CN115246416B - Track prediction method, track prediction device, track prediction equipment and computer readable storage medium - Google Patents
Track prediction method, track prediction device, track prediction equipment and computer readable storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0011—Planning or execution of driving tasks involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0015—Planning or execution of driving tasks specially adapted for safety
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Abstract
The application provides a track prediction method, which comprises the following steps: acquiring vehicle surrounding information of a target vehicle at the current moment; classifying the target obstacles around the target vehicle according to at least one preset classification mode according to the information about the obstacles in the information around the vehicle; determining a track prediction strategy of the target obstacle based on the classification result of the target obstacle; and predicting at least one future moving track of the target obstacle according to a track prediction strategy, and predicting the possibility that each future moving track is selected by the target obstacle. The method comprehensively describes the possible track space of the target obstacle, and improves the accuracy of the obstacle track prediction result. The application also provides a track prediction device, equipment and a computer readable storage medium.
Description
Technical Field
The present application relates to the field of control technologies, and in particular, to a track prediction method, apparatus, device, and computer readable storage medium.
Background
With the gradual maturity of the automatic driving technology, the application of logistics distribution, shared travel, sanitation operation and the like is safer and more efficient. The automatic driving automobile needs to sense surrounding environment information in real time, and timely makes interactive decisions according to the motion states of obstacles such as pedestrians, other vehicles and the like to ensure running safety, so that the automatic driving automobile needs to utilize various information to make more accurate predictions on future motion tracks of the obstacles, and make reasonable interactive actions such as speed reduction, yield, parking waiting and the like.
In the automatic driving system, the prediction interaction system receives various information such as obstacle states, map semantics, structures, traffic rules and the like provided by an upstream module, and adopts modes such as a mathematical model, rules and the like to realize the prediction of future movement tracks of other obstacles around the automatic driving vehicle, and the selection of the interaction mode of the automatic driving vehicle and the obstacles, so that the automatic driving vehicle is helped to plan a reasonable driving track under a complex running environment, and the safe avoidance when necessary is realized.
In the prior art, obstacle trajectory prediction and interactive decision making are typically accomplished using mathematical models or rules. Recording historical information of obstacles around the automatic driving vehicle, including position coordinates, speed, time stamp and the like, inputting the information into a model for regression analysis or solving according to rules, so as to obtain future movement tracks of the obstacles, and completing interaction between the automatic driving vehicle and the obstacles according to the position, speed, future tracks and the like of the obstacles. However, when the prior art scheme is adopted to predict the track of the obstacle, the predicted track is mostly single in number, and the structure level of the prediction method is simpler, so that the accuracy of the track prediction result is lower.
Disclosure of Invention
In view of the above, the present application provides a track prediction method, apparatus, device, and computer readable storage medium, which can improve accuracy of a track prediction result when predicting a moving track of an obstacle.
Specifically, the application is realized by the following technical scheme:
a trajectory prediction method, comprising:
acquiring vehicle surrounding information of a target vehicle at the current moment;
classifying the target obstacles around the target vehicle according to at least one preset classification mode in the information about the obstacles in the information around the vehicle;
determining a track prediction strategy of the target obstacle based on the classification result of the target obstacle;
and predicting at least one future moving track of the target obstacle according to the track prediction strategy, and predicting the possibility of each future moving track being selected by the target obstacle.
A trajectory prediction device, comprising:
an information acquisition unit configured to acquire vehicle surrounding information of a target vehicle at a current time;
an obstacle classification unit configured to classify, according to at least one preset classification manner, a target obstacle around the target vehicle according to information about the obstacle in the vehicle surrounding information;
a policy determination unit configured to determine a trajectory prediction policy of the target obstacle based on a classification result of the target obstacle;
and the track prediction unit is used for predicting at least one future running track of the target obstacle according to the track prediction strategy and predicting the possibility of each future running track being selected by the target obstacle.
An electronic device, comprising: a processor, a memory;
the memory is used for storing a computer program;
the processor is used for executing the track prediction method by calling the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the trajectory prediction method described above.
In the technical scheme provided by the application, for each target obstacle around the target vehicle, one or more future running tracks of the target obstacle can be predicted, the possibility that each future running track is selected by the target obstacle is predicted, the predicted tracks can better express the uncertainty of the actual movement of the target obstacle, the possible track space of the target obstacle is comprehensively described, and the accuracy of the predicted result of the obstacle track is improved. Moreover, through more perfect prediction of the track space, the target vehicle can obtain the estimation result of the future movement track of the target obstacle in advance, and more reasonable interaction between the target vehicle and the target obstacle can be helped, so that safe operation of the target vehicle is realized.
Drawings
FIG. 1 is a schematic flow chart of a track prediction method according to the present application;
FIG. 2 is a schematic diagram of a track prediction device according to the present application;
fig. 3 is a schematic structural diagram of an electronic device according to the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the application. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.
It should be noted that in some existing track prediction methods, when predicting the track of the obstacle, the predicted track is mostly a single number, in practice, for different types of obstacles, the movement modes are different, for example, the pedestrian can directly move along the road or cross the road, and the vehicle can directly pass through the intersection or turn to other directions, so the future movement track of the obstacle often has stronger uncertainty, while in the prior art, only a single track is used to describe the future movement mode of the obstacle, so that the spatial position of the obstacle cannot be accurately estimated, and the interaction between the autonomous vehicle and the obstacle is difficult. In addition, the prediction method in the prior art scheme has simple structure level, cannot perform hierarchical prediction according to the environment where the automatic driving vehicle and the obstacle are located, the type of the obstacle, the traffic condition and the like, and has low detail degree, so that the track prediction accuracy is low.
To this end, an embodiment of the present application provides a track prediction method, referring to fig. 1, which is a schematic flow chart of the track prediction method provided by the embodiment of the present application, where the method includes steps S101 to S104:
s101: and acquiring the vehicle surrounding information of the target vehicle at the current moment.
Any automatic driving automobile can be defined as a target vehicle, the target vehicle can continuously acquire the surrounding information of the vehicle in a certain frequency range by utilizing the self perception module in the actual running process, and then a prediction interaction system of the target vehicle receives the surrounding information of the vehicle provided by the upstream perception module.
In the embodiment of the application, the vehicle surrounding information may include obstacle state information around the target vehicle, and may also include, but is not limited to, map environment information around the target vehicle, traffic regulation restriction information, and other information.
Wherein, regarding obstacle state information, it relates to state information of one or more obstacles around the target vehicle, where each obstacle is defined as a target obstacle, and in order to acquire the state information of the target obstacle, related calculations involved include, but are not limited to, coordinate conversion of position coordinates, speed, acceleration, and calculation of attitude angle of the target obstacle. For the obstacle state information, after calculation is completed, the obstacle state information may be stored in order to be a history feature of the obstacle.
The map environment information can be obtained by caching the lane information, and related calculation includes but is not limited to data preprocessing such as lane cache construction, lane center curve fitting and the like. For map-related information, only one copy of the cached data may be maintained to reduce duplicate computations.
The traffic regulation limit information refers to traffic regulation limit of lanes around the target vehicle, including but not limited to vehicle speed limit, straight line limit, turning limit, and the like.
S102: classifying the target obstacle around the target vehicle according to at least one preset classification mode according to the information about the obstacle in the information around the vehicle.
In the embodiment of the application, for each target obstacle around the target vehicle, the prediction interaction system of the target vehicle can extract the state information of the target obstacle from the information around the vehicle, and judge the type of the target obstacle according to the state information of the target obstacle. The classification method of the target obstacle includes, but is not limited to, obstacle types (such as vehicles, pedestrians, bicycles, etc.), obstacle speeds (such as high speed, low speed, etc.), obstacle operation intention (such as lane changing, turning, parking, etc.). When classifying the target obstacle according to one or more preset classification modes, one or more classification results of the target obstacle can be correspondingly obtained.
S103: and determining a track prediction strategy of the target obstacle based on the classification result of the target obstacle.
In the embodiment of the application, a track prediction algorithm (i.e., a track prediction strategy) suitable for the target obstacle can be selected according to the classification result of the target obstacle, the rule according to which is set during the selection can be set through a configuration file, and the corresponding relation between the classification result of the obstacle to be marked and the track prediction algorithm in the configuration file, such as a track prediction algorithm A corresponding to the classification result A of the obstacle, a track prediction algorithm B corresponding to the classification result B of the obstacle, and the like. When the configuration file is read, a corresponding track prediction algorithm can be selected according to rules in the configuration file according to the classification result of the target obstacle.
In addition, the embodiment of the application can also determine the track prediction strategy of the target obstacle based on the combined classification result of the target obstacle and the surrounding environment of the vehicle. Specifically, in one implementation manner of the embodiment of the present application, the step S103 may include the following steps A1-A2:
step A1: and classifying the vehicle surrounding environment of the target vehicle according to at least one preset classification mode according to the information about the environment of the vehicle in the vehicle surrounding information.
In this step, the prediction interaction system of the target vehicle may extract, from the vehicle surrounding information, the position coordinates where the target vehicle is located, map environment information, and the like, so as to perform type judgment on the vehicle surrounding environment of the target vehicle according to the position coordinates where the target vehicle is located, map environment information, and the like. The classification manner of the surrounding environment of the vehicle includes, but is not limited to, region types (such as city, country, etc.), traffic conditions (such as smoothness, congestion, etc.), road scenes (such as expressways, crossroads, parking lots, etc.). When the vehicle surrounding environment of the target vehicle is classified according to one or more preset classification modes, one or more classification results of the vehicle surrounding environment can be correspondingly obtained.
Step A2: and determining a track prediction strategy of the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle.
In this step, different models, different rules and the like can be preset for predicting the track of the obstacle, based on which, when the classification results of the target obstacle and the surrounding environment of the vehicle are determined, the corresponding models, rules and the like are called to form a track prediction strategy for predicting the running track of the target obstacle in a hierarchical manner.
In one implementation of the embodiment of the present application, step A2 may include: and selecting a track prediction strategy of the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle based on the preset classification result of the obstacle and the surrounding environment in the configuration file and the corresponding relation between the classification result of the obstacle and the surrounding environment and the track prediction strategy of the obstacle.
In this implementation manner, a trajectory prediction algorithm (i.e., a trajectory prediction strategy) applicable to the target obstacle and the surrounding environment of the vehicle may be selected according to the classification results of the target obstacle and the surrounding environment of the vehicle, and rules according to the selection may be set through a configuration file, where the classification results of the obstacle, the correspondence between the classification results of the surrounding environment of the vehicle and the trajectory prediction algorithm, for example, the classification result a of the obstacle and the surrounding environment corresponds to the trajectory prediction algorithm a, the classification result B of the obstacle and the surrounding environment corresponds to the trajectory prediction algorithm B, and so on. When the configuration file is read, a corresponding track prediction algorithm can be selected according to rules in the configuration file according to classification results of the target obstacle and the surrounding environment of the vehicle.
Further, the embodiment of the application can further comprise: and determining an interaction strategy between the target vehicle and the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle. In specific implementation, the interaction strategy between the target vehicle and the target obstacle can be selected according to the classification result of the target obstacle and the surrounding environment of the vehicle based on the preset classification result of the obstacle and the surrounding environment in the configuration file and the corresponding relation between the vehicle interaction strategy.
Similar to the selection of the trajectory prediction algorithm, an interaction scheme (i.e., an interaction strategy) applicable to the target vehicle and the target obstacle can be selected according to the classification results of the target obstacle and the surrounding environment of the vehicle, rules according to which during the selection can be set through a configuration file, and the configuration file needs to be marked with the correspondence between the classification results of the obstacle and the surrounding environment of the vehicle, such as the correspondence between the classification result a of the obstacle and the surrounding environment and the interaction scheme a, the correspondence between the classification result B of the obstacle and the surrounding environment and the interaction scheme B, and so on. When the configuration file is read, a corresponding interaction scheme can be selected according to rules in the configuration file according to classification results of the target obstacle and the surrounding environment of the vehicle.
S104: and predicting at least one future moving track of the target obstacle according to a track prediction strategy of the target obstacle, and predicting the possibility that each future moving track is selected by the target obstacle.
In the embodiment of the application, after the track prediction strategy of the target obstacle and the interaction strategy between the target vehicle and the target obstacle are determined through the steps, relevant characteristic information required by the corresponding strategy needs to be calculated. It will be appreciated that, since the input information required by the different policy methods is different, the vehicle surrounding information obtained in the step S101 may be used as basic information to complete the calculation of the input information required by the corresponding policy, where the input information includes, but is not limited to, the characteristics of the target obstacle (such as average speed, average acceleration, etc.), the characteristics between the target obstacle and the map (such as distance between the obstacle and the nearest lane, direction angle, etc.), the characteristics of the map (such as topological relation between lanes, shape characteristics, etc.), and so on.
Then, using the provided multiple input information, predicting multiple future running tracks of the target obstacle by using track prediction strategies including but not limited to rules, optimization algorithms and machine learning models, wherein each prediction algorithm can be realized and packaged into a predictor, and related functions of parameter loading, initialization, prediction and the like of the multiple algorithms can be realized simply and efficiently through a predictor manager. It should be noted that, in some special road environments, only one movement track may be predicted.
In practice, the uncertainty of the actual movement of the obstacle can be well expressed by the multiple predicted tracks, and compared with a single predicted track, the accurate track prediction result can be obtained with higher probability, so that the automatic driving vehicle is helped to realize more reasonable interaction.
In addition, after solving the possible multiple future running tracks of the target obstacle, the embodiment of the application can further solve the probability value of each future running track selected by the target obstacle, wherein the probability value reflects the possibility of the corresponding future running track selected by the target obstacle, and can describe the possible track space of the target obstacle more fully.
In one implementation manner of the embodiment of the present application, the "predicting the possibility that each moving track is selected by the target obstacle" in S104 may include: and predicting the possibility that each future moving track is selected by the target obstacle according to the historical moving track of the historical obstacle, the vehicle surrounding information of the target vehicle and each predicted future moving track of the target obstacle.
In this implementation manner, the probability of each future moving track being selected by the target obstacle may be obtained by using information such as a historical moving track of the historical obstacle (for example, a historical obstacle of the same type as the target obstacle), vehicle surrounding information of the target vehicle (for example, map environment information in the vehicle surrounding information), each future moving track of the predicted target obstacle, and the like, using a machine learning ordering algorithm including, but not limited to, lambdamar. For example, in actual prediction, when the target obstacle approaches the intersection area, the predictor will obtain multiple possible prediction tracks such as straight running, turning around, and the like, the track distribution is more divergent, however, when the target obstacle shows obvious intention, the predictor will give a relatively concentrated prediction track, and the process from divergent to convergent can show that the multi-track prediction covers the track space of the target obstacle more completely and accurately.
Therefore, in the embodiment of the application, the target vehicle can obtain the estimation of the future motion track of the target obstacle earlier by more perfect prediction of the track space of the target obstacle, so that the target vehicle can make interaction with the target obstacle in advance, and safer interaction results are presented.
Further, the embodiment of the application can further comprise: and verifying the validity of the future moving track of the target obstacle, wherein the validity verification comprises verifying at least one of whether the geometric characteristics of the track are reasonable, whether the track meets the kinematic constraint and whether the track collides with the map structure.
Specifically, each future movement trajectory of the predicted target obstacle may be validated to ensure that the future movement trajectory provided to the downstream system is effective and reasonable, ensuring safe operation of the target vehicle. For each future moving track of the target obstacle, when the validity of the future moving track is verified, if the track geometric characteristics of the future moving track are reasonable, the track meets kinematic constraint, and the track does not conflict with the map structure, the future moving track is valid, and then the target vehicle can realize normal interaction with the target obstacle based on the future moving track.
The verification of whether the track geometric characteristics are reasonable may include: at least one of whether the running track forms a loop and whether the running track is smooth. Generally, when verifying a future moving track, if the track is looped, the track is unreasonable, otherwise, the track is reasonable; if the track is not smooth, the track is unreasonable and vice versa.
The verification content of whether the track meets the kinematic constraint may include: at least one of the maximum acceleration required by the running track and the track curvature exceeds the steering angle range of the vehicle when the vehicle runs normally. Generally, when verifying a future moving track, if the acceleration required by the track exceeds the maximum acceleration when the vehicle is running normally, the track is unreasonable, otherwise, the track is reasonable; if the track curvature of the track is beyond the steering angle range of the vehicle, the track is unreasonable, otherwise, reasonable.
The verification content of whether the track conflicts with the map structure may include: whether the track spans at least one of a plurality of lanes, whether the track is retrograde, and whether the track is out of map region. Generally, when verifying a future trajectory, if the trajectory spans multiple lanes, the trajectory is unreasonable, and vice versa; if the track is retrograde, the track is unreasonable, otherwise, reasonable; if the track is off the map area, the track is unreasonable and vice versa.
Further, the embodiment of the application can further comprise: determining a reference travel route of the target vehicle based on static obstacles around the target vehicle; judging whether the target vehicle collides with the dynamic obstacle in a preset time or not based on the future running track of the dynamic obstacle around the target vehicle; and determining the actual driving route of the target vehicle according to the judging result.
In particular, the interaction between the target vehicle and the obstacle may be achieved on a "speed let away" principle. After each static obstacle around the vehicle is considered by a path planning module of the target vehicle, calculating and generating a reference running route of the target vehicle, wherein the target vehicle can run along the reference running route, and when encountering a dynamic obstacle (namely, a certain target obstacle around the target vehicle mentioned in the previous description), the target vehicle is processed by interaction logic during running; during processing, whether the target vehicle collides with the dynamic obstacle in a preset time (such as 10 seconds) is determined according to each predicted future running track of the dynamic obstacle, so as to form a series of dynamic constraints, and a proper acceleration value without collision is finally selected for the target vehicle by combining acceleration constraints of vehicle kinematics, so that the final actual running track of the target vehicle is determined. In the actual running process, the interaction between the target vehicle and the dynamic obstacle is realized based on the interaction strategy mentioned in the foregoing, wherein the interaction strategy can adopt different strategies such as conservation, aggressive and the like for the dynamic obstacle in the following, cutting-in, traversing, reversing and other running states.
In the track prediction method provided by the embodiment of the application, for each target obstacle around the target vehicle, one or more future running tracks of the target obstacle can be predicted, the possibility that each future running track is selected by the target obstacle is predicted, the predicted tracks can better express the uncertainty of the actual movement of the target obstacle, the possible track space of the target obstacle is comprehensively described, and the accuracy of the predicted result of the track of the obstacle is improved. Moreover, through more perfect prediction of the track space, the target vehicle can obtain the estimation result of the future movement track of the target obstacle in advance, and more reasonable interaction between the target vehicle and the target obstacle can be helped, so that safe operation of the target vehicle is realized.
It should be noted that, the track prediction method provided by the embodiment of the application is more suitable for the target vehicle in the low-speed scene.
Referring to fig. 2, a schematic diagram of a track prediction apparatus according to an embodiment of the present application is provided, where the apparatus includes:
an information acquisition unit 210 for acquiring vehicle surrounding information of the target vehicle at the current time;
an obstacle classification unit 220, configured to classify, according to at least one preset classification manner, a target obstacle around the target vehicle according to information about the obstacle in the vehicle surrounding information;
a policy determining unit 230 for determining a trajectory prediction policy of the target obstacle based on a classification result of the target obstacle;
the trajectory prediction unit 240 is configured to predict at least one future moving trajectory of the target obstacle according to the trajectory prediction strategy, and predict a likelihood that each future moving trajectory is selected by the target obstacle.
In one implementation of the embodiment of the present application, the policy determining unit 230 includes:
the environment classification subunit is used for classifying the vehicle surrounding environment of the target vehicle according to at least one preset classification mode according to the information about the environment of the vehicle in the vehicle surrounding information;
and the track strategy determination subunit is used for determining a track prediction strategy of the target obstacle according to the classification results of the target obstacle and the surrounding environment of the vehicle.
In one implementation manner of the embodiment of the present application, the track policy determining subunit is specifically configured to:
and selecting a track prediction strategy of the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle based on the preset classification result of the obstacle and the surrounding environment in the configuration file and the corresponding relation between the classification result of the obstacle and the surrounding environment and the track prediction strategy of the obstacle.
In one implementation manner of the embodiment of the present application, the policy determining unit 230 further includes:
and the interaction strategy determining subunit is used for determining the interaction strategy between the target vehicle and the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle.
In one implementation manner of the embodiment of the present application, the interaction policy determining subunit is specifically configured to:
and selecting an interaction strategy between the target vehicle and the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle based on the classification result of the preset obstacle and the surrounding environment in the configuration file and the corresponding relation between the preset obstacle and the surrounding environment and the vehicle interaction strategy.
In one implementation of the embodiment of the present application, the track prediction unit 240 is specifically configured to:
and predicting the possibility that each future running track is selected by the target obstacle according to the historical running track of the historical obstacle, the vehicle surrounding information of the target vehicle and the predicted future running tracks of the target obstacle.
In one implementation manner of the embodiment of the present application, the apparatus further includes:
and the track verification unit is used for verifying the validity of the future running track of the target obstacle, wherein the validity verification comprises verification of at least one of whether the geometrical characteristics of the track are reasonable, whether the track meets kinematic constraint and whether the track collides with a map structure.
In one implementation manner of the embodiment of the present application, the verification of whether the track geometric characteristics are reasonable includes: at least one of whether the running track forms a loop on itself and whether the running track is smooth; verifying whether the trajectory satisfies the kinematic constraint includes: whether the acceleration required by the running track exceeds the maximum acceleration of the vehicle during normal running or whether the track curvature exceeds at least one of the steering angle ranges of the vehicle; the verification content for whether the track conflicts with the map structure comprises the following steps: whether the track spans at least one of a plurality of lanes, whether the track is retrograde, and whether the track is out of map region.
In one implementation manner of the embodiment of the present application, the apparatus further includes:
a reference route determination unit configured to determine a reference travel route of the target vehicle based on static obstacles around the target vehicle;
a collision judging unit configured to judge whether the target vehicle collides with a dynamic obstacle within a preset time based on a future moving trajectory of the dynamic obstacle around the target vehicle;
and the actual route determining unit is used for determining the actual running route of the target vehicle according to the judging result.
The implementation process of the functions and roles of each unit in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present application. Those of ordinary skill in the art will understand and implement the present application without undue burden.
The embodiment of the application also provides an electronic device, the structural schematic diagram of which is shown in fig. 3, the electronic device 3000 includes at least one processor 3001, a memory 3002 and a bus 3003, and at least one processor 3001 is electrically connected with the memory 3002; the memory 3002 is configured to store at least one computer executable instruction and the processor 3001 is configured to execute the at least one computer executable instruction to perform the steps of any one of the trajectory prediction methods as provided by any one of the embodiments or any one of the alternative implementations of the application.
Further, the processor 3001 may be an FPGA (Field-Programmable Gate Array, field programmable gate array) or other device having logic processing capabilities, such as an MCU (Microcontroller Unit, micro control unit), CPU (Central Process Unit, central processing unit).
By applying the embodiment of the application, one or more future running tracks of the target obstacle can be predicted, the possibility that each future running track is selected by the target obstacle can be predicted, the predicted tracks can better express the uncertainty of the target obstacle in actual movement, the possible track space of the target obstacle is comprehensively described, and the accuracy of the predicted result of the track of the obstacle is improved. Moreover, through more perfect prediction of the track space, the target vehicle can obtain the estimation result of the future movement track of the target obstacle in advance, and more reasonable interaction between the target vehicle and the target obstacle can be helped, so that safe operation of the target vehicle is realized.
The embodiment of the application also provides another computer readable storage medium, which stores a computer program for implementing the steps of any one of the track prediction methods provided in any one embodiment or any one of the optional embodiments of the application when the computer program is executed by a processor.
The computer readable storage medium provided by the embodiments of the present application includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs (Read-Only memories), RAMs (Random Access Memory, random access memories), EPROMs (Erasable Programmable Read-Only memories), EEPROMs (Electrically Erasable Programmable Read-Only memories), flash memories, magnetic cards, or optical cards. That is, a readable storage medium includes any medium that stores or transmits information in a form readable by a device (e.g., a computer).
By applying the embodiment of the application, one or more future running tracks of the target obstacle can be predicted, the possibility that each future running track is selected by the target obstacle can be predicted, the predicted tracks can better express the uncertainty of the target obstacle in actual movement, the possible track space of the target obstacle is comprehensively described, and the accuracy of the predicted result of the track of the obstacle is improved. Moreover, through more perfect prediction of the track space, the target vehicle can obtain the estimation result of the future movement track of the target obstacle in advance, and more reasonable interaction between the target vehicle and the target obstacle can be helped, so that safe operation of the target vehicle is realized.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather to enable any modification, equivalent replacement, improvement or the like to be made within the spirit and principles of the application.
Claims (10)
1. A track prediction method, comprising:
acquiring vehicle surrounding information of a target vehicle at the current moment;
classifying the target obstacles around the target vehicle according to at least one preset classification mode in the information about the obstacles in the information around the vehicle;
determining a track prediction strategy of the target obstacle based on the classification result of the target obstacle;
predicting at least one future moving track of the target obstacle according to the track prediction strategy, and predicting the possibility of each future moving track being selected by the target obstacle;
wherein, the input information of the track prediction strategy comprises: characteristics of the target obstacle itself, characteristics between the target obstacle and the map, and characteristics of the map;
characteristics of the target obstacle itself include: average speed and/or average acceleration of the target obstacle;
features between the target obstacle and the map include: the distance and/or the direction included angle between the obstacle and the nearest lane;
the map features include: topological relationships and/or shape features of lanes;
the determining a track prediction strategy of the target obstacle based on the classification result of the target obstacle comprises the following steps:
classifying the vehicle surrounding environment of the target vehicle according to at least one preset classification mode according to the information about the environment of the vehicle in the vehicle surrounding information;
determining a track prediction strategy of the target obstacle according to classification results of the target obstacle and the surrounding environment of the vehicle;
the determining a track prediction strategy of the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle comprises the following steps:
and selecting a track prediction strategy of the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle based on the preset classification result of the obstacle and the surrounding environment in the configuration file and the corresponding relation between the classification result of the obstacle and the surrounding environment and the track prediction strategy of the obstacle.
2. The method according to claim 1, wherein the method further comprises:
and determining an interaction strategy between the target vehicle and the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle.
3. The method of claim 2, wherein the determining an interaction strategy between the target vehicle and the target obstacle based on the classification of the target obstacle and the surrounding environment of the vehicle comprises:
and selecting an interaction strategy between the target vehicle and the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle based on the classification result of the preset obstacle and the surrounding environment in the configuration file and the corresponding relation between the preset obstacle and the surrounding environment and the vehicle interaction strategy.
4. The method of claim 1, wherein predicting the likelihood that each trajectory will be selected by the target obstacle comprises:
and predicting the possibility that each future running track is selected by the target obstacle according to the historical running track of the historical obstacle, the vehicle surrounding information of the target vehicle and the predicted future running tracks of the target obstacle.
5. The method according to any one of claims 1-4, further comprising:
and verifying the validity of the future running track of the target obstacle, wherein the validity verification comprises verifying at least one of whether the geometrical characteristics of the track are reasonable, whether the track meets kinematic constraint and whether the track collides with a map structure.
6. The method of claim 5, wherein the step of determining the position of the probe is performed,
verifying whether the track geometric characteristics are reasonable includes: at least one of whether the running track forms a loop on itself and whether the running track is smooth;
verifying whether the trajectory satisfies the kinematic constraint includes: whether the acceleration required by the running track exceeds the maximum acceleration of the vehicle during normal running or whether the track curvature exceeds at least one of the steering angle ranges of the vehicle;
the verification content for whether the track conflicts with the map structure comprises the following steps: whether the track spans at least one of a plurality of lanes, whether the track is retrograde, and whether the track is out of map region.
7. The method according to any one of claims 1-4, further comprising:
determining a reference travel route of the target vehicle based on static obstacles around the target vehicle;
judging whether the target vehicle collides with the dynamic obstacle in a preset time or not based on the future running track of the dynamic obstacle around the target vehicle;
and determining the actual driving route of the target vehicle according to the judging result.
8. A trajectory prediction device, comprising:
an information acquisition unit configured to acquire vehicle surrounding information of a target vehicle at a current time;
an obstacle classification unit configured to classify, according to at least one preset classification manner, a target obstacle around the target vehicle according to information about the obstacle in the vehicle surrounding information;
a policy determination unit configured to determine a trajectory prediction policy of the target obstacle based on a classification result of the target obstacle;
the track prediction unit is used for predicting at least one future running track of the target obstacle according to the track prediction strategy and predicting the possibility of each future running track being selected by the target obstacle;
wherein, the input information of the track prediction strategy comprises: characteristics of the target obstacle itself, characteristics between the target obstacle and the map, and characteristics of the map;
characteristics of the target obstacle itself include: average speed and/or average acceleration of the target obstacle;
features between the target obstacle and the map include: the distance and/or the direction included angle between the obstacle and the nearest lane;
the map features include: topological relationships and/or shape features of lanes;
wherein the policy determination unit includes:
the environment classification subunit is used for classifying the vehicle surrounding environment of the target vehicle according to at least one preset classification mode according to the information about the environment of the vehicle in the vehicle surrounding information;
a track strategy determination subunit, configured to determine a track prediction strategy of the target obstacle according to a classification result of the target obstacle and the surrounding environment of the vehicle;
the track strategy determination subunit is specifically configured to:
and selecting a track prediction strategy of the target obstacle according to the classification result of the target obstacle and the surrounding environment of the vehicle based on the preset classification result of the obstacle and the surrounding environment in the configuration file and the corresponding relation between the classification result of the obstacle and the surrounding environment and the track prediction strategy of the obstacle.
9. An electronic device, comprising: a processor, a memory;
the memory is used for storing a computer program;
the processor is configured to execute the trajectory prediction method according to any one of claims 1 to 7 by calling the computer program.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements the trajectory prediction method of any one of claims 1-7.
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| CN202110522250.1A CN115246416B (en) | 2021-05-13 | 2021-05-13 | Track prediction method, track prediction device, track prediction equipment and computer readable storage medium |
| PCT/CN2022/070452 WO2022237208A1 (en) | 2021-05-13 | 2022-01-06 | Trajectory prediction |
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| CN117429448B (en) * | 2023-10-24 | 2024-10-25 | 北京易航远智科技有限公司 | Prediction method and device for future occupied space of obstacle, electronic equipment and medium |
| CN117909895A (en) * | 2023-12-22 | 2024-04-19 | 曙光云计算集团股份有限公司 | Vehicle management method, device, computer equipment and storage medium |
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