CN113516846B - Vehicle lane change behavior prediction model construction, prediction and early warning method and system - Google Patents

Vehicle lane change behavior prediction model construction, prediction and early warning method and system Download PDF

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CN113516846B
CN113516846B CN202110702854.4A CN202110702854A CN113516846B CN 113516846 B CN113516846 B CN 113516846B CN 202110702854 A CN202110702854 A CN 202110702854A CN 113516846 B CN113516846 B CN 113516846B
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惠飞
贾硕
魏诚
房山
靳少杰
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Abstract

The invention discloses a method and a system for constructing, predicting and early warning a vehicle lane change behavior prediction model, which utilize a vehicle-mounted terminal to acquire vehicle running state data in real time and carry out data communication among vehicles; by combining game theory and deep learning technology, dynamic interaction of different vehicles in the driving process is obtained from data, information of the driving environment around the vehicles is analyzed, whether the current environment is suitable for lane changing of the vehicles is judged by using the game theory, lane changing intention of a driver is further quantized, and running data of the vehicles are identified and predicted by using a deep learning algorithm.

Description

Vehicle lane change behavior prediction model construction, prediction and early warning method and system
Technical Field
The invention belongs to the technical field of auxiliary driving, and relates to a method and a system for building and predicting and early warning a vehicle lane change behavior prediction model, in particular to a method for predicting a vehicle lane change behavior by combining a game theory and a deep learning algorithm, and modules for data acquisition, workshop communication, warning display and the like required by an early warning system and required by the prediction method.
Background
The driving behavior of drivers is closely related to road traffic safety, and most of traffic accidents are caused by improper driving operation. Of all the operations by the driver, the lateral operations (right and left lane changes and turning) of the vehicle have the greatest influence on the stability of the traffic flow, and cause the most accidents. The existing Advanced Driver Assistance System (ADAS) integrates a Blind-Spot-Warning System (Blind-Spot-Warning System) and a Lane Departure Warning System (Lane Departure Warning System), which can reduce the probability of accidents caused by transverse operation to a certain extent, however, the normal operation of these Systems depends on the Driver to correctly use a turn signal, and in the actual life, some drivers do not use the turn signal to warn surrounding vehicles and passersby before changing lanes or turning lanes, so the functions of these Systems are limited, and the problem of frequent accidents caused by transverse operation cannot be solved. Therefore, the mathematical model is used for simulating the perception of the driver to the surrounding driving environment and the expected driving state in the driving process, and the vehicle running data collected by the vehicle-mounted terminal is used for accurately and efficiently predicting the lateral operation such as lane change to be carried out by the driver, so that the purpose of warning the vehicles which are possibly affected around before the vehicle motion state is changed can be achieved, the occurrence of accidents is reduced, and the road traffic safety is guaranteed.
Although recent research starts to identify and predict driving behaviors, on one hand, data sets used in some research are historical data collected in advance, so that the constructed algorithm framework can only be used for identifying driving operations which have already occurred, and can not be used for predicting the driving behaviors of a driver in the future in real time; on one hand, a mathematical model (a hidden Markov model, a game theory model and the like) is used for predicting real-time lane change intention at present, although the potential possibility can be predicted before the lane change action occurs, the prediction mode can only draw the conclusion that the current driving environment is suitable for the lane change of the vehicle, and whether the driver performs according to the intention is unknown, so that the prediction accuracy is difficult to guarantee; on the other hand, the deep learning algorithm has unique advantages in predicting continuous time sequence data similar to driving behavior data, and can achieve an accurate prediction effect through a large number of historical data segment training algorithm models, but the algorithm can often obtain accurate prediction accuracy within a period of time after the lane changing behavior starts, the real-time performance of behavior prediction is difficult to guarantee, and in practical traffic application, the lane changing behavior of a vehicle is predicted and surrounding vehicles and people are warned every 0.1 second in advance, so that accidents can be reduced to a great extent.
Disclosure of Invention
Aiming at immaturity of the prior art, the invention provides a method and a system for building and predicting and early warning a vehicle lane changing behavior prediction model, which take the viewpoints of real-time performance and accuracy of a driver in the lane changing behavior prediction process into consideration, reduce the length of time sequence data required by an algorithm to obtain a correct prediction result on the premise of ensuring higher prediction accuracy, namely lower misjudgment rate, namely obtain the correct prediction result in a shorter time, and send early warning information to surrounding vehicles in time, and aim to solve the problem of predicting the lane changing behavior of the driver.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for constructing a prediction model of vehicle lane change behavior, which is used for predicting the vehicle lane change behavior, comprises the following steps:
step 1, collecting vehicle running state data:
the system comprises a vehicle-mounted terminal, a communication module, a display module and a CAN bus module, wherein the vehicle-mounted terminal is arranged in each vehicle and integrates a CAN bus module to acquire vehicle running data, a GPS module to acquire positioning data of the vehicles and the communication module to realize data communication among the vehicles and the display module to display the acquired vehicle running state data; the vehicle travel data includes a speed, an acceleration, and a steering angle of the vehicle;
step 2, data communication between vehicles:
each vehicle sends the vehicle running state data to other vehicles through the communication module of the vehicle, receives the vehicle running state data of other vehicles, and randomly selects one vehicle as a target vehicle;
step 3, obtaining lane change intention probability of the lane change behavior of the target vehicle:
after receiving vehicle running state data of other vehicles, the target vehicle establishes a game matrix by combining the vehicle running state data of the target vehicle and utilizing a game theory, and then solves the game matrix to obtain the lane change intention probability of the target vehicle;
and 4, establishing a deep learning algorithm model, taking the vehicle driving data and the lane change intention probability as driving sample data in the lane change process of the vehicle, inputting a training sample set consisting of the driving sample data into the deep learning algorithm model, taking whether the vehicle is subjected to lane change as the output of the deep learning algorithm model, training the deep learning algorithm model, and taking the trained deep learning algorithm model as a vehicle lane change behavior prediction model.
The invention also comprises the following technical characteristics:
specifically, in the step 3, a game matrix is established by using a game theory, and when the target vehicle is influenced by a front vehicle with a slow speed, the strategy of the target vehicle comprises lane changing or lane changing-free; meanwhile, the target vehicle can influence the running of the rear vehicle close to the lane, so that the rear vehicle can give way or not give way by two strategies; the gaming matrix of the target vehicle and the rear workshop therefore includes the following:
the lane change is required for the target vehicle, and under the condition that the rear vehicle gives way to the target vehicle: target vehicle revenue is U SVC The benefit of the rear vehicle is U V1A ；
The lane change is required for the target vehicle, and the rear vehicle does not give way to the target vehicle: target vehicle revenue is U SVC The benefit of the rear vehicle is U V1R ；
And when the target vehicle does not require lane changing and the rear vehicle gives way to the target vehicle: target vehicle revenue is U SVS The benefit of the rear vehicle is U V1A ；
The target vehicle does not require lane changing, and the rear vehicle does not give way to the target vehicle: target vehicle revenue is U SVS Then the rear vehicle is taken upBenefit as U V1R 。
Specifically, each profit function under different strategies is respectively a safety profit U safety And space profit U space The composition is as follows:
U＝αU safety +βU space wherein β is a Sigmoid function, α =1- β;
wherein:
Figure BDA0003130840890000031
Figure BDA0003130840890000032
in the above formula, T (T) is the headway at time T, T min (t) is the minimum safe headway, D (t) is the distance to the front vehicle, D min (t) is the minimum safe distance.
Specifically, the minimum safe distance between the target vehicle and the rear vehicle is as follows:
Figure BDA0003130840890000033
wherein, a V1 For rear vehicle acceleration, a sv Is a target vehicle acceleration, v v1 Is the rear vehicle speed, v sv Is a target vehicle speed;
minimum safe headway of target vehicle and back car:
T min (t)＝min(T initial ,T a )
wherein, T initial The time interval between the rear vehicle and the front vehicle at the time T, T a Is a fixed value and is set to 3.
Specifically, in the step 3, solving the game matrix to obtain the lane change intention probability of the target vehicle includes:
firstly, the target vehicle judges the strategy of the maximum profit of the target vehicle according to the game matrix, namely lane changing or lane not changing, then the rear vehicle selects the reaction of the maximum profit of the target vehicle under the current condition according to the decision of the target vehicle, namely lane giving or lane not giving, and then the reaction is fed back to the target vehicle, and the circulation is continued until both game parties are satisfied, and the final balanced state is reached;
according to the game matrix, under the balanced state, the decision profit of the target vehicle is U sv And the other decision benefit is U sv* And if the quantifiable game result is the lane change probability:
Figure BDA0003130840890000041
wherein Pro (t) is the game result and is also the lane change probability of the target vehicle.
Specifically, the deep learning algorithm model consists of a long-short term memory network LSTM and a convolutional neural network CNN; in the deep learning algorithm model, the number of nodes of the hidden layer is set to be 100, the learning rate is 0.001, the step length is 8, and in the CNN, the convolution kernel is a 3 × 3 matrix and the convolution step length is 1.
A vehicle lane change behavior prediction early warning method comprises the following steps:
step one, collecting vehicle driving state data:
the system comprises a vehicle-mounted terminal, a communication module, a display module and a CAN bus module, wherein the vehicle-mounted terminal is arranged in each vehicle and integrates a CAN bus module to acquire vehicle running data, a GPS module to acquire positioning data of the vehicles and the communication module to realize data communication among the vehicles and the display module to display the acquired vehicle running state data; the vehicle travel data includes a speed, an acceleration, and a steering angle of the vehicle;
step two, data communication between vehicles:
each vehicle transmits its vehicle driving state data to other vehicles through its communication module and receives the vehicle driving state data of the other vehicles;
step three, obtaining lane change intention probability of the lane change behavior of the target vehicle by the method in the step 3 in the vehicle lane change behavior prediction model construction method;
step four, taking the driving state data of the target vehicle and the lane change intention probability as input, importing the driving state data and the lane change intention probability into the trained deep learning algorithm model, and predicting whether the target vehicle changes the lane or not; and if the target vehicle is predicted to change the lane, displaying early warning information on vehicle-mounted terminals of other vehicles.
The utility model provides a vehicle lane change behavior prediction early warning system which characterized in that includes:
the vehicle running state data acquisition module comprises a vehicle-mounted terminal arranged in each vehicle, wherein the vehicle-mounted terminal integrates a CAN bus module to acquire vehicle running data, a GPS module to acquire positioning data of the vehicles, and a communication module to realize data communication between the vehicles and a display module to display the acquired vehicle running state data;
the inter-vehicle data communication module is used for transmitting the vehicle driving state data of each vehicle to other vehicles and receiving the vehicle driving state data of other vehicles;
the lane change intention probability acquisition module is used for establishing a game matrix by combining the driving state data of the vehicle after the target vehicle receives the driving state data of other vehicles and utilizing a game theory after the target vehicle receives the driving state data of the other vehicles, and then solving the game matrix to acquire the lane change intention probability of the target vehicle;
the target vehicle lane change predicting and early warning module is used for inputting driving state data and lane change intention probability of a target vehicle, importing the driving state data and the lane change intention probability into a trained deep learning algorithm model, and predicting whether the target vehicle changes lanes or not; and if the target vehicle is predicted to change the lane, displaying early warning information on vehicle-mounted terminals of other vehicles.
Compared with the prior art, the invention has the beneficial technical effects that:
1. the invention can utilize the vehicle-mounted terminal to acquire, send and receive the driving state of the vehicle in real time. 2. The invention can use the vehicle running state data to estimate the intention of the vehicle to change the lane. 3. The invention can combine the lane changing intention with the driving state data to accurately predict the lane changing behavior of the vehicle in time. 4. The invention can send the predicted lane change behavior to the vehicle-mounted terminals of the surrounding vehicles in a vehicle-to-vehicle communication mode, thereby playing a role of early warning.
Drawings
FIG. 1 is a schematic diagram of a channel change prediction and early warning system implementation process of the present invention;
FIG. 2 is a schematic diagram of a vehicle terminal design framework;
FIG. 3 is a schematic diagram of a vehicle terminal hardware device;
FIG. 4 is a schematic view of a plant communication system operational interface;
FIG. 5 is a schematic view of a lane-change behavior scenario of a vehicle;
FIG. 6 is a diagram illustrating the prediction accuracy of a prediction model;
fig. 7 is a schematic diagram of warning information display.
Detailed Description
In order to solve the problems of low prediction accuracy and long prediction time in the prior art, dynamic interaction of different vehicles in the driving process is obtained from data by combining a game theory and a deep learning technology, the information of the driving environment around the vehicles is analyzed, whether the current environment is suitable for lane changing of the vehicles is judged by using a game theory, the lane changing intention of a driver is further quantized, the running data of the vehicles are identified and predicted by using a deep learning algorithm under the condition that the lane changing intention of the driver is determined, and when the running data start to meet the characteristics of lane changing, the whole model can obtain the prediction result of the lane changing operation of the vehicles in a short time; the invention can also send the prediction result of the vehicle about to change the lane to the surrounding vehicles by using the vehicle-mounted terminal, thereby achieving the effect of early warning, avoiding accidents to the greatest extent and solving the problem that the prior art can not send early warning to the surrounding vehicles in time.
The following definitions or conceptual connotations related to the present invention are provided for illustration:
game theory: the Game Theory, also called the Game Theory, the Game office Theory, etc., is a new branch of modern mathematics and an important discipline of operational research. Generally consists of several elements including: participants, actions, information, policies, benefits, outcomes, balances, etc.
Game matrix: the set of policies of a plurality of participants participating in a game can be represented by a matrix or block diagram, which is called a game matrix.
The gain function: each participant participating in the game calculates a function of the benefit that is obtainable based on the type and selected action to which the participant belongs when participating in the game.
A safety revenue function: the calculation function of the profit under driving safety is available to each participant participating in the game.
A spatial gain function: the revenue calculation function in terms of travel space available to each participant participating in the game.
The following describes the embodiments of the present invention in detail. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are given by way of illustration and explanation only, not limitation.
The invention provides a method for constructing a prediction model of lane changing behavior of a vehicle, which comprises the following steps as shown in figure 1:
step one, collecting vehicle driving state data:
a vehicle-mounted terminal is arranged in each vehicle, and the vehicle-mounted terminal integrates a CAN bus module to collect vehicle running data, a GPS module to acquire vehicle positioning data, and a communication module to realize inter-vehicle data communication and a display module to display the collected vehicle running state data, as shown in FIGS. 2 to 4, FIG. 2 is a schematic diagram of a vehicle-mounted terminal design framework, FIG. 3 is a schematic diagram of vehicle-mounted terminal hardware equipment, and FIG. 4 is a schematic diagram of a vehicle-to-vehicle communication system display module operation interface; the vehicle travel data includes a speed, an acceleration, and a steering angle of the vehicle;
further, in the first step, the vehicle-mounted terminal adopts a design scheme of an MCU main control board and a communication module, the main control board adopts an NXP IMX6 development board, a CAN processing chip and an external interface are integrated, and the GPS module adopts an NEO7N module; CAN collect CAN bus data and GPS data in the running process of the vehicle.
Step two, data communication between vehicles:
each vehicle sends the vehicle running state data thereof to other vehicles through the communication module of the vehicle, receives the vehicle running state data of other vehicles, and randomly selects one vehicle as a target vehicle, such as a vehicle in fig. 5, namely the target vehicle;
further, in the second step, the communication equipment between the vehicles is a Zhongxing ZM 8350C-V2X module integrated on the MCU main control board, and the tasks of sending and receiving data are completed.
Step three, obtaining the lane change intention probability of the lane change behavior of the target vehicle:
after receiving vehicle running state data of other vehicles, the target vehicle establishes a game matrix by combining the vehicle running state data of the target vehicle and utilizing a game theory, and then solves the game matrix to obtain the lane change intention probability of the target vehicle; further, in the third step, the game theory considers the game among drivers as a non-cooperative static game under complete information.
More specifically, in the step three, a game matrix is established by using a game theory, and when the target vehicle is influenced by a front vehicle with a slow speed, the strategy of the target vehicle comprises lane changing or lane changing-free; meanwhile, the target vehicle can influence the driving of the rear vehicle close to the lane, so that the rear vehicle can give way or not give way through two strategies; the game matrix for the target vehicle and the rear workshop is therefore as follows:
TABLE 1 Game matrix for target vehicles and post-shop
Figure BDA0003130840890000071
The gaming matrix includes the following cases:
the lane change is required for the target vehicle, and under the condition that the rear vehicle gives way to the target vehicle: target vehicle profit is U SVC The benefit of the rear vehicle is U V1A ；
And when the target vehicle requires lane changing and the rear vehicle does not give way to the target vehicle: target vehicle revenue is U SVC The benefit of the rear vehicle is U V1R ；
And when the target vehicle does not require lane changing and the rear vehicle gives way to the target vehicle: target vehicle revenue is U SVS The income of the rear vehicle is U V1A ；
The target vehicle does not require lane changing, and the rear vehicle does not give way to the target vehicle: target vehicle revenue is U SVS The benefit of the rear vehicle is U V1R 。
Each income function under different strategies is composed of a safety income U safety And space profit U space The composition is as follows:
U＝αU safety +βU space wherein β is a Sigmoid function, α =1- β;
wherein:
Figure BDA0003130840890000072
Figure BDA0003130840890000073
in the above formula, T (T) is the headway at time T, T min (t) is the minimum safe headway, D (t) is the distance to the front vehicle, D min (t) is the minimum safe distance.
Minimum safe distance of target vehicle to rear vehicle:
Figure BDA0003130840890000081
wherein, a V1 For rear vehicle acceleration, a sv Is the target vehicle acceleration, v v1 To rear vehicle speed, v sv Is the target vehicle speed;
minimum safe headway of target vehicle and back car:
T min (t)＝min(T initial ,T a )
wherein, T initial The time interval between the rear vehicle and the front vehicle at the time T, T a Is a fixed value and is set to 3.
In the third step, solving the game matrix to obtain the lane change intention probability of the target vehicle comprises the following steps:
firstly, the target vehicle judges the strategy of the maximum profit of the target vehicle according to the game matrix, namely lane changing or lane not changing, then the rear vehicle selects the reaction of the maximum profit of the target vehicle under the current condition according to the decision of the target vehicle, namely lane giving or lane not giving, and then the reaction is fed back to the target vehicle, and the circulation is continued until both game parties are satisfied, and the final balanced state is reached;
according to the game matrix, under the equilibrium state, the decision gain of the target vehicle is U sv And the other decision benefit is U sv* And if the quantifiable game result is the lane change probability:
Figure BDA0003130840890000082
pro (t) is a game result and is also the lane change probability of the target vehicle; if the game is passed, the decision of the target vehicle is lane change, and the lane change probability of the target vehicle can be calculated as follows:
Figure BDA0003130840890000083
and step four, establishing a deep learning algorithm model, taking the vehicle driving data and the lane change intention probability as driving sample data in the lane change process of the vehicle, inputting a training sample set consisting of the driving sample data into the deep learning algorithm model, taking whether the lane change of the vehicle is carried out as the output of the deep learning algorithm model, training the deep learning algorithm model, and taking the trained deep learning algorithm model as a vehicle lane change behavior prediction model. The deep learning algorithm model consists of a long-short term memory network LSTM and a convolutional neural network CNN; wherein the network structure of the LSTM can be described by three control gates and a memory state, which can be represented by the following formula:
an input gate:
Figure BDA0003130840890000091
Figure BDA0003130840890000092
forgetting the door:
Figure BDA0003130840890000093
Figure BDA0003130840890000094
an output gate:
Figure BDA0003130840890000095
Figure BDA0003130840890000096
a storage unit:
Figure BDA0003130840890000097
Figure BDA0003130840890000098
and (3) outputting:
Figure BDA0003130840890000099
in the above-mentioned equation, the equation,
Figure BDA00031308408900000910
the input data for each moment consists of the running state of the vehicle (horizontal/longitudinal speed, horizontal/longitudinal acceleration and steering angle) and the result of the game theory model.
Figure BDA00031308408900000911
Indicating the memory of the information at the previous moment.
Figure BDA00031308408900000912
Is composed of input data and cell states, each representing a computation of a corresponding part of the network, including an input gate, a forgetting gate, an output gate, and a memory cell (cell).
Figure BDA0003130840890000101
Respectively, the outputs of the various parts after different activation functions. Omega il 、ω cl 、
Figure BDA0003130840890000102
ω iw 、ω cw Respectively, the weight matrix of the network. During the learning process of the network, their values gradually tend to the optimal weights. The result of this module is
Figure BDA0003130840890000103
It represents the output of blocks.
Importing the data processed by the LSTM into a CNN assembly, and further extracting features from the result; the mathematical model of CNN is represented by the following function:
a convolutional layer: the convolution layer has the function of extracting the characteristics of input data, and each element forming the convolution kernel corresponds to a weight coefficient and a deviation value.
Figure BDA0003130840890000104
A pooling layer: after feature extraction is performed on the convolutional layer, the output features are transmitted to the pooling layer for feature selection and information filtering.
c＝[c 1 ，c 2 ，…，c n-k+1 ]
Figure BDA0003130840890000105
Wherein c is i For the convolution result, W ∈ R and b ∈ R are parameters in the convolution process and represent convolution kernels and biases.
c is equal to R and
Figure BDA0003130840890000106
is a simple expression of the pooling process; and after convolution and pooling operations are carried out, outputting a prediction result of whether the vehicle changes the lane or not by using the whole model by using the full communication layer.
More specifically, in the present embodiment, in the deep learning algorithm model, the number of hidden layer nodes is set to 100, the learning rate is 0.001, the step size is 8, and in the CNN, the convolution kernel is a 3 × 3 matrix, and the convolution step size is 1.
In this embodiment, the sample data is a driving data segment which is acquired in advance, segmented and labeled manually in the lane change process of the vehicle, the data dimensions include lateral/longitudinal speed, lateral/longitudinal acceleration, steering angle and game results, the length of each sample segment is 20, the data frequency is 10Hz, 1659 lane change samples and 986 lane change-free samples are used together to train the network, the number of the right lane change samples is 509, and the number of the left lane change samples is 1150.
In this embodiment, the trained network prediction result is shown in fig. 6, and the accuracy can reach about 94%.
The invention also provides a vehicle lane change behavior prediction and early warning method, which comprises the following steps:
step one, collecting vehicle driving state data:
the system comprises a vehicle-mounted terminal, a communication module, a display module and a CAN bus module, wherein the vehicle-mounted terminal is arranged in each vehicle and integrates a CAN bus module to acquire vehicle running data, a GPS module to acquire positioning data of the vehicles and the communication module to realize data communication among the vehicles and the display module to display the acquired vehicle running state data; the vehicle travel data includes a speed, an acceleration, and a steering angle of the vehicle;
step two, data communication between vehicles:
each vehicle transmits its vehicle driving state data to other vehicles through its communication module and receives the vehicle driving state data of the other vehicles;
step three, obtaining the lane change intention probability of the lane change behavior of the target vehicle by the method in the step 3 in the vehicle lane change behavior prediction model construction method;
step four, taking the driving state data of the target vehicle and the lane change intention probability as input, importing the data into the trained deep learning algorithm model, and predicting whether the target vehicle changes lanes or not; if it is predicted that the target vehicle will make a lane change, the warning information is displayed on the in-vehicle terminals of the other vehicles, as shown in fig. 7.
The invention also provides a vehicle lane change behavior prediction and early warning system, which comprises:
the vehicle running state data acquisition module comprises a vehicle-mounted terminal arranged in each vehicle, the vehicle-mounted terminal integrates a CAN bus module to acquire vehicle running data, a GPS module to acquire positioning data of the vehicles, and a communication module to realize data communication between the vehicles and a display module to display the acquired vehicle running state data;
the inter-vehicle data communication module is used for transmitting the vehicle driving state data of each vehicle to other vehicles and receiving the vehicle driving state data of other vehicles;
the lane change intention probability acquisition module is used for establishing a game matrix by combining the driving state data of the vehicle after the target vehicle receives the driving state data of other vehicles and utilizing a game theory after the target vehicle receives the driving state data of the other vehicles, and then solving the game matrix to acquire the lane change intention probability of the target vehicle;
the target vehicle lane change prediction and early warning module is used for importing the driving state data of the target vehicle and the lane change intention probability into a trained deep learning algorithm model to predict whether the target vehicle changes lanes or not; and if the target vehicle is predicted to change the lane, displaying early warning information on vehicle-mounted terminals of other vehicles.
And (3) experimental verification:
compared with the prior art and experimental effects, in the aspects of the prediction accuracy and prediction time of the algorithm, the prior art and the technique provided by the invention are tested by using real data, and the test results are shown in the following table 1:
TABLE 1 test results
Figure BDA0003130840890000121
The test results show that the accuracy of the lane change behavior prediction algorithm based on the game theory is improved, and the prediction time based on the deep learning algorithm is reduced.
The invention provides a complete behavior prediction process, which comprises data acquisition, workshop communication, a prediction algorithm and an early warning module, and realizes accurate prediction and early warning of lane changing behavior of a driver theoretically and practically. The data acquisition module ensures the data source under the real condition of the invention, and achieves the purpose of acquiring the vehicle operation data in real time; the vehicle-to-vehicle communication module ensures the effectiveness of the vehicle networking and the feasibility of the invention, and can complete the data transmission between vehicles and the sending of early warning information; the prediction algorithm combines the game theory and deep learning, and reduces the prediction time as much as possible on the premise of ensuring the prediction precision; the early warning module utilizes workshop communication to send the prediction result to display device on, can reach the purpose of reminding other vehicle drivers.

Claims (4)

1. A method for constructing a prediction model of a lane change behavior of a vehicle, wherein the model is used for predicting the lane change behavior of the vehicle, and is characterized by comprising the following steps:
step 1, collecting vehicle running state data:
a vehicle-mounted terminal is arranged in each vehicle, and the vehicle-mounted terminal integrates a CAN bus module to collect vehicle running data, a GPS module to acquire positioning data of the vehicles, and a communication module to realize data communication between the vehicles and a display module to display the collected vehicle running state data; the vehicle travel data includes a speed, an acceleration, and a steering angle of the vehicle;
step 2, data communication between vehicles:
each vehicle sends the vehicle running state data to other vehicles through a communication module of the vehicle, receives the vehicle running state data of other vehicles, and randomly selects one vehicle as a target vehicle;
step 3, obtaining the lane change intention probability of the lane change behavior of the target vehicle:
after receiving vehicle running state data of other vehicles, the target vehicle establishes a game matrix by combining the vehicle running state data of the target vehicle and utilizing a game theory, and then solves the game matrix to obtain the lane change intention probability of the target vehicle;
step 4, establishing a deep learning algorithm model, taking vehicle driving data and lane change intention probability as driving sample data in a vehicle lane change process, inputting a training sample set consisting of the driving sample data into the deep learning algorithm model, taking whether the vehicle lane change is carried out or not as the output of the deep learning algorithm model, training the deep learning algorithm model, and taking the trained deep learning algorithm model as a vehicle lane change behavior prediction model;
step 3, establishing a game matrix by using a game theory, wherein when the target vehicle is influenced by a front vehicle with a slow speed, the strategy of the target vehicle comprises lane changing or lane changing not; meanwhile, the target vehicle can influence the running of the rear vehicle close to the lane, so that the rear vehicle can give way or not give way by two strategies; the gaming matrix of the target vehicle and the following workshop therefore includes the following:
the lane change is required for the target vehicle, and under the condition that the rear vehicle gives way to the target vehicle: target vehicle profit is U SVC The benefit of the rear vehicle is U V1A ；
And when the target vehicle requires lane changing and the rear vehicle does not give way to the target vehicle: target vehicle revenue is U' SVC The benefit of the rear vehicle is U V1R ；
And when the target vehicle does not require lane changing and the rear vehicle gives way to the target vehicle: target vehicle revenue is U SVS And the return of the rear vehicle is U' V1A ；
The target vehicle does not require lane changing, and the rear vehicle does not give way to the target vehicle: target vehicle revenue is U' SVS After, afterThe vehicle income is U' V1R ；
Each income function under different strategies is composed of a safety income U safety And space profit U space The composition is as follows:
U＝αU safety +βU space wherein β is a Sigmoid function, α =1- β;
wherein:
Figure FDA0003805666330000021
Figure FDA0003805666330000022
in the above formula, T (T) is the headway at time T, T min (t) is the minimum safe headway, D (t) is the distance to the front vehicle, D min (t) is the minimum safe distance;
minimum safe distance of target vehicle and rear vehicle:
Figure FDA0003805666330000023
wherein, a V1 For rear vehicle acceleration, a sv Is a target vehicle acceleration, v v1 To rear vehicle speed, v sv Is a target vehicle speed;
minimum safe headway of target vehicle and back car:
T min (t)＝min(T initial ,T a )
wherein, T initial The time interval between the rear vehicle and the front vehicle at the time T, T a Is a fixed value and is set to be 3;
in the step 3, solving the game matrix to obtain the lane change intention probability of the target vehicle comprises the following steps:
firstly, the target vehicle judges the strategy of the maximum profit of the target vehicle according to the game matrix, namely lane changing or lane not changing, then the rear vehicle selects the reaction of the maximum profit of the target vehicle under the current condition according to the decision of the target vehicle, namely lane giving or lane not giving, and then the reaction is fed back to the target vehicle, and the circulation is continued until both game parties are satisfied, and the final balanced state is reached;
according to the game matrix, under the equilibrium state, the decision gain of the target vehicle is U sv And the other decision benefit is instead
Figure FDA0003805666330000025
The quantifiable game result, namely the lane change probability is as follows:
Figure FDA0003805666330000024
wherein Pro (t) is the game result and is also the lane change probability of the target vehicle.
2. The vehicle lane-changing behavior prediction model construction method according to claim 1, characterized in that the deep learning algorithm model is composed of a long-short term memory network LSTM and a convolutional neural network CNN; in the deep learning algorithm model, the number of hidden layer nodes is set to be 100, the learning rate is 0.001, the step length is 8, and in the CNN, the convolution kernel is a 3 × 3 matrix and the convolution step length is 1.
3. A prediction and early warning method for a lane change behavior of a vehicle is characterized by comprising the following steps:
step one, collecting vehicle driving state data:
a vehicle-mounted terminal is arranged in each vehicle, and the vehicle-mounted terminal integrates a CAN bus module to collect vehicle running data, a GPS module to acquire positioning data of the vehicles, and a communication module to realize data communication between the vehicles and a display module to display the collected vehicle running state data; the vehicle travel data includes a speed, an acceleration, and a steering angle of the vehicle;
step two, data communication between vehicles:
each vehicle transmits its vehicle running state data to other vehicles through its communication module and receives the vehicle running state data of the other vehicles;
step three, obtaining the lane change intention probability of the lane change behavior of the target vehicle by the method in the step 3 in the vehicle lane change behavior prediction model building method in claim 1;
step four, taking the driving state data of the target vehicle and the lane change intention probability as input, importing the data into the trained deep learning algorithm model of claim 1, and predicting whether the target vehicle changes lanes or not; and if the target vehicle is predicted to change the lane, displaying early warning information on vehicle-mounted terminals of other vehicles.
4. The utility model provides a vehicle lane change action prediction early warning system which characterized in that includes:
the vehicle running state data acquisition module comprises a vehicle-mounted terminal arranged in each vehicle, the vehicle-mounted terminal integrates a CAN bus module to acquire vehicle running data, a GPS module to acquire positioning data of the vehicles, and a communication module to realize data communication between the vehicles and a display module to display the acquired vehicle running state data;
the inter-vehicle data communication module is used for transmitting the vehicle driving state data of each vehicle to other vehicles and receiving the vehicle driving state data of other vehicles;
the lane change intention probability acquisition module is used for establishing a game matrix by combining the driving state data of the vehicle with the driving state data of the vehicle after the target vehicle receives the driving state data of other vehicles through the vehicle lane change behavior prediction model construction method in claim 1, and solving the game matrix to acquire the lane change intention probability of the target vehicle;
the lane change predicting and early warning module of the target vehicle is used for taking the driving state data and the lane change intention probability of the target vehicle as input, importing the data into the trained deep learning algorithm model of claim 1, and predicting whether the target vehicle changes the lane or not; and if the target vehicle is predicted to change the lane, displaying early warning information on vehicle-mounted terminals of other vehicles.
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