CN110674723A - Method and device for determining driving track of unmanned vehicle - Google Patents
Method and device for determining driving track of unmanned vehicle Download PDFInfo
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Abstract
The specification discloses a method and a device for determining the driving track of an unmanned vehicle. And then, determining a running track for the current vehicle according to the matching degree of each candidate track of the current vehicle at the current moment and each track cluster. By the method in the specification, the track cluster formed by the regular behaviors of other vehicles can be used as a basis for determining the running track of the current vehicle, so that the current vehicle can bypass obstacles and road conditions which cannot be detected by the sensing device, and the running safety is guaranteed.
Description
Technical Field
The specification relates to the technical field of unmanned driving, in particular to a method and a device for determining a driving track of an unmanned vehicle.
Background
At present, vehicle-assisted driving and unmanned vehicles (which can be collectively referred to as unmanned vehicles) are increasingly prominent in social production and life as important components of artificial intelligence technology, and become one of the main directions for guiding the development of traffic technology.
In the prior art, the perception of vehicles with driving assistance function and unmanned vehicles to the driving environment mostly depends on a sensing device, and the driving track is planned based on data collected by the sensing device. For example, the unmanned vehicle can acquire obstacles and road condition information in a driving environment through a radar and/or a camera, and determine a driving track according to the acquired obstacles and road condition information, so as to avoid the obstacles or change the driving direction.
Therefore, the perception of the vehicle driving track planning in the prior art for obstacles and road conditions depends too much on the detection capability of the sensing device. Obstacles and road conditions which cannot be detected by the sensing device bring hidden dangers to driving safety.
Disclosure of Invention
The embodiment of the specification provides a method and a device for determining a driving track of an unmanned vehicle, so as to partially solve the problems in the prior art.
The embodiment of the specification adopts the following technical scheme:
the method for determining the driving track of the unmanned vehicle comprises the following steps:
acquiring candidate tracks planned in advance by a current vehicle at the current moment, and acquiring running tracks of other vehicles;
clustering the driving tracks of other vehicles to obtain at least one track cluster, wherein each track cluster at least comprises the driving track of one other vehicle;
for each candidate track, determining the matching degree of the candidate track and each track cluster according to the running tracks of other vehicles contained in each track cluster;
and determining the running track of the current vehicle in each candidate track based on the matching degree of each candidate track and each track cluster.
Optionally, the acquiring the driving track of each other vehicle specifically includes:
determining each pre-divided acquisition area in the acquisition range of the current vehicle;
collecting the running track of each other vehicle falling into each collection area;
clustering the driving tracks of other vehicles to obtain at least one track cluster, which specifically comprises the following steps:
and clustering the running tracks of other vehicles falling into the acquisition area aiming at each acquisition area to obtain at least one track cluster in the acquisition area.
Optionally, determining a matching degree of the candidate trajectory and each trajectory cluster according to the travel trajectories of other vehicles included in each trajectory cluster specifically includes:
aiming at each track cluster, determining the central track of the track cluster according to the running tracks of other vehicles in the track cluster;
and determining the matching degree of the candidate track and the central track according to the central track of the track cluster, and taking the matching degree of the candidate track and the track cluster as the matching degree of the candidate track and the track cluster.
Optionally, determining a center trajectory of the trajectory cluster according to the travel trajectories of other vehicles in the trajectory cluster specifically includes:
determining the average track of the track cluster according to the running tracks of other vehicles in the track cluster, and taking the average track as the central track of the track cluster; or
According to the running tracks of other vehicles in the track cluster, determining the average track of the track cluster, determining the similarity between the running track of each other vehicle and the average track, and according to the similarity between the running track of each other vehicle and the average track, selecting the central track of the track cluster from the running tracks of other vehicles in the track cluster.
Optionally, determining the matching degree between the candidate trajectory and the center trajectory specifically includes:
determining the similarity between the candidate track and the central track;
and determining the matching degree of the candidate track and the central track according to the distance between the current vehicle and the acquisition area where the track cluster to which the central track belongs is located and the similarity of the candidate track and the central track.
Optionally, determining the driving trajectory of the current vehicle in each candidate trajectory based on the matching degree between each candidate trajectory and each trajectory cluster specifically includes:
and determining the running track of the current vehicle in each candidate track according to the preset constraint conditions and the matching degree of each candidate track with each track cluster.
Optionally, determining the driving trajectory of the current vehicle in each candidate trajectory based on the matching degree between each candidate trajectory and each trajectory cluster specifically includes:
determining the comprehensive matching degree of the candidate track according to the matching degree of each track cluster in each acquisition area of the candidate track passing through and the candidate track;
and determining the running track of the current vehicle in each candidate track according to the determined comprehensive matching degree of each candidate track.
The present specification provides an apparatus for determining a driving trajectory of an unmanned vehicle, comprising:
the acquisition module is used for acquiring each candidate track planned in advance by the current vehicle at the current moment and acquiring the running track of each other vehicle;
the clustering module is used for clustering the driving tracks of other vehicles to obtain at least one track cluster, wherein each track cluster at least comprises the driving track of one other vehicle;
the matching degree determining module is used for determining the matching degree of each candidate track and each track cluster according to the running tracks of other vehicles contained in each track cluster;
and the running track determining module is used for determining the running track of the current vehicle in each candidate track based on the matching degree of each candidate track and each track cluster.
The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described method of determining a driving trajectory of an unmanned vehicle.
The present specification provides an unmanned vehicle, which includes a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements the above method for determining the driving track of the unmanned vehicle when executing the program.
The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects:
according to the method and the device, in the process of determining the driving track of the unmanned vehicle, at least one track cluster is determined according to the acquired driving tracks of other vehicles. And then, determining a running track for the current vehicle according to the matching degree of each candidate track of the current vehicle at the current moment and each track cluster. By the method, track clusters formed by regular behaviors of other vehicles can be used as a basis for determining the current vehicle running track, and the degree of dependence of vehicles and/or unmanned vehicles with driving assistance functions on the detection capability of the sensing device is effectively reduced. When the obstacle and the road condition which cannot be detected by the sensing device exist in the driving environment of the vehicle and/or the unmanned vehicle with the driving assisting function, the driving track of the vehicle and/or the unmanned vehicle with the driving assisting function can be determined according to the track cluster formed by the regular behaviors of other vehicles, so that the vehicle and/or the unmanned vehicle with the driving assisting function can bypass the obstacle and the road condition which cannot be detected by the sensing device, and the driving safety is guaranteed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. In the drawings:
FIG. 1 is a process for determining a trajectory of an unmanned vehicle provided herein;
fig. 2 is a process provided in this specification for determining a matching degree of the candidate trajectory with each trajectory cluster according to the travel trajectories of other vehicles included in each trajectory cluster;
FIG. 3 is a schematic illustration of a determined acquisition region provided herein;
fig. 4 is a schematic structural diagram of an apparatus for determining a driving track of an unmanned vehicle according to an embodiment of the present disclosure;
fig. 5 is a schematic view of an unmanned vehicle corresponding to fig. 1 provided in an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Fig. 1 is a process for determining a driving trajectory of an unmanned vehicle according to an embodiment of the present disclosure, which may specifically include the following steps:
s100: and acquiring each candidate track planned in advance by the current vehicle at the current moment, and acquiring the running tracks of other vehicles.
In the method of the embodiment, the current vehicle may be: a vehicle and/or an unmanned vehicle having a driving assistance function. Other vehicles may include: and passing through at least part of the vehicle of the current vehicle collection range. For example, the other vehicle may be: and passing the vehicle in the acquisition range of the current vehicle within or before the planning period of the current driving track of the current vehicle.
S102: and clustering the driving tracks of other vehicles to obtain at least one track cluster, wherein each track cluster at least comprises the driving track of one other vehicle.
Clustering can be realized by k-means clustering (k-means), k-center point clustering (k-means) and other methods. When the other vehicle driving tracks are clustered, clustering can be performed according to the distance between the other vehicle driving tracks, and the track cluster obtained by clustering is equivalent to a set of other vehicle driving tracks formed by regular behaviors of the other vehicles. The track cluster may include one or several other vehicle driving tracks, and each other vehicle driving track in the track cluster may be used to express at least part of the information of the track cluster.
S104: and for each candidate track, determining the matching degree of the candidate track and each track cluster according to the running tracks of other vehicles contained in each track cluster.
For a candidate track and a track cluster, the matching degree of the candidate track and the running track of any other vehicle in the track cluster can be determined and used as the matching degree of the candidate track and the track cluster.
Specifically, the center trajectory of the trajectory cluster may be determined according to the traveling trajectories of other vehicles in the trajectory cluster, and the matching degree between the candidate trajectory and the center trajectory may be determined according to the center trajectory of the trajectory cluster, and is used as the matching degree between the candidate trajectory and the trajectory cluster.
When the center track of the track cluster is determined, the average track of the track cluster can be determined according to the traveling tracks of other vehicles in the track cluster, and the average track is used as the center track of the track cluster. For example, the driving tracks of other vehicles in the track cluster are sampled according to a certain sequence to obtain sampling points in the driving tracks of other vehicles, average coordinates are determined according to the coordinates of the sampling points sequenced in the driving tracks of other vehicles, and finally the determined average coordinates are sequentially connected according to the sequence to obtain the average track of the track cluster as the center track of the track cluster.
After the average track is determined, the similarity between the running track of each other vehicle in the track cluster and the average track is determined, and the center track of the track cluster is selected from the running tracks of each other vehicle in the track cluster according to the similarity between the running track of each other vehicle and the average track, for example, the running track of the other vehicle with the highest similarity is selected as the center track of the track cluster.
Since each track cluster obtained by clustering in step S102 represents different regular behaviors of other vehicles, the matching degree between one candidate track and each track cluster can represent which regular behavior the candidate track is more similar to, and when a running track of the current vehicle is selected from candidate tracks of the current vehicle, the selection is performed based on the matching degree between the candidate track and each track cluster, so that the dependence on a sensor mounted on the current vehicle can be removed to a certain extent.
S106: and determining the running track of the current vehicle in each candidate track based on the matching degree of each candidate track and each track cluster.
For each candidate track, the matching degree between the candidate track and each track cluster can be integrated to represent the candidate track as the basis of the adaptation degree of the current vehicle running track, so that the method in the embodiment of the specification can determine the feasibility of the candidate track as the running track of the current vehicle according to the road condition information expressed by one or more track clusters and the corresponding modes of other vehicles to the road condition information, and further the representation for the candidate track is more objective and comprehensive. The current vehicle running track determined on the basis is also more suitable for the current road condition and running environment.
In an optional embodiment of the present disclosure, in step S100, acquiring the driving track of each other vehicle may include: determining each pre-divided acquisition area in the acquisition range of the current vehicle; and collecting the running tracks of other vehicles falling into the collecting areas.
The acquisition range of the current vehicle may be the acquisition range of the sensing device. One or several acquisition regions may be included in the acquisition range of the current vehicle.
Each acquisition area can be determined in the acquisition range of the current vehicle according to a preset division rule. Optionally, the preset partitioning rule may include at least one of: the size rule that the collection range should satisfy, the adaptability rule of collection range and road conditions (for example, the collection range should not include infrastructure that street lamps, roadblocks and the like on the road affect the current vehicle to travel), the distance rule of collection range from the current vehicle (for example, the number of collection ranges divided in the range of ten meters from the current vehicle should not be less than eight, the number of collection ranges divided in the range of ten meters to twenty meters from the current vehicle should not be less than four), and the like.
In an alternative embodiment of the present description, the acquisition range of the current vehicle is divided into six acquisition regions, as shown in fig. 3. The other vehicle trajectory T1 is routed through acquisition regions a11, a21, and a31, and the other vehicle trajectory T2 is routed through acquisition regions a11, a12, a22, and a 32.
In an optional embodiment of the present specification, in step S102, clustering the driving tracks of each other vehicle to obtain at least one track cluster, may include: and clustering the running tracks of other vehicles falling into the acquisition area aiming at each acquisition area to obtain at least one track cluster in the acquisition area.
Because the lengths of the acquired travel tracks of the other vehicles are different, if the acquired travel track of one other vehicle is shorter (as shown in fig. 3, the length of the travel track T1 of the other vehicle is shorter than the length of the travel track T2 of the other vehicle), the travel track of the other vehicle has less data for comparison, and thus an error is more likely to be caused. According to the method, the running tracks of the longer other vehicles are divided into a plurality of different acquisition areas, the running tracks of the shorter other vehicles can be divided into at least one acquisition area, the clustering error caused by the difference of the running tracks of the other vehicles in the length direction can be reduced, and the running tracks of the shorter other vehicles can also play a corresponding role in determining the running track of the current vehicle.
Corresponding tags may be set for the travel tracks of each of the other vehicles. Wherein the tag may include at least one of identification information of the other vehicle (e.g., license plate number, etc.), type of vehicle (e.g., van, truck, etc.), type of vehicle (e.g., unmanned vehicle, human-driven vehicle, etc.), size of vehicle (e.g., width, height, etc. of vehicle). Whether the other vehicle can be an object of the cluster may be determined according to the tag of the other vehicle.
Specifically, clustering the driving tracks of the other vehicles to obtain at least one track cluster may include: the corresponding label can be set for the running track of each other vehicle, and according to the label, the running track of the non-unmanned vehicle is determined in the running track of each other vehicle, and the running track of the unmanned vehicle is eliminated. And then clustering the determined driving track of the non-unmanned vehicle to obtain at least one track cluster. Because the unmanned vehicle driving track planning depends on the sensing device to a greater extent, the driving track corresponding to the unmanned vehicle can be abandoned during clustering, the contribution degree of the regular behaviors shown by human driving of the vehicle in the process of determining the driving track of the current vehicle can be improved, and the beneficial effect of determining the driving track of the current vehicle is further improved.
Optionally, the travel track of the other vehicle may include, in addition to the track that the other vehicle has traveled: the predicted possible future travel trajectory of the other vehicle. The possible future driving tracks of other vehicles can be predicted according to the driving speed of the other vehicles, the road conditions of the driving environment of the other vehicles, the collected driving tracks of the other vehicles and the like.
In an alternative embodiment of the present disclosure, determining the matching degree between the candidate trajectory and the center trajectory, as shown in fig. 2, may include:
s200: and determining the similarity between the candidate track and the central track.
The similarity between the candidate trajectory and the center trajectory can be used to characterize the degree of coincidence between the candidate trajectory and the center trajectory in terms of position, orientation, curvature, and the like.
S202: and determining the matching degree of the candidate track and the central track according to the distance between the current vehicle and the acquisition area where the track cluster to which the central track belongs is located and the similarity of the candidate track and the central track.
In the embodiment of the present specification, the calculation manner of the similarity and the calculation manner of the matching degree may be the same, that is, in step S104 shown in fig. 1, the similarity between the candidate trajectory and the center trajectory of each trajectory cluster may be directly used as the matching degree between the candidate trajectory and each trajectory cluster.
Of course, the calculation methods of the similarity and the matching degree may be different. For example, for a candidate trajectory and a trajectory cluster, the similarity between the candidate trajectory and the center trajectory of the trajectory cluster may be multiplied by the weight of the acquisition region where the trajectory cluster is located, so as to determine the matching degree between the candidate trajectory and the trajectory cluster. Specifically, the weight may be set for each of the collection areas according to the distance between the collection area and the current vehicle, and the closer the distance, the higher the weight, and the farther the distance, the lower the weight. In this way, the weight of the similarity between the central track in the acquisition area closer to the current vehicle and the candidate track can be increased to obtain a larger matching degree; and the weight of the similarity between the central track in the acquisition area far away from the current vehicle and the candidate track is reduced to obtain a smaller matching degree, so that the influence of each central track in the acquisition area near the current vehicle on the determination of the running track of the current vehicle is increased.
Of course, in addition to the above method of multiplying the similarity between the candidate trajectory and the center trajectory of the trajectory cluster by a certain weight as the matching degree, different value ranges may be set for the similarity between each center trajectory in each collection area and the candidate trajectory according to the distance between each collection area and the current vehicle, that is, the similarity between the candidate trajectory and the center trajectory of the trajectory cluster is adjusted to the value range corresponding to the collection area where the trajectory cluster is located. For example, the value range of the similarity between each central track in the acquisition area closer to the current vehicle and the candidate track can be set to be fifty to eighty so as to obtain a larger matching degree; and the value range of the similarity between each central track in the acquisition area far away from the current vehicle and the candidate track is set to be between ten and twenty so as to obtain smaller matching degree.
In an optional embodiment of the present specification, in step S106, determining the driving trajectory of the current vehicle in the candidate trajectories based on the matching degree between each candidate trajectory and each trajectory cluster may include: determining the comprehensive matching degree of the candidate track according to the matching degree of each track cluster in each acquisition area of the candidate track passing through and the candidate track; and determining the running track of the current vehicle in each candidate track according to the determined comprehensive matching degree of each candidate track.
The comprehensive degree of matching can be used for characterizing: and matching the candidate track with all track clusters in all the acquisition areas passing by the candidate track on the whole. The comprehensive matching degree of the candidate track can be determined according to the preset weight of the acquisition region and the matching degree of the candidate track and each track cluster belonging to the acquisition region aiming at each acquisition region through which the candidate track passes. For example, for each acquisition region through which the candidate trajectory passes, the matching degrees of the candidate trajectory and each trajectory cluster in the acquisition region may be summed and multiplied by the weight of the acquisition region to determine the total matching degree of the candidate trajectory and the acquisition region. And determining the sum of the total matching degrees of the candidate track and each acquisition area passing through the candidate track as the comprehensive matching degree of the candidate track.
Optionally, when determining the comprehensive matching degree of the candidate track, it may be determined whether the candidate track passes through the acquisition area, and further distinguish between the acquisition area where the candidate track passes and the acquisition area where the candidate track does not pass. As shown in fig. 3, if a candidate trajectory passes through only the acquisition regions a11, a21, and a31, the total matching degrees of the candidate trajectory with the acquisition regions a12, a22, and a32 are all adjusted to zero, that is, the matching degrees of the candidate trajectory with each of the trajectory clusters in the acquisition regions a12, a22, and a32 are all adjusted to zero.
In an optional embodiment of the present specification, in step S106, determining the driving trajectory of the current vehicle in the candidate trajectories based on the matching degree between each candidate trajectory and each trajectory cluster may include: and determining the running track of the current vehicle in each candidate track according to preset constraint conditions and the matching degree of each candidate track with each track cluster.
When the running track of the current vehicle is determined, the preset constraint conditions are added, so that the determined running track of the current vehicle can better accord with the running conditions of the current vehicle.
The preset constraint conditions may include: curvature constraints of the candidate trajectory (e.g., if the curvature of the candidate trajectory is large, the candidate trajectory does not satisfy the curvature constraints of the candidate trajectory), inter-vehicle distance constraints of the vehicle and each of the other vehicles, and the like.
The method in the embodiment of the present specification determines the basis of the travel track of the current vehicle, and may further include preset constraint conditions in addition to the matching degree between each candidate track and each track cluster, so that the method in the embodiment of the present specification can at least integrate the factors of the matching degree and the constraint conditions, and determine the travel track of the current vehicle in each candidate track.
Optionally, the candidate tracks may be screened according to preset constraint conditions, candidate tracks that do not meet the preset constraint conditions are excluded, and then the candidate tracks obtained after screening are selected according to the matching degree to determine the driving track of the current vehicle. Or, the candidate tracks can be screened according to the matching degree, the candidate tracks with poor matching degree with the track clusters in the collection areas are eliminated, and then the candidate tracks obtained after screening are selected according to the preset constraint conditions to determine the running track of the current vehicle.
Of course, the method in the embodiment of the present specification may also determine, for each candidate trajectory, a score of the candidate trajectory according to a constraint condition that the candidate trajectory satisfies, determine, in each candidate trajectory, a travel trajectory of the current vehicle according to the candidate trajectory score and a matching degree of the candidate trajectory and each trajectory cluster, for example, perform summation calculation on the candidate trajectory score and the matching degree of the candidate trajectory and each trajectory cluster, and select a candidate trajectory with a largest calculation result value as the travel trajectory of the current vehicle.
Based on the service execution method shown in fig. 1, the embodiment of the present specification further provides a schematic structural diagram of an apparatus for determining a driving trajectory of an unmanned vehicle, as shown in fig. 4.
Fig. 4 is a schematic structural diagram of an apparatus for determining a driving trajectory of an unmanned vehicle according to an embodiment of the present disclosure, where the apparatus includes:
the obtaining module 500 may be configured to obtain candidate tracks planned in advance by the current vehicle at the current time, and collect driving tracks of other vehicles.
The clustering module 502 may be configured to cluster the driving tracks of the other vehicles to obtain at least one track cluster, where each track cluster at least includes the driving track of one other vehicle.
The matching degree determining module 504 may be configured to determine, for each candidate trajectory, a matching degree between the candidate trajectory and each trajectory cluster according to the travel trajectories of other vehicles included in each trajectory cluster.
The driving trajectory determining module 506 may be configured to determine the driving trajectory of the current vehicle in the candidate trajectories based on a matching degree of each candidate trajectory and each trajectory cluster.
The obtaining module 500 is in communication connection with the clustering module 502, and the matching degree determining module 504 is in communication connection with the obtaining module 500, the clustering module 502 and the driving track determining module 506 respectively.
Optionally, the acquisition module 500 may include an acquisition region determination sub-module 5000 and an acquisition sub-module 5002. The acquisition region determination sub-module 5000 is communicatively coupled to the acquisition sub-module 5002.
The acquisition region determination sub-module 5000 may be configured to determine each of the acquisition regions divided in advance in the acquisition range of the current vehicle.
The acquisition submodule 5002 may be configured to acquire a travel track of each other vehicle falling into each acquisition area. So that the clustering module 502 can cluster the driving tracks of other vehicles falling into the collection area for each collection area to obtain at least one track cluster in the collection area.
Optionally, the matching degree determination module 504 includes a center trajectory determination sub-module 5040 and a matching degree determination sub-module 5042. The center trajectory determination sub-module 5040 and the matching degree determination sub-module 5042 are electrically connected.
The center trajectory determination sub-module 5040 may be configured to determine, for each trajectory cluster, a center trajectory of the trajectory cluster based on the travel trajectories of other vehicles in the trajectory cluster.
The matching degree determining sub-module 5042 may be configured to determine, according to the center trajectory of the trajectory cluster, a matching degree between the candidate trajectory and the center trajectory, as the matching degree between the candidate trajectory and the trajectory cluster.
Optionally, the center trajectory determination sub-module 5040 may include an average trajectory determination unit and a center trajectory determination unit. The average trajectory determination unit is communicatively coupled to the center trajectory determination unit.
And the average track determining unit can be used for determining the average track of the track cluster according to the running tracks of other vehicles in the track cluster.
A central track determining unit, configured to use the determined average track as a central track of the track cluster; or determining the average track of the track cluster according to the running tracks of other vehicles in the track cluster, determining the similarity between the running track of each other vehicle and the average track, and selecting the central track of the track cluster from the running tracks of other vehicles in the track cluster according to the similarity between the running track of each other vehicle and the average track.
Alternatively, the matching degree determination sub-module 5042 may include a similarity determination unit and a matching degree determination unit. The similarity determining unit is in communication connection with the matching degree determining unit.
The similarity determination unit may be configured to determine a similarity between the candidate trajectory and the center trajectory.
The matching degree determining unit may be configured to determine the matching degree between the candidate trajectory and the center trajectory according to a distance between the current vehicle and an acquisition area where the trajectory cluster to which the center trajectory belongs is located, and a similarity between the candidate trajectory and the center trajectory.
Alternatively, the travel track determination module 506 may include a constraint condition determination submodule and a travel track determination submodule. And the constraint condition determining submodule is in communication connection with the driving track determining submodule.
And the constraint condition determining submodule can be used for determining preset constraint conditions.
And the running track determining submodule can be used for determining the running track of the current vehicle in each candidate track according to the preset constraint conditions and the matching degree of each candidate track and each track cluster.
Alternatively, the travel track determination submodule may include a comprehensive matching degree determination unit and a travel track determination unit. And the comprehensive matching degree determining unit is in communication connection with the driving track determining unit.
And the comprehensive matching degree determining unit may be configured to determine, for each acquisition region through which the candidate trajectory passes, a comprehensive matching degree between the candidate trajectory and the acquisition region according to a matching degree between the candidate trajectory and each trajectory cluster belonging to the acquisition region.
The driving track determining unit may be configured to determine the driving track of the current vehicle in each candidate track according to the respective comprehensive matching degrees of the candidate track and each collection area where the candidate track passes through.
Embodiments of the present specification also provide a computer-readable storage medium, where the storage medium stores a computer program, and the computer program can be used to execute the order reassignment method provided in fig. 1.
Based on the method for service execution shown in fig. 1, the embodiment of the present specification further proposes an unmanned vehicle as shown in fig. 5. As shown in fig. 5, the unmanned vehicle includes, at a hardware level, a processor, an internal bus, a network interface, a memory, and a non-volatile memory, although it may include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs the computer program to implement the order reassignment method described in fig. 1 above.
Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (10)
1. A method of determining a trajectory of an unmanned vehicle, comprising:
acquiring candidate tracks planned in advance by a current vehicle at the current moment, and acquiring running tracks of other vehicles;
clustering the driving tracks of other vehicles to obtain at least one track cluster, wherein each track cluster at least comprises the driving track of one other vehicle;
for each candidate track, determining the matching degree of the candidate track and each track cluster according to the running tracks of other vehicles contained in each track cluster;
and determining the running track of the current vehicle in each candidate track based on the matching degree of each candidate track and each track cluster.
2. The method of claim 1, wherein collecting the travel trajectory of each of the other vehicles comprises:
determining each pre-divided acquisition area in the acquisition range of the current vehicle;
collecting the running track of each other vehicle falling into each collection area;
clustering the driving tracks of other vehicles to obtain at least one track cluster, which specifically comprises the following steps:
and clustering the running tracks of other vehicles falling into the acquisition area aiming at each acquisition area to obtain at least one track cluster in the acquisition area.
3. The method according to claim 2, wherein determining the matching degree of the candidate trajectory with each trajectory cluster according to the travel trajectories of other vehicles included in each trajectory cluster specifically comprises:
aiming at each track cluster, determining the central track of the track cluster according to the running tracks of other vehicles in the track cluster;
and determining the matching degree of the candidate track and the central track according to the central track of the track cluster, and taking the matching degree of the candidate track and the track cluster as the matching degree of the candidate track and the track cluster.
4. The method according to claim 3, wherein determining the center trajectory of the trajectory cluster based on the travel trajectories of other vehicles in the trajectory cluster specifically comprises:
determining the average track of the track cluster according to the running tracks of other vehicles in the track cluster, and taking the average track as the central track of the track cluster; or
According to the running tracks of other vehicles in the track cluster, determining the average track of the track cluster, determining the similarity between the running track of each other vehicle and the average track, and according to the similarity between the running track of each other vehicle and the average track, selecting the central track of the track cluster from the running tracks of other vehicles in the track cluster.
5. The method of claim 3, wherein determining the degree of matching between the candidate trajectory and the center trajectory specifically comprises:
determining the similarity between the candidate track and the central track;
and determining the matching degree of the candidate track and the central track according to the distance between the current vehicle and the acquisition area where the track cluster to which the central track belongs is located and the similarity of the candidate track and the central track.
6. The method according to claim 1, wherein determining the driving trajectory of the current vehicle in the candidate trajectories based on the matching degree of each candidate trajectory with each trajectory cluster specifically comprises:
and determining the running track of the current vehicle in each candidate track according to the preset constraint conditions and the matching degree of each candidate track with each track cluster.
7. The method according to claim 2, wherein determining the driving trajectory of the current vehicle in the candidate trajectories based on the matching degree of each candidate trajectory with each trajectory cluster specifically comprises:
determining the comprehensive matching degree of the candidate track according to the matching degree of each track cluster in each acquisition area of the candidate track passing through and the candidate track;
and determining the running track of the current vehicle in each candidate track according to the determined comprehensive matching degree of each candidate track.
8. An apparatus for determining a trajectory of an unmanned vehicle, comprising:
the acquisition module is used for acquiring each candidate track planned in advance by the current vehicle at the current moment and acquiring the running track of each other vehicle;
the clustering module is used for clustering the driving tracks of other vehicles to obtain at least one track cluster, wherein each track cluster at least comprises the driving track of one other vehicle;
the matching degree determining module is used for determining the matching degree of each candidate track and each track cluster according to the running tracks of other vehicles contained in each track cluster;
and the running track determining module is used for determining the running track of the current vehicle in each candidate track based on the matching degree of each candidate track and each track cluster.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-7.
10. An unmanned vehicle comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method of claims 1-7.
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| PCT/CN2020/113224 WO2021052185A1 (en) | 2019-09-19 | 2020-09-03 | Determining driving trajectory of intelligent driving vehicle |
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