CN112597822A - Vehicle track determination method and device, electronic equipment and computer storage medium - Google Patents
Vehicle track determination method and device, electronic equipment and computer storage medium Download PDFInfo
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- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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Abstract
The application provides a method and a device for determining a track of a vehicle, electronic equipment and a computer storage medium. The track determination method of the vehicle comprises the following steps: acquiring a position characteristic and a motion characteristic of a target vehicle; matching a target road mode from each preset road mode based on the position characteristics and the motion characteristics; the road mode is a mode obtained by preprocessing historical traffic data acquired by a road side camera and clustering the preprocessed historical traffic data; calculating a region constraint point based on the track end of the target road mode; generating a candidate track set based on the region constraint points; and determining the target track from the candidate track set according to the motion model of the target vehicle. According to the embodiment of the application, the track of the vehicle can be determined more accurately.
Description
Technical Field
The present application belongs to the field of intelligent transportation technologies, and in particular, to a method and an apparatus for determining a trajectory of a vehicle, an electronic device, and a computer storage medium.
Background
At present, the bicycle intelligence realizes most traffic scenes, but has the self limitation of limited perception range of the surrounding environment. Most algorithms for predicting the track in the automatic driving field are based on the fact that a vehicle end sensor acquires vehicle end state information in real time and predicts the future vehicle running track. The implementation of the algorithm mostly depends on real-time vehicle end state data of a vehicle end sensor, the global perception range of the automatic driving vehicle on the road is limited, and perception errors of the surrounding environment due to shielding and the like are more prone to occur.
Therefore, how to determine the trajectory of the vehicle more accurately is a technical problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
The embodiment of the application provides a method and a device for determining a track of a vehicle, electronic equipment and a computer storage medium, which can more accurately determine the track of the vehicle.
In a first aspect, an embodiment of the present application provides a method for determining a trajectory of a vehicle, including:
acquiring a position characteristic and a motion characteristic of a target vehicle;
matching a target road mode from each preset road mode based on the position characteristics and the motion characteristics; the road mode is a mode obtained by preprocessing historical traffic data acquired by a road side camera and clustering the preprocessed historical traffic data;
calculating a region constraint point based on the track end of the target road mode;
generating a candidate track set based on the region constraint points;
and determining the target track from the candidate track set according to the motion model of the target vehicle.
Optionally, before the matching of the target road mode from the preset road modes based on the position feature and the motion feature, the method further includes:
acquiring historical video stream data in the perception range of each road side camera;
based on historical video stream data, obtaining historical traffic stream data by using a detection tracking algorithm;
preprocessing historical traffic data to obtain preprocessed historical traffic data;
and clustering by using the preprocessed historical traffic data to obtain each road mode.
Optionally, the preprocessing is performed on the historical traffic data to obtain the preprocessed historical traffic data, and the preprocessing includes:
and carrying out invalid short track filtering and track point time normalization on the historical traffic data to obtain the preprocessed historical traffic data.
Optionally, clustering the preprocessed historical traffic data to obtain each road mode, including:
calculating a track similarity matrix based on the preprocessed historical traffic flow data;
calculating the local density value and the inter-cluster distance of each track according to the track similarity matrix;
and dividing based on the local density value and the inter-cluster distance of each track to obtain each road mode.
Optionally, the dividing is performed based on the local density value and the inter-cluster distance of each track to obtain each road mode, and the method includes:
obtaining a track weight dictionary according to the product value of the local density value and the inter-cluster distance;
and dividing according to the track weight dictionary and the acquired road environment information to obtain each road mode.
Optionally, matching a target road mode from each preset road mode based on the location feature and the motion feature, including:
respectively calculating the main track matching degree of the target vehicle and each road mode based on the position characteristics and the motion characteristics;
and determining a target road mode according to the matching degree of each main track.
Optionally, determining the target trajectory from the candidate trajectory set according to the motion model of the target vehicle includes:
calculating the average speed and the speed change rate of the target vehicle in the first direction and the second direction respectively; wherein the first direction is perpendicular to the second direction;
and determining a target track from the candidate track set based on the average component speed and the speed change rate.
In a second aspect, an embodiment of the present application provides a trajectory determination device for a vehicle, including:
the first acquisition module is used for acquiring the position characteristic and the motion characteristic of the target vehicle;
the matching module is used for matching a target road mode from each preset road mode based on the position characteristics and the motion characteristics; the road mode is a mode obtained by preprocessing historical traffic data acquired by a road side camera and clustering the preprocessed historical traffic data;
the calculation module is used for calculating a region constraint point based on the tail end of the track of the target road mode;
the generating module is used for generating a candidate track set based on the area constraint points;
and the determining module is used for determining the target track from the candidate track set according to the motion model of the target vehicle.
Optionally, the apparatus further comprises:
the second acquisition module is used for acquiring historical video stream data in the perception range of each road side camera;
the third acquisition module is used for acquiring historical traffic flow data by using a detection tracking algorithm based on the historical video flow data;
the preprocessing module is used for preprocessing the historical traffic data to obtain preprocessed historical traffic data;
and the clustering module is used for clustering by utilizing the preprocessed historical traffic flow data to obtain each road mode.
Optionally, the preprocessing module includes:
and the preprocessing unit is used for carrying out invalid short track filtering and track point time normalization on the historical traffic data to obtain the preprocessed historical traffic data.
Optionally, the clustering module includes:
the first calculation unit is used for calculating a track similarity matrix based on the preprocessed historical traffic flow data;
the second calculation unit is used for calculating the local density value and the inter-cluster distance of each track according to the track similarity matrix;
and the dividing unit is used for dividing based on the local density value and the inter-cluster distance of each track to obtain each road mode.
Optionally, the dividing unit includes:
the acquisition subunit is used for acquiring a track weight dictionary according to the product value of the local density value and the inter-cluster distance;
and the dividing subunit is used for dividing according to the track weight dictionary and the acquired road environment information to obtain each road mode.
Optionally, the matching module includes:
the third calculation unit is used for calculating the main track matching degree of the target vehicle and each road mode respectively based on the position characteristics and the motion characteristics;
and the first determining unit is used for determining the target road mode according to the matching degree of each main track.
Optionally, the determining module includes:
a fourth calculation unit for calculating an average component speed and a speed change rate of the target vehicle in the first direction and the second direction, respectively; wherein the first direction is perpendicular to the second direction;
and the second determining unit is used for determining the target track from the candidate track set based on the average speed and the speed change rate.
In a third aspect, an embodiment of the present application provides an electronic device, where the electronic device includes: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a trajectory determination method for a vehicle as shown in the first aspect.
In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, where the computer program instructions, when executed by a processor, implement the trajectory determination method for a vehicle as shown in the first aspect.
The method, the device, the electronic equipment and the computer storage medium for determining the track of the vehicle can determine the track of the vehicle more accurately. The vehicle track determining method comprises the steps of obtaining position characteristics and motion characteristics of a target vehicle; matching a target road mode from each preset road mode based on the position characteristics and the motion characteristics; the road mode is a mode obtained by preprocessing historical traffic data acquired by a road side camera and clustering the preprocessed historical traffic data; calculating a region constraint point based on the track end of the target road mode; generating a candidate track set based on the region constraint points; and determining the target track from the candidate track set according to the motion model of the target vehicle. Therefore, the method has the advantages that the roadside camera is used for obtaining the historical traffic data, the roadside camera has the advantages of wide range, fixed background and the like, namely has a wider visual angle than a single vehicle sensor, is larger in sensing range, can continuously sense static environment information, is insensitive to sensed noise information and the like. Therefore, the method and the device can combine position location with a road mode which is deeply excavated by using historical traffic flow data, predict the future track and the driving direction of the vehicle in the current perception range in advance, master the driving track of the global vehicle, and particularly can early warn the potential collision risk of the vehicle at the intersection in advance, help reduce the occurrence of traffic accidents, optimize the traffic flow in a small range and reduce the traffic jam condition.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram of a method for determining a trajectory of a vehicle according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram of a method for determining a trajectory of a vehicle according to another embodiment of the present application;
FIG. 3 is a schematic illustration of a local density-inter-cluster distance visualization provided by an embodiment of the present application;
FIG. 4 is a schematic diagram of a visualization of local density-cluster spacing product values provided by an embodiment of the present application;
FIG. 5 is a flow chart of a primary trajectory matching calculation provided by an embodiment of the present application;
FIG. 6 is a schematic diagram of a single mode-multiple mode system provided by one embodiment of the present application;
FIG. 7 is a schematic diagram illustrating a trajectory prediction process provided by one embodiment of the present application;
FIG. 8 is a schematic diagram of an example trajectory prediction provided by an embodiment of the present application;
FIG. 9 is a schematic structural diagram of a trajectory determination device of a vehicle according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
As known in the background art, most algorithms for predicting the track in the field of automatic driving are based on a vehicle end sensor to acquire vehicle end state information in real time and predict the future vehicle running track. The implementation of the algorithm mostly depends on real-time vehicle end state data of a vehicle end sensor, the global perception range of the automatic driving vehicle to the road is limited, and perception errors of the surrounding environment due to shielding and the like are more prone to occur.
In order to solve the prior art problems, embodiments of the present application provide a method and an apparatus for determining a trajectory of a vehicle, an electronic device, and a computer storage medium. First, a method for determining a trajectory of a vehicle according to an embodiment of the present application will be described.
Fig. 1 shows a schematic flowchart of a trajectory determination method of a vehicle according to an embodiment of the present application. As shown in fig. 1, the trajectory determination method of the vehicle may include:
s101, acquiring the position characteristic and the motion characteristic of the target vehicle.
S102, matching a target road mode from each preset road mode based on the position characteristics and the motion characteristics; the road mode is a mode obtained by preprocessing historical traffic data acquired by the road-side camera and clustering the preprocessed historical traffic data.
In one embodiment, matching a target road pattern from the respective preset road patterns based on the location features and the motion features comprises: respectively calculating the main track matching degree of the target vehicle and each road mode based on the position characteristics and the motion characteristics; and determining a target road mode according to the matching degree of each main track.
In one embodiment, before matching the target road mode from the preset road modes based on the position feature and the motion feature, the method further includes:
acquiring historical video stream data in the perception range of each road side camera;
based on historical video stream data, obtaining historical traffic stream data by using a detection tracking algorithm;
preprocessing historical traffic data to obtain preprocessed historical traffic data;
and clustering by using the preprocessed historical traffic data to obtain each road mode.
In one embodiment, the preprocessing the historical traffic data to obtain the preprocessed historical traffic data includes:
and carrying out invalid short track filtering and track point time normalization on the historical traffic data to obtain the preprocessed historical traffic data.
In one embodiment, clustering the preprocessed historical traffic data to obtain each road mode includes:
calculating a track similarity matrix based on the preprocessed historical traffic flow data;
calculating the local density value and the inter-cluster distance of each track according to the track similarity matrix;
and dividing based on the local density value and the inter-cluster distance of each track to obtain each road mode.
In one embodiment, the dividing based on the local density value and the inter-cluster distance of each track to obtain each road mode includes:
obtaining a track weight dictionary according to the product value of the local density value and the inter-cluster distance;
and dividing according to the track weight dictionary and the acquired road environment information to obtain each road mode.
And S103, calculating a region constraint point based on the track tail end of the target road mode.
And S104, generating a candidate track set based on the area constraint points.
And S105, determining a target track from the candidate track set according to the motion model of the target vehicle.
In one embodiment, determining the target trajectory from the set of candidate trajectories according to a motion model of the target vehicle comprises:
calculating the average speed and the speed change rate of the target vehicle in the first direction and the second direction respectively; wherein the first direction is perpendicular to the second direction;
and determining a target track from the candidate track set based on the average component speed and the speed change rate.
The vehicle track determining method comprises the steps of obtaining position characteristics and motion characteristics of a target vehicle; matching a target road mode from each preset road mode based on the position characteristics and the motion characteristics; the road mode is a mode obtained by preprocessing historical traffic data acquired by a road side camera and clustering the preprocessed historical traffic data; calculating a region constraint point based on the track end of the target road mode; generating a candidate track set based on the region constraint points; and determining the target track from the candidate track set according to the motion model of the target vehicle. Therefore, the method has the advantages that the roadside camera is used for obtaining the historical traffic data, the roadside camera has the advantages of wide range, fixed background and the like, namely has a wider visual angle than a single vehicle sensor, is larger in sensing range, can continuously sense static environment information, is insensitive to sensed noise information and the like. Therefore, the method and the device can combine position location with a road mode which is deeply excavated by using historical traffic flow data, predict the future track and the driving direction of the vehicle in the current perception range in advance, master the driving track of the global vehicle, and particularly can early warn the potential collision risk of the vehicle at the intersection in advance, help reduce the occurrence of traffic accidents, optimize the traffic flow in a small range and reduce the traffic jam condition.
The following describes the above technical solution with a specific scene embodiment.
The method provided by the embodiment can be mainly divided into three parts: 1) firstly, collecting historical traffic flow information from a historical video flow shot by a road side camera by using a detection tracking algorithm, and preprocessing a traffic flow track; 2) clustering the traffic flow tracks based on a rapid clustering algorithm to extract features, and judging a future driving mode of the vehicle through position positioning; 3) and calculating a possible driving area at the tail end of the future vehicle based on the track cluster in the future driving mode of the vehicle, and regulating the driving track of the vehicle through the area constraint.
The general flow chart of the method can be specifically shown in fig. 2, and the following explains the road pattern extraction problem and the optimal trajectory prediction problem based on the historical traffic flow based on fig. 2. First, a road pattern extraction problem based on a history traffic flow will be explained.
In order to obtain the road mode and the corresponding track cluster, the embodiment needs three processes of track acquisition, track filtering and main track selection of the historical traffic flow.
1. Track acquisition:
1) acquiring historical video stream data in a sensing range of a current road side camera, and selecting a video stream with clear weather and high definition;
2) and based on the selected video stream data, obtaining all vehicle tracks passing through the road side camera by using a detection tracking algorithm, and storing the vehicle tracks.
2. Track preprocessing:
1) invalid short traces are filtered. The invalid short tracks mainly include too short tracks and stop tracks in the present embodiment, tracks with fewer track points may occur due to target detection or tracking errors, and the minimum track point threshold value in the present embodiment is set to 100, and the minimum track point threshold value may be adjusted according to the total number of tracks. The stopping trajectory refers to a non-driving vehicle trajectory in which some vehicles wait for stopping at the roadside, such a trajectory provides less information for the driving mode of the vehicle, and may also become a noise trajectory, and in this embodiment, a trajectory with a distance less than 60 is deleted by taking the distance between the head and the tail of the trajectory as a reference basis.
2) And (5) time normalization of the trace points. The vehicle running track has no uniform time standard, so in order to process the track difference value, the time needs to be aligned firstly and Tra is usedjTaking a track as an example, the track is time-aligned:
t′i＝ti-t1
namely, the current time is subtracted by the first track point time of the track, and all track normalization is calculated from the time point 0. Defining the track set after track screening as
TnThe number of tracks is indicated and is,
wherein P isnRepresenting the number of points of the track, representing information about one of the points of the track, Pk＝{tid,cxk,cyk,t′kDenotes the vehicle number, tkRepresenting the temporal order in the trace.
3. Selecting a main track of historical traffic flow:
in this embodiment, historical traffic flow information is clustered, a central track of the cluster becomes a primary track, and other tracks of the same cluster are called secondary tracks.
1) And calculating a track similarity matrix. The track similarity matrix is non-real-time update data, accuracy and applicability can be simply considered in calculation, and real-time performance does not need to be considered, so that the longest common substring algorithm is selected in the embodiment.
Hypothetical track TiAnd TjRespectively has a length of
And
then the longest common subsequence length of the track is:
where gamma is the similarity threshold for the trace points,
the similarity between the traces is:
obtaining a track correlation matrix: SMat ═ (S)i,j),i,j∈[1,Tn]
2) And calculating the local density value of each track according to the track correlation matrix SMat, wherein the calculation formula is as follows:
wherein the function
Sc> 0 and is an integer, a truncation distance, the value being specified by the user. RhoiIndicating that the ith track and other tracks are less than ScNumber of traces of distance.
3) And calculating the inter-cluster distance of each track according to the track correlation matrix, wherein the calculation formula is as follows:
where α is a self-defining term, this embodiment sets it to 0.1. Fig. 3 is a schematic diagram of visualization of local density-cluster spacing provided in an embodiment of the present application, where ρ and δ respectively represent the abscissa and ordinate of fig. 3.
4) Calculate ρ for each tracei·δiAfter the values are sorted in descending order, as shown in fig. 4, the dots represent main tracks, the pentagons represent secondary tracks, the first C corresponding tracks are selected as the road modes in the perception range by combining the road complexity under the current camera, and the main tracks are represented as the road modes in the perception range
C represents the number of track clusters.
5) And dividing other tracks into corresponding road modes according to the correlation degrees to obtain the road modes and the sub-tracks in the corresponding modes.
The road pattern extraction problem based on the historical traffic flow has been explained above, and the optimal trajectory prediction problem is explained below.
1. And calculating the matching degree of the target vehicle and the main track under the C road modes according to the position characteristics and the motion characteristics.
Calculating the distance matching degree of the current vehicle track and the historical vehicle running main mode and the corresponding point index value, traversing the main track
Calculating the distance between the target vehicle w and each main track, and expressing as P ═ PosDis in descending order of valuesw_k},k∈[1,C]When the distance between the vehicle w and each main mode is smaller than the threshold value, it indicates that the vehicle has the driving intention in the direction, and the calculation flow is as shown in fig. 5.
There may be multiple driving modes, such as single mode and multi-mode in fig. 6, by distance matching. Assuming that Z main modes are obtained through filtering and Z is less than or equal to C, in order to obtain the current optimal road mode of the vehicle, calculating the average speed of the target vehicle and the x axis of the main track k on the basis of the latest n track points of the target vehicle and the corresponding track points of the Z main tracks
And acceleration
Average speed on y-axis
And acceleration
Obtaining a correlation based on the motion state:
the values are expressed as M ═ MoveDis in descending orderw_k},k∈[1,Z]And selecting the main track corresponding to the minimum value as the current optimal running mode of the target vehicle.
2. And predicting the vehicle track in the optimal running mode.
1) And calculating a regional constraint point at the tail end of the track of the optimal running mode. A data set is prepared based on the driving pattern of the vehicle and the corresponding trajectory cluster, and features are extracted from the trajectory cluster as F ═ F1,f2,f3,f4}, label is label, f1Indicating the distance from the lane centerline when the track exits the intersection, f2Indicating speed, f3Representing acceleration, f4Indicating the azimuth angle, and label indicating the distance from the lane center line when the track passes through the intersection and enters the road.
And (3) selecting a linear regression algorithm to research the relation between the label and the characteristic F, and solving the formula of the label WT·F+w0Middle parameters W and W0After the parameters are obtained, the characteristics of the target vehicle are extracted and input into the vehicleFormula (iv) to obtain the predicted value
I.e., the region constraint points indicated by "x" in fig. 7.
2) Based on the region constraint points, a set of candidate trajectories is generated. The region constraint point is a place where the end of the predicted trajectory obtained based on the current vehicle travel information in combination with the history traffic is most likely to pass, as shown in fig. 7, "indicates the current vehicle history trajectory," indicates the real trajectory, the solid black line is a candidate trajectory set obtained by adjusting parameters ξ and η, η indicates the farthest point distance from the baseline line, and ξ indicates the distance from the initial point after the baseline projection.
3) And selecting an optimal track according to the motion model.
And calculating the average speed and the speed change rate alpha of the current speed of the target vehicle on the x axis and the y axis, and predicting the vehicle track by the vehicle according to a uniform speed change formula, wherein the track is shown by a dotted line in fig. 7.
And predicting the average minimum distance between the k track points and the candidate track by the motion model, and selecting the candidate track corresponding to the minimum distance as the current vehicle predicted track.
The method and the device make full use of the advantages of the road side sensing equipment, extract road modes and corresponding track clusters based on historical traffic flows, predict the driving modes of the current target vehicle, and restrict the speed and the acceleration of the vehicle through the candidate terminal areas by combining the driving modes and the candidate terminal areas of the corresponding track clusters, so that the vehicle reaches a better fitting state, and the final aim of predicting the vehicle track is fulfilled.
As shown in fig. 8, after the travel mode is obtained by the trajectory @, a constrained region point x is obtained by predicting based on the historical traffic flow in the mode, and an optimal trajectory in a candidate trajectory set is selected by predicting the trajectory through a motion model.
As shown in fig. 9, an embodiment of the present application further provides a trajectory determination device for a vehicle, including:
a first obtaining module 901, configured to obtain a position characteristic and a motion characteristic of a target vehicle;
a matching module 902, configured to match a target road mode from each preset road mode based on the location feature and the motion feature; the road mode is a mode obtained by preprocessing historical traffic data acquired by a road side camera and clustering the preprocessed historical traffic data;
a calculating module 903, configured to calculate an area constraint point based on a trajectory end of the target road mode;
a generating module 904, configured to generate a candidate trajectory set based on the region constraint point;
a determining module 905, configured to determine a target trajectory from the candidate trajectory set according to a motion model of the target vehicle.
In one embodiment, the trajectory determination device of the vehicle further includes:
the second acquisition module is used for acquiring historical video stream data in the perception range of each road side camera;
the third acquisition module is used for acquiring historical traffic flow data by using a detection tracking algorithm based on the historical video flow data;
the preprocessing module is used for preprocessing the historical traffic data to obtain preprocessed historical traffic data;
and the clustering module is used for clustering by utilizing the preprocessed historical traffic flow data to obtain each road mode.
In one embodiment, a pre-processing module comprises:
and the preprocessing unit is used for carrying out invalid short track filtering and track point time normalization on the historical traffic data to obtain the preprocessed historical traffic data.
In one embodiment, the clustering module includes:
the first calculation unit is used for calculating a track similarity matrix based on the preprocessed historical traffic flow data;
the second calculation unit is used for calculating the local density value and the inter-cluster distance of each track according to the track similarity matrix;
and the dividing unit is used for dividing based on the local density value and the inter-cluster distance of each track to obtain each road mode.
In one embodiment, the partitioning unit includes:
the acquisition subunit is used for acquiring a track weight dictionary according to the product value of the local density value and the inter-cluster distance;
and the dividing subunit is used for dividing according to the track weight dictionary and the acquired road environment information to obtain each road mode.
In one embodiment, the matching module 902 includes:
the third calculation unit is used for calculating the main track matching degree of the target vehicle and each road mode respectively based on the position characteristics and the motion characteristics;
and the first determining unit is used for determining the target road mode according to the matching degree of each main track.
In one embodiment, the determining module 905 includes:
a fourth calculation unit for calculating an average component speed and a speed change rate of the target vehicle in the first direction and the second direction, respectively; wherein the first direction is perpendicular to the second direction;
and the second determining unit is used for determining the target track from the candidate track set based on the average speed and the speed change rate.
Each module/unit in the apparatus shown in fig. 9 has a function of implementing each step in fig. 1, and can achieve the corresponding technical effect, and for brevity, no further description is provided herein.
Fig. 10 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
The electronic device may include a processor 1001 and a memory 1002 that stores computer program instructions.
Specifically, the processor 1001 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
In one example, the Memory 1002 may be a Read Only Memory (ROM). In one example, the ROM may be mask programmed ROM, programmable ROM (prom), erasable prom (eprom), electrically erasable prom (eeprom), electrically rewritable ROM (earom), or flash memory, or a combination of two or more of these.
The processor 1001 realizes the trajectory determination method of the vehicle in any one of the above embodiments by reading and executing computer program instructions stored in the memory 1002.
In one example, the electronic device may also include a communication interface 1003 and a bus 1010. As shown in fig. 10, the processor 1001, the memory 1002, and the communication interface 1003 are connected to each other via a bus 1010 to complete communication therebetween.
The communication interface 1003 is mainly used for implementing communication between modules, apparatuses, units and/or devices in this embodiment.
In addition, the embodiment of the application can be realized by providing a computer storage medium. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement a trajectory determination method of a vehicle as in any of the above embodiments.
It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations 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, 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, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.
Claims (10)
1. A method for determining a trajectory of a vehicle, comprising:
acquiring a position characteristic and a motion characteristic of a target vehicle;
matching a target road mode from each preset road mode based on the position characteristics and the motion characteristics; the road mode is a mode obtained by preprocessing historical traffic data acquired by a road side camera and clustering based on the preprocessed historical traffic data;
calculating a region constraint point based on the track end of the target road mode;
generating a candidate track set based on the region constraint points;
and determining a target track from the candidate track set according to the motion model of the target vehicle.
2. The method according to claim 1, wherein before the matching a target road pattern from among the preset road patterns based on the location feature and the motion feature, the method further comprises:
acquiring historical video stream data in the perception range of each road side camera;
based on the historical video stream data, obtaining the historical traffic stream data by using a detection tracking algorithm;
preprocessing the historical traffic data to obtain the preprocessed historical traffic data;
and clustering by using the preprocessed historical traffic data to obtain each road mode.
3. The method for determining the trajectory of the vehicle according to claim 2, wherein the preprocessing the historical traffic data to obtain the preprocessed historical traffic data includes:
and carrying out invalid short track filtering and track point time normalization on the historical traffic data to obtain the preprocessed historical traffic data.
4. The method according to claim 2, wherein the clustering by using the preprocessed historical traffic data to obtain each road pattern comprises:
calculating a track similarity matrix based on the preprocessed historical traffic flow data;
calculating the local density value and the inter-cluster distance of each track according to the track similarity matrix;
and dividing the local density value and the inter-cluster distance of each track to obtain each road mode.
5. The trajectory determination method of a vehicle according to claim 4, wherein the dividing based on the local density value and the inter-cluster distance of each trajectory to obtain each of the road patterns includes:
obtaining a track weight dictionary according to the product value of the local density value and the inter-cluster distance;
and dividing according to the track weight dictionary and the acquired road environment information to obtain each road mode.
6. The method according to claim 1, wherein the matching a target road pattern from among the preset road patterns based on the position feature and the motion feature comprises:
respectively calculating the main track matching degree of the target vehicle and each road mode based on the position characteristic and the motion characteristic;
and determining the target road mode according to the matching degree of each main track.
7. The method according to claim 1, wherein the determining a target trajectory from the set of candidate trajectories according to the motion model of the target vehicle comprises:
calculating the average speed and the speed change rate of the target vehicle in the first direction and the second direction respectively; wherein the first direction is perpendicular to the second direction;
determining the target trajectory from the set of candidate trajectories based on the average component velocity and the rate of change of velocity.
8. A trajectory determination device of a vehicle, characterized by comprising:
the first acquisition module is used for acquiring the position characteristic and the motion characteristic of the target vehicle;
the matching module is used for matching a target road mode from each preset road mode based on the position characteristics and the motion characteristics; the road mode is a mode obtained by preprocessing historical traffic data acquired by a road side camera and clustering based on the preprocessed historical traffic data;
a calculation module for calculating a region constraint point based on a trajectory end of the target road mode;
a generating module, configured to generate a candidate trajectory set based on the region constraint point;
and the determining module is used for determining a target track from the candidate track set according to the motion model of the target vehicle.
9. An electronic device, characterized in that the electronic device comprises: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a trajectory determination method of a vehicle as claimed in any one of claims 1 to 7.
10. A computer storage medium having computer program instructions stored thereon which, when executed by a processor, implement a trajectory determination method of a vehicle as claimed in any one of claims 1 to 7.
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