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a right turn trajectory;
determining that the priority of the second vehicle is a fourth priority if the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a left turn trajectory;
determining that the priority of the second vehicle is a fifth priority, if the second vehicle type is a non-emergency vehicle and the second predicted trajectory is a u-turn trajectory.
6. The method of claim 3, wherein determining an optimal intersection travel path for the autonomous vehicle based on the first priority and the second priority comprises;
determining a first intersection travel path as the optimal intersection travel path if the first priority is higher than the second priority, the first intersection travel path being a path that causes the autonomous vehicle to pass through the collision zone later than the obstacle vehicle;
and under the condition that the first priority is lower than the second priority, determining that a second intersection driving path is the optimal intersection driving path, wherein the second intersection driving path is a path which enables the automatic driving vehicle to pass through the collision area earlier than the obstacle vehicle.
7. A device for planning a driving path at an intersection, comprising:
an acquisition unit configured to acquire a first predicted trajectory and a second predicted trajectory, the first predicted trajectory being an intersection predicted trajectory of an obstacle vehicle, the second predicted trajectory being an intersection predicted trajectory of an autonomous vehicle;
a first determination unit configured to determine a collision region from the first predicted trajectory and the second predicted trajectory;
and the second determination unit is used for determining the crossing driving path of the automatic driving vehicle according to the collision area.
8. A computer-readable storage medium characterized by comprising a stored program, wherein the program executes the method of planning an intersection travel path according to any one of claims 1 to 6.
9. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the method for planning a crossing travel path according to any one of claims 1 to 6 when running.
10. An autonomous vehicle comprising a device for planning a crossing travel path, characterized in that the device for planning a crossing travel path is configured to perform the method for planning a crossing travel path according to any one of claims 1 to 6.
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