CN115081186B - Driving behavior simulation system supporting data driving and simulation method thereof - Google Patents
Driving behavior simulation system supporting data driving and simulation method thereof Download PDFInfo
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Abstract
The invention relates to a driving behavior simulation system supporting data driving and a simulation method thereof, wherein the system mainly comprises 3 modules: the universal kernel is developed based on the Opendrive road network data format and is used for constructing a wide traffic environment; the network model is memorized for a long time by using a mixed retraining constraint, namely, an SHRC-LSTM model, so that the integrated modeling of various driving behaviors is realized, and a high-precision track is simulated; and realizing the independence and connection of the driving behavior model and the general kernel by using one custom interface. Compared with the prior art, the invention realizes the simulation reproduction of traffic flow by providing a completely data-driven simulation system, can support the replacement of different scenes and different types of models, and improves the precision, flexibility and expansibility of microscopic traffic simulation.
Description
Technical Field
The invention relates to the technical field of microscopic traffic simulation, in particular to a driving behavior simulation system supporting data driving and a simulation method thereof.
Background
The microscopic traffic simulation technology can realize the multidimensional evaluation of road network traffic by simulating the driving behavior of the vehicle, provides effective support for traffic management, and can also be used for evaluating design schemes in a planning period. After about half a century of development, simulation systems are packaged as mature simulation software, and have gained wide attention and application in the industry. High simulation accuracy is an important guarantee of reliability of simulation results, and a simulation system is usually a complex structure integrated by multiple modules, wherein a driving behavior model is the most important module for influencing accuracy. So far, the traditional driving behavior model spatially decomposes the motion into two mutually noninterfere directions of a motion direction (longitudinal direction) and a vertical motion direction (transverse direction), and the motion is divided into various driving behaviors which are sequentially connected in time and are respectively controlled by different models; and the model needs to be calibrated and verified according to the observation data so as to ensure that the performance of the model accords with reality. However, the existing model greatly simplifies the actual behavior, and complicated calibration process is accompanied by errors which are difficult to overcome, so that the simulation precision is not advanced. On the other hand, the data driving method which is rapidly developed has great potential in various fields, and the data driving method is connected into a simulation system, so that the method is a feasible means for improving the simulation precision, and the application of the simulation technology in the industry is further promoted.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a driving behavior simulation system supporting data driving and a simulation method thereof, which aim to realize the access of a driving behavior model of data driving by adjusting the structure and connection of the existing simulation system so as to improve the precision and compatibility of the simulation system.
The invention designs a simulation system structure and operation logic, reconstructs a simulation environment by reading road network and traffic flow information, simulates vehicle motion by adopting a data driving model, and realizes the independence and connection between a driving behavior model and other modules.
The aim of the invention can be achieved by the following technical scheme:
the invention divides the whole simulation system into three parts (see figure 1): firstly, a general kernel is composed of a plurality of submodules such as input/output, infrastructure, traffic control, vehicle generation, road network and the like and is used for building a simulation environment; secondly, a driving behavior model driven by data, namely a long-short-time memory network model (the model is called Sim-Hybrid Retraining Constrained LSTM for short, SHRC-LSTM) of mixed retraining constraint, and a vehicle operation safety constraint is added in the model training process so as to realize safe behavior prediction; finally, the custom interface for connecting the two parts realizes the transmission of vehicle motion information and ensures the independence and connection of each module.
The method comprises the following steps:
the driving behavior simulation system supporting data driving comprises a general kernel, a driving behavior model driven by data and a customized interface, wherein the customized interface is used for connecting the general kernel and the driving behavior model driven by data to carry out data transmission;
the general kernel is used for constructing a traffic scene and generating a vehicle state and an environment state;
the data-driven driving behavior model is used for predicting the vehicle state of the next time step according to the time sequence data of the vehicle;
the customized interface is used for integrating the vehicle state and the environment state, constructing time sequence data and transmitting the time sequence data to a data-driven driving behavior model; and applying constraint to the predicted vehicle state of the next time step to obtain a re-revised vehicle state, and feeding back the re-revised vehicle state to the general kernel to update the traffic scene.
Further, the general purpose kernel includes:
the input module is used for initializing road network and vehicles and analyzing road network information;
the output module is used for outputting simulation statistical parameters and analysis results and deriving a vehicle motion trail;
the foundation setting module is used for configuring corresponding infrastructures for different road types;
the traffic control module is used for configuring a control scheme of the signal lamp;
the vehicle generation module is used for setting the entering position of the vehicle, the initial state of the vehicle and the entering interval of the vehicle according to the initialization information of the input module and generating the vehicle state according to the data of the traffic control module;
the road network module is used for constructing a road network according to the information of the basic setting module and the traffic control module and based on a preset road network format to generate an environment state;
and the vehicle control module is used for updating the vehicle state in real time according to the re-revised vehicle state and feeding back to the road network module and the output module.
Further, the road network module constructs a road network according to an OpenDRIVE road grid, wherein the road grid comprises road sections and nodes, the road sections are sets of one or more lanes with the same driving direction, the road sections are connected by the nodes, and one or more connectors are arranged in one node.
Further, the input of the data-driven driving behavior model comprises continuous running characteristics of the vehicle, interaction characteristics with surrounding vehicles, lane line information and traffic rules;
the model structure of the data-driven driving behavior model is a pre-constructed and trained SHRC-LSTM model;
the output of the data-driven driving behavior model is a two-dimensional position coordinate.
Further, the training process of the SHRC-LSTM model comprises the following steps:
the method comprises the steps of obtaining a training set of driving behaviors, dividing the training set into a data set D1 and a data set D2, firstly training a long-short-time memory network model by the data set D1, then inputting the data set D2 for prediction, checking and correcting inconsistent prediction results according to preset safety constraints, taking the corrected data set D2 as the data set D3, and integrating the data set D1 and the data set D3 to train the long-short-time memory network model continuously.
Further, the SHRC-LSTM model is a double-layer LSTM model.
Further, the safety constraints include having to exceed a maximum speed, having to be less than a minimum distance from the surrounding vehicle, and not reversing.
Further, the computational expression of the security constraint is:
in the method, in the process of the invention,for the predicted coordinates of instant i, Y i As the real coordinate of the moment i, deltav y For maximum speed, Δt is the predicted time interval, +.>Is the coordinates of the nearest car in the right direction, +.>For the coordinates of the nearest car in the left direction, +.>For the minimum distance between the vehicles in the right direction, < >>For the minimum distance between the vehicles in the left direction, < >>Is the predicted speed.
Further, constraints imposed by the customization interface on the predicted vehicle state for the next time step include a maximum speed constraint, a minimum spacing constraint, and a no-back constraint.
The invention also provides a simulation method supporting the driving behavior simulation system driven by data, which comprises the following steps:
s1: initializing a road network according to the input module and the road network module;
s2: updating the road network state through the road network model;
s3: generating a vehicle through a vehicle generation module according to the road network state;
s4: calculating the vehicle state;
s5: generating track coordinates of the vehicle according to the vehicle state, extracting surrounding vehicles and roads according to the road network state, obtaining a vehicle time sequence data set and integrating state information;
s6: predicting the vehicle track according to the integrated state information through a data-driven driving behavior model;
s7: adding a constraint to the vehicle track prediction result;
s8: updating the vehicle state;
s9: judging whether the vehicle reaches a travel terminal point, if so, deleting the vehicle, and if not, executing a step S10;
s10: generating a track record of the vehicle;
s11: judging whether the vehicle is the last vehicle at the current moment, if so, executing the step S12, otherwise, returning to the step S4;
s12: judging whether the preset simulation termination time is reached, if so, finishing the simulation, otherwise, returning to the step S2.
Compared with the prior art, the invention has the following advantages:
(1) The SHRC-LSTM model provided by the invention extracts space-time operation rules from track data, can realize high-precision vehicle track prediction, and improves simulation precision, thereby promoting the application of simulation to a certain extent.
(2) The invention is developed and adapted based on the open drive road grid, provides an adaptation algorithm of the road network and traffic flow model, is convenient for standardizing the road network structure, and is beneficial to establishing a universal simulation system tool.
(3) The general data driving simulation framework provided by the invention can support two forms of a data driving model and a driving behavior model, and the customized interface ensures the universality of the kernel structure of the system on one hand and can maintain the safety of vehicles in the system on the other hand.
Drawings
FIG. 1 is a schematic diagram of a system framework of a driving behavior simulation system supporting data driving according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of control logic of a driving behavior simulation system supporting data driving according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of an interactive vehicle inspection zone provided in an embodiment of the present invention;
fig. 4 is a schematic diagram of a SHRC-LSTM model structure and training provided in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
Example 1
As shown in fig. 1, the present embodiment provides a driving behavior simulation system supporting data driving, which includes a general kernel, a driving behavior model supporting data driving, and a customizing interface, where the customizing interface is used to connect the general kernel and the driving behavior model supporting data driving for data transmission;
the general kernel is used for constructing a traffic scene and generating a vehicle state and an environment state;
the data-driven driving behavior model is used for predicting the vehicle state of the next time step according to the time sequence data of the vehicle;
the customized interface is used for integrating the vehicle state and the environment state, constructing time sequence data and transmitting the time sequence data to the driving behavior model driven by the data; and the method is also used for applying constraint on the predicted vehicle state of the next time step and feeding back the re-revised vehicle state to the general kernel to update the traffic scene.
The following describes each portion in detail.
1. Establishing a generic kernel part
1.1, input/output module. The module mainly realizes the initialization of road network and vehicles. At the beginning of the operation of the simulation system, no simulation individuals exist in the system. The flow defined in the stage of initializing the road network needs to gradually enter the road network according to a certain rule, and three main problems are to be solved: the vehicle entry location, the vehicle initial state, and the vehicle entry interval will invoke the vehicle generation module implementation.
In addition, the input module also bears the work of road network information analysis, and the analyzed information is transferred to the relevant module. The OpenDRIVE format is a generic standard describing roads and road networks based on extensible markup language (XML). The data stored in the OpenDRIVE file describes the geometry of the road and the characteristics along the line and defines the underlying traffic signs and road infrastructure, such as lanes and lights.
The output module can output relevant statistical parameters and analysis results according to the requirements of the simulation scene; and after the motion trail of the vehicle is derived, generating a simulation animation.
1.2, infrastructure module. Different infrastructures will be considered for different road types. If the road is a highway, considering a service area connected with the entrance ramp; if the road is an urban road, taking a parking lot into consideration; and urban and rural roads consider road-bridge connection.
And 1.3, a traffic control module. The signal lamp timing scheme is mainly an adjustable signal lamp and a signal lamp timing scheme.
1.4, a vehicle generation module. The position of vehicle entry is controlled by the following parameters: { Global abscissa G x Global ordinate G y Link coding id Lane coding Lane id (where the road segments encode Link) id Lane coding Lane id Selectable according to a simulated coordinate system), parameters support both the designation and the random function generation. The initial state of the vehicle includes { velocity v, acceleration a, direction angle γ, vehicle type T }, in addition to the position p The above parameters haveThe method has randomness and correlation, and can be realized by adopting random functions according to requirements; vehicles enter the interval and are expressed by poisson distribution. Poisson distribution is suitable for the conditions that the traffic flow density is moderate or high, the interaction of arrival conditions among individuals is weak, and the arrival is random. Poisson distribution characterizes the distribution of the number of occurrences of an event over a period of time, an important property of which is that both the mathematical expectation and the variance are equal to the parameter λ. Let the number of vehicles arriving in a signal cycle be X, the possible values of X be 0,1,2, …, n. The probability of value is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
p (x=k) is the probability of reaching k vehicles in one signal period;
λ is the average arrival rate of the vehicle per cycle, and k may be 0,1,2, …, n.
Since the vehicle arrival follows a poisson distribution, the vehicle-generated time intervals follow a negative exponential distribution. The probability of a vehicle arriving at a time interval of at least t seconds is:
P(h≥t)＝e -λt
when the arrival interval is calculated, the motor vehicle can be controlled to enter the simulation road network at the arrival time conforming to the poisson distribution.
And 1.5, a road network module. The road network mainly comprises road sections and nodes. A road segment is a collection of one or more lanes traveling in the same direction, while the number of lanes within a road segment does not change. The road segments are connected by nodes, and a plurality of connectors may be arranged in one node. The link attribute includes { code link } id Type, lane Lane, front link be Rear link af Center line coordinates xy cen Coordinates xy of edge line si }；
Wherein the lane attributes include { encoding lane ] id Adjacent left lane le Adjacent right lane ri Lane dividing line attribute side, center line coordinate xy, side line coordinate xy si }。
1.6, a vehicle control module. The vehicle state is updated in real time according to the parameters calculated by the driving behavior model, and the parameters comprise { speed v, acceleration a, direction angle gamma and vehicle type T p Global abscissa G x Global ordinate G y -matching vehicles into road network, i.e. updating the following states { Lane) id Link of the road section id Global abscissa G x Global ordinate G y Map lane centerline position IN y }。
2. Establishing a data-driven driving behavior model
2.1 model input
The position is selected as a representative index of the driving characteristics of the vehicle, and a history track of continuous Li/s is selected as input. Assuming that the vehicle only interacts with the vehicle in the detection area, selecting the history track of the interactive vehicle with continuous Li/s as input, and indicating the interactive characteristics; here, a rectangular range surrounded by a lane where a vehicle is located and adjacent left and right lanes is defined as a detection area, and as shown in fig. 3, the nearest vehicles in six directions in the detection area are interactive vehicles, specifically, vehicles in left front, right front, left rear, rear and right rear positions. And finally, calculating the position of the current vehicle speed for n/s, and taking the coordinate positions mapped to the lane lines at two sides as input to indicate the influence of the lane lines. To sum up, the inputs at each moment are:
wherein x is t For the input of the time points of the model,for two-dimensional coordinate positions, t indicates time, ind indicates different vehicles, and i is a time interval;
the model will last L i At/s, the historical track with the time interval of at is taken as input, and then the model is input X t The method comprises the following steps:
2.2 model Structure
Selecting a Long Short-Term Memory network model (LSTM) as a basic structure, wherein each unit of the LSTM network is a Memory block with three self-parameterized control gates: an input gate, a forget gate and an output gate, the model adds or removes information to the cell state through the fine structure of the three "gates".
In order to reduce the result against the conventional rule generated in the practical application, the invention adds the position and speed constraint into the model design, proposes a SHRC-LSTM model, as shown in figure 4, divides the training set into two data sets D1 and D2, and firstly inputs the data set D1 to train the model; after training, inputting a data set D2 for prediction, sequentially checking and correcting inconsistent prediction results according to three bundles, and enabling the corrected data set D2 to be called a data set D3; finally, the data sets D1 and D3 are integrated to serve as training sets of the model, and the model is continuously trained.
The three conditions to be corrected are: must not exceed maximum speed, must not be less than minimum distance from surrounding vehicles, and must not reverse:
wherein:
Y i predicting coordinates and real coordinates for the moment i;
Δv y at maximum speedA degree;
Δt is the predicted time interval;
coordinates of nearest vehicles in each direction of right (front) and left (back);
the minimum vehicle distance in each direction of right (front) and left (rear);
is the predicted speed.
The main structure of the model adopts a double-layer LSTM model, the number of neurons in a hidden layer is 250, a Mean Square Error (MSE) is selected as a loss function, a tanh is adopted as an activation function, adam is adopted as an optimizer, batch size (Batch size) is selected to be 32, and a shaping period (Epoch) is selected to be 50.
2.3 model output
The future position of the predicted vehicle with the output time interval Δt of the model is as follows:
3. establishing customized interface to realize model access
The information to be transferred from the general kernel to the driving behavior model specifically comprises: the current vehicle speed predicts 4s lane boundary coordinates for the historical track of the host vehicle and the historical track of 6 interactive vehicles with the duration of 8 s.
Note that all vehicle information is integrated and transferred, i.e. each simulation step is transferred only once.
The information to be transferred from the driving behavior model to the general kernel specifically includes: corrected model predicted trajectory
The three correction conditions are respectively: maximum speed, minimum pitch, no backing:
wherein:
Y i predicting coordinates and real coordinates for the moment i;
Δv y is the maximum speed;
Δt is the predicted time interval;
coordinates of nearest vehicles in each direction of right (front) and left (back);
the minimum vehicle distance in each direction of right (front) and left (rear);
predicting a speed;
4. universal framework and control logic for simulation system
As shown in fig. 2, the simulation starts from initializing the road network and the vehicle, and a simulation environment is constructed; the outer circulation is simulation time, inner circulation is carried out on all vehicles at each moment, calculation of motion state is carried out on each vehicle, and after safety constraint inspection, the vehicles are updated into the road network. And after the calculation of the last vehicle in the road network is finished, adding 1 to the simulation step length, and continuing the simulation. The mark deleted from the road network by the vehicle is the trip end point, and the mark of the simulation end is the simulation end time.
The specific process is as follows:
s1: initializing a road network according to the input module and the road network module;
s2: updating the road network state through the road network model;
s3: generating a vehicle through a vehicle generation module according to the road network state;
s4: calculating the vehicle state;
s5: generating track coordinates of the vehicle according to the vehicle state, extracting surrounding vehicles and roads according to the road network state, obtaining a vehicle time sequence data set and integrating state information;
s6: predicting the vehicle track according to the integrated state information through a data-driven driving behavior model;
s7: adding a constraint to the vehicle track prediction result;
s8: updating the vehicle state;
s9: judging whether the vehicle reaches a travel terminal point, if so, deleting the vehicle, and if not, executing a step S10;
s10: generating a track record of the vehicle;
s11: judging whether the vehicle is the last vehicle at the current moment, if so, executing the step S12, otherwise, returning to the step S4;
s12: judging whether the preset simulation termination time is reached, if so, finishing the simulation, otherwise, returning to the step S2.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (7)
1. The driving behavior simulation system supporting data driving is characterized by comprising a general kernel, a driving behavior model driven by data and a customized interface, wherein the customized interface is used for connecting the general kernel and the driving behavior model driven by data for data transmission;
the general kernel is used for constructing a traffic scene and generating a vehicle state and an environment state;
the data-driven driving behavior model is used for predicting the vehicle state of the next time step according to the time sequence data of the vehicle;
the customized interface is used for integrating the vehicle state and the environment state, constructing time sequence data and transmitting the time sequence data to a data-driven driving behavior model; constraint is applied to the predicted vehicle state of the next time step, so that a re-revised vehicle state is obtained, and the re-revised vehicle state is fed back to the general kernel to update the traffic scene;
the general kernel includes:
the input module is used for initializing road network and vehicles and analyzing road network information;
the output module is used for outputting simulation statistical parameters and analysis results and deriving a vehicle motion trail;
the foundation setting module is used for configuring corresponding infrastructures for different road types;
the traffic control module is used for configuring a control scheme of the signal lamp;
the vehicle generation module is used for setting the entering position of the vehicle, the initial state of the vehicle and the entering interval of the vehicle according to the initialization information of the input module and generating the vehicle state according to the data of the traffic control module;
the road network module is used for constructing a road network according to the information of the basic setting module and the traffic control module and based on a preset road network format to generate an environment state;
the vehicle control module is used for updating the vehicle state in real time according to the re-revised vehicle state and feeding back the vehicle state to the road network module and the output module;
the data-driven driving behavior model input comprises continuous running characteristics of the vehicle, interaction characteristics of the vehicle and surrounding vehicles, lane line information and traffic rules;
the model structure of the data-driven driving behavior model is a pre-constructed and trained SHRC-LSTM model;
the output of the data-driven driving behavior model is a two-dimensional position coordinate;
constraints imposed by the custom interface on the predicted vehicle state for the next time step include a maximum speed constraint, a minimum spacing constraint, and a no-back constraint.
2. A driving behavior simulation system supporting data driving according to claim 1, wherein the road network module constructs the road network according to an OpenDRIVE road grid, the road grid comprising road segments and nodes, the road segments being a set of one or more lanes with the same driving direction, the road segments being connected by nodes, one or more connectors being present in one node.
3. A driving behavior simulation system supporting data driving according to claim 1, wherein the training process of the SHRC-LSTM model comprises:
the method comprises the steps of obtaining a training set of driving behaviors, dividing the training set into a data set D1 and a data set D2, firstly training a long-short-time memory network model by the data set D1, then inputting the data set D2 for prediction, checking and correcting inconsistent prediction results according to preset safety constraints, taking the corrected data set D2 as the data set D3, and integrating the data set D1 and the data set D3 to train the long-short-time memory network model continuously.
4. A driving behavior simulation system supporting data driving according to claim 3, wherein the SHRC-LSTM model is a double-layer LSTM model.
5. A driving behavior simulation system supporting data driving according to claim 3, wherein the safety constraints include that maximum speed must not be exceeded, that distance from surrounding vehicles must not be less than minimum distance and that no reverse is possible.
6. A driving behavior simulation system supporting data driving according to claim 5, wherein the computational expression of the safety constraint is:
in the method, in the process of the invention,for the predicted coordinates of instant i, Y i As the real coordinate of the moment i, deltav y For maximum speed, Δt is the predicted time interval, +.>Is the coordinate of the nearest car in the right direction, Y l i For the coordinates of the nearest car in the left direction, +.>For the minimum distance between the vehicles in the right direction, < >>For the minimum distance between the vehicles in the left direction, < >>Is the predicted speed.
7. A simulation method supporting a data-driven driving behavior simulation system according to claim 1, comprising the steps of:
s1: initializing a road network according to the input module and the road network module;
s2: updating the road network state through the road network model;
s3: generating a vehicle through a vehicle generation module according to the road network state;
s4: calculating the vehicle state;
s5: generating track coordinates of the vehicle according to the vehicle state, extracting surrounding vehicles and roads according to the road network state, obtaining a vehicle time sequence data set and integrating state information;
s6: predicting the vehicle track according to the integrated state information through a data-driven driving behavior model;
s7: adding a constraint to the vehicle track prediction result;
s8: updating the vehicle state;
s9: judging whether the vehicle reaches a travel terminal point, if so, deleting the vehicle, and if not, executing a step S10;
s10: generating a track record of the vehicle;
s11: judging whether the vehicle is the last vehicle at the current moment, if so, executing the step S12, otherwise, returning to the step S4;
s12: judging whether the preset simulation termination time is reached, if so, finishing the simulation, otherwise, returning to the step S2.
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