CN112686281A - Vehicle track prediction method based on space-time attention and multi-stage LSTM information expression - Google Patents
Vehicle track prediction method based on space-time attention and multi-stage LSTM information expression Download PDFInfo
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Abstract
The invention discloses a vehicle track prediction method based on space-time attention and multi-stage LSTM information expression. The method comprises the following steps: acquiring vehicle running track information and space-time map information of road conditions where the vehicle runs, wherein the space-time map information is used for representing position information of the vehicle and surrounding targets in a preset time period and a spatial relation between the vehicle and the surrounding targets along with the time; and inputting the vehicle running track information and the space-time diagram information into a track prediction model to obtain a predicted running track of the vehicle at a subsequent moment, wherein the track prediction model is obtained by pre-training sample space-time diagram information based on a plurality of sample vehicles in the same time period and sample running tracks corresponding to the sample vehicles respectively, and the track prediction model comprises a multistage LSTM codec and a space-time attention module. The invention improves the track prediction accuracy and the robustness under the complex environment, so that the vehicle track prediction can be better used for unmanned driving.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to a vehicle track prediction method based on space-time attention and multi-level LSTM information expression.
Background
Vehicle trajectory prediction aims to let the vehicle know how to move next. If the track is accurately predicted, the vehicle can reasonably and effectively infer the future motion of the surrounding vehicle, and make the best planning decision for the driving operation of the vehicle. At present, vehicle track prediction focuses on solving extraction and fusion of interaction information (interaction of data in time sequence and space and semantic relation) between a target vehicle and surrounding vehicles, but the moving track of an object in an actual scene changes due to mutual influence among various targets, so that an independent prediction method simply considering the historical track of the object is difficult to cope with influence caused by association among multiple targets.
The existing vehicle track prediction methods mainly comprise the following three methods:
(1) many of the most advanced LSTM-based trajectory prediction methods model the non-linear temporal dependencies in sequence learning and generation tasks. Are often used for speech recognition and text analysis. While LSTM has the ability to learn and copy a vehicle's historical track, it cannot capture the dependencies between multiple related historical tracks. Therefore, the accuracy of trajectory prediction using LSTM alone is not high.
(2) The Social-LSTM based approach, which processes each track in a scene separately by multiple LSTMs. The LSTMs are then interconnected by Social-LSTM. Unlike conventional LSTM, this pooling layer allows spatially close LSTM to share information with each other, and for one pooling layer of target objects, the hidden states of all LSTM within a certain radius are aggregated together and used as input in the next time step. But this method is not high in capturing capability of mutual information between time series data.
(3) Deep learning approaches attempt to learn data representations from raw data through a multi-layer neural network, learning these features during the course of training. Research has shown that the Convolutional Neural Network (CNN) based method works better than the partial LSTM method, but the Convolutional Neural Network (CNN) belongs to a local extraction feature, i.e., a feature at a short distance. Such methods have insufficient spatial information-interactive capabilities to capture data.
It has been analyzed that in practical situations, autonomously driven vehicles are less likely to be present alone on a road, but are surrounded by vehicles that are not fixed in their surroundings. Existing vehicle trajectory prediction methods typically only consider the state of the vehicle itself and the navigation requirements, and do not fully capture the problem of relevant interactivity between the vehicle and surrounding vehicles. Furthermore, existing research also tends to ignore the correlation between temporal and spatial information.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a vehicle track prediction method based on space-time attention and multi-level LSTM information expression.
The technical scheme of the invention is as follows: a vehicle trajectory prediction method based on spatiotemporal attention and multi-level LSTM information expression is provided. The method comprises the following steps:
acquiring vehicle running track information and space-time map information of road conditions where the vehicle runs, wherein the space-time map information is used for representing position information of the vehicle and surrounding targets in a preset time period and a spatial relation between the vehicle and the surrounding targets along with the time;
and inputting the vehicle running track information and the space-time diagram information into a track prediction model to obtain a predicted running track of the vehicle at a subsequent moment, wherein the track prediction model is obtained by pre-training sample space-time diagram information based on a plurality of sample vehicles in the same time period and sample running tracks corresponding to the sample vehicles respectively, and the track prediction model comprises a multistage LSTM codec and a space-time attention module.
In one embodiment, the space-time diagram information is a dynamic space-time diagram represented by a diagram structure, and is labeled G ═ V, Σ S, Σ T, where V is a set of nodes, Σ S is a set of spatial edges, Σ T is a set of temporal edges, node V represents each individual target, spatial edges represent two inter-node relationships to reflect the interaction between the targets, and temporal edges connect the same node in consecutive time steps to represent a connected graph over time.
In one embodiment, each stage of the multi-stage LSTM codec respectively corresponds to a time edge LSTM for capturing position information between nodes; the space edge LSTM is used for capturing relative space distance information between the node and the neighbor node along with the time; the node LSTM is used for capturing the position information of each node changing along with the time; the spatiotemporal attention module is configured to perform spatial attention weighting and temporal attention weighting.
In one embodiment, the spatiotemporal attention module cascades and averages all edge features of each node with soft attention and implements the spatial domain features and the temporal domain features of common attention with a single layer.
In one embodiment, the sample data set is constructed according to the following steps:
obtaining sample running tracks corresponding to a plurality of sample vehicles in a preset time period;
and processing the sample track data of the plurality of sample vehicles to obtain corresponding sample space-time diagram information, wherein the sample space-time diagram information comprises a sample space-time diagram corresponding to each moment arranged in time sequence in the same time period.
In one embodiment, the sample data set consists of trajectories of real road traffic, covering light, medium and congested road conditions.
Compared with the prior art, the vehicle trajectory prediction method based on the space-time attention mechanism and the multi-stage LSTM information expression and association has the advantages that the space-time attention mechanism and the multi-stage LSTM information expression and association are fully combined, the trajectory prediction accuracy and the robustness in a complex environment are improved, and the vehicle trajectory prediction can be better used for unmanned driving.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram of a vehicle trajectory prediction method based on spatiotemporal attention and multi-level LSTM information expression in accordance with one embodiment of the present invention;
FIG. 2 is an overall architecture diagram based on a spatiotemporal attention model and multi-stage LSTM trajectory prediction, according to one embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Aiming at the problem that the correlation interactivity between a vehicle and surrounding vehicles is difficult to be comprehensively captured by the current vehicle track prediction and the problem that the correlation between time and space is insufficient in the existing research, the invention provides a vehicle track prediction method based on the information representation and the correlation prediction of space-time attention and multi-stage long-short term memory (LSTM). In short, the invention comprehensively considers the space positions and the moving tracks of the obstacles and the vehicles, constructs a graph convolution network model based on a space-time attention mechanism, designs a coder-decoder based on an LSTM algorithm as a main part, analyzes the moving track of the obstacles, and combines the historical track of the obstacles and the interaction relation between each object and the vehicles to achieve the effect of accurately predicting different tracks. Because the space-time attention mechanism can simultaneously represent the time and space reasoning activities of the area needing attention and the node (node) sharing parameters, excessive parameters are avoided, and the prediction precision and the running speed are improved.
Specifically, referring to fig. 1, the vehicle trajectory prediction method provided by this embodiment includes the following steps:
step S110, a sample data set is constructed based on vehicle running track information and the space-time diagram information of the road condition where the vehicle runs, wherein the space-time diagram information is used for representing the position information of the vehicle and the surrounding targets in a preset time period and the spatial relationship of the vehicle and the surrounding targets along with the time.
The invention takes the starting point that the prediction of the running track of a target vehicle is obtained by training a track prediction model based on the space-time diagram information and the corresponding motion information among multiple targets, and preferably takes the prediction model combining a space-time attention mechanism and a multi-stage LSTM codec as an example for explanation.
For example, a trajectory prediction model is trained and validated on a sample data set consisting of trajectories of real highway traffic. Specifically, each data set was captured at a frequency of 10hz within 45 minutes. Each data set had 15 minutes of light, medium and congested traffic conditions. The data set provides vehicle coordinates projected to a local coordinate system, dividing the entire data set into a training set and a test set. The test set is from 3 subsets of the US-101 and I-80 data sets, and one-fourth of the trace data is selected from each subset as the test set. In one embodiment, the edge set and the point set are semantically divided based on the two data sets, so that the data sets are constructed into a space-time diagram, and then the characteristics are extracted through a network model, and finally the trajectory prediction of the vehicle is realized.
In the description herein, trajectory data is data sampled from the course of motion of a moving object (including vehicles and obstacles) and is used to characterize the position, time, speed of motion, etc. of the moving object. The motion tracks are obtained by sequencing according to the running time sequence and can be used for reflecting the motion trends of time dimension and space dimension.
Step S120, training a track prediction model by using the sample data set, wherein the track prediction model comprises a multi-stage LSTM codec and a space-time attention module.
In order to predict the multi-target future motion trajectory around the moving vehicle more accurately, the importance of the interaction among various targets needs to be clarified, and the more influential sources need to be paid attention to. The preferred embodiment of the invention estimates the corresponding track by adopting a mode of combining a space-time attention mechanism and a multi-stage LSTM codec based on the space-time diagram information and the corresponding motion information among multiple targets, and the overall frame diagram is shown in figure 2.
First, vehicle motion information obtained during trajectory tracking is directly used as an input to the network architecture. And converting the obtained data into space-time diagram information. For example, a data format for processing data in an image coordinate system into a dynamic space-time diagram is represented by G ═ V, Σ S, Σ T, where V represents a node set V, Σ S represents a spatial edge set, and Σ T represents a temporal edge set. Node V represents each individual target (e.g., vehicle or other obstacle); the spatial edge represents the relationship between two nodes, namely, the relationship is used for reflecting the interaction between the nodes; the time edges then connect the same node in successive time steps, representing the connected graph over time.
With reference to fig. 2, a multi-stage LSTM codec is constructed, each stage corresponding to a temporal edge LSTM, a spatial edge LSTM and a node LSTM, respectively. The relation between two nodes being characterised by their relative coordinates, e.g. XV2V3Representing the spatial distance, X, between nodes v2 and v3V2V2Is node V2 that changes over timeLocation. Defining a spatial edge LSTM embedding function phi whose input is
And embedding the weight matrix WsExpressed as:
wherein
Representing node XV2All relative spatial distances to its neighbors (e.g., including X)V2V3)。
Spatial edge LSTM obtains embedded input features and previous spatial hidden states from all relevant nodes
And using normally initialized weight matrices
They are converted. Outputting hidden state vectors
Expressed as:
then, a time-edge LSTM embedding function φ is defined, which is based on the time position of the node
And embedding the weight matrix WtAs input, expressed as:
for an LSTM cell and its inputs: previous temporal hidden state
Embedded input features
Normally initialized transition of these inputs to the current hidden state
And then, respectively inputting the space-time diagram information and the attitude information of each object into corresponding multistage LSTM codecs in sequence, and realizing the dependency prediction through a space-time attention module.
For example, for the spatiotemporal attention module, a variant of multi-attention is employed, with soft attention initially set to two simple operations, cascading and averaging all the edge features of each node. Furthermore, a multi-node note is created that has only a single layer, collectively notes the features of the spatial and temporal domains, and stores the note coefficients into a single vector for each time step of node v 2.
Next, the object position coordinates are determined
By embedding the layer phi, which is then used as an input node to the LSTM, a vector is output
With the previous hidden state
Connected and then passed to node V2, and the weight matrix W is transformedlstmTo generate a current hidden state
And finally estimating the future position of the target from the binary normal distribution N.
In summary, the multi-stage LSTM codec can capture the time-series positional relationship of each individual moving vehicle, and also can capture the relative positional relationship between vehicles, so that the dynamic interaction situation between vehicles or between a vehicle and other obstacles can be better analyzed. And, by combining the spatiotemporal attention module (or component), the corresponding attention can be calculated for each stage of the LSTM module, thereby improving the accuracy, flexibility and applicability of the prediction.
Step S130, inputting the vehicle running track information acquired in real time and the time-space diagram information of the road condition where the vehicle runs into the trained track prediction model to obtain the predicted running track of the vehicle at the subsequent moment.
After the training is completed, in a prediction stage, the vehicle running track information obtained in real time and the corresponding space-time diagram information are used as input, and the predicted vehicle running track can be obtained, wherein the data obtaining process is similar to that in the training stage and is not repeated herein.
In conclusion, the data structure obtained by utilizing the space-time attention mechanism is ingenious in form, the relation between points and edges in the graph structure is effectively utilized, space-time information is represented and predicted through the multi-stage LSTM module, and a reasonable structural framework is combined, so that training parameters are reduced, the accuracy of vehicle trajectory prediction and the flexibility of a model are improved, and the robustness of the model is enhanced. Compared with the prior art, the method has remarkable advantages in the aspects of efficiency and robustness.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each 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-readable program instructions.
These computer-readable 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, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted 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-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (8)
1. A vehicle track prediction method based on space-time attention and multi-level LSTM information expression comprises the following steps:
acquiring vehicle running track information and space-time map information of road conditions where the vehicle runs, wherein the space-time map information is used for representing position information of the vehicle and surrounding targets in a preset time period and a spatial relation between the vehicle and the surrounding targets along with the time;
and inputting the vehicle running track information and the space-time diagram information into a track prediction model to obtain a predicted running track of the vehicle at a subsequent moment, wherein the track prediction model is obtained by pre-training sample space-time diagram information based on a plurality of sample vehicles in the same time period and sample running tracks corresponding to the sample vehicles respectively, and the track prediction model comprises a multistage LSTM codec and a space-time attention module.
2. The method of claim 1, wherein the space-time graph information is a dynamic space-time graph represented in a graph structure, denoted G ═ V, Σ S, Σ T, where V is a set of nodes, Σ S is a set of spatial edges, Σ T is a set of temporal edges, node V represents each individual target, spatial edges represent two inter-node relationships to reflect interactions between targets, and temporal edges connect the same node in consecutive time steps to represent a connected graph over time.
3. The method of claim 2, wherein each stage of the multi-stage LSTM codec respectively corresponds to a time edge LSTM for capturing position information between nodes; the space edge LSTM is used for capturing relative space distance information between the node and the neighbor node along with the time; the node LSTM is used for capturing the position information of each node changing along with the time; the spatiotemporal attention module is configured to perform spatial attention weighting and temporal attention weighting.
4. The method of claim 2, wherein the spatiotemporal attention module cascades and averages all edge features of each node with soft attention and implements the spatial domain features and the temporal domain features of common attention with a single layer.
5. The method of claim 1, wherein the sample data set is constructed according to the following steps:
obtaining sample running tracks corresponding to a plurality of sample vehicles in a preset time period;
and processing the sample track data of the plurality of sample vehicles to obtain corresponding sample space-time diagram information, wherein the sample space-time diagram information comprises a sample space-time diagram corresponding to each moment arranged in time sequence in the same time period.
6. The method of claim 1, wherein the sample data set consists of trajectories of real road traffic, covering light, medium and congested road conditions.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
8. A computer device comprising a memory and a processor, on which memory a computer program is stored which is executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 6 when executing the program.
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