Abstract
Lane changing is an important scenario in traffic environments, and accurate prediction of lane-changing behavior is essential to ensure traffic and driver safety. To achieve this goal, a vehicle lane-changing prediction model based on game theory and deep learning is developed. In the game theory component, the interaction between vehicles during lane changing is analyzed according to the running state of the vehicle, with the probability of lane changing as its output. For the deep-learning component, long short-term memory and a convolutional neural network are used to extract and learn data features during the lane-changing process as well as combine the output of the game theory component to obtain the prediction result of whether the vehicle will change lanes. By using an open-source traffic dataset to train and verify the proposed model, the verification results show that the prediction accuracy can reach 94.56% within 0.4 s of lane-changing operation and that the model can achieve timely and accurate prediction of the lane-changing behavior of vehicles.
Highlights
Drivers’ driving operations are closely related to road traffic safety; approximately 92.9% of the total traffic accidents are caused by improper driving operations [1]
Blind-spot-warning systems and lane departure warning systems integrated into advanced driver assistance systems can reduce the probability of accidents being caused by lateral operations to a certain extent, in actual applications, these systems need to rely on the driver to use the turn signal lights correctly
Active lane change is performed by the driver in expectation of a better driving environment, whereas passive lane change is performed because the vehicle needs to leave the current road, which generally occurs in a ramp area. is study only examines the active lane-changing behavior
Summary
Drivers’ driving operations are closely related to road traffic safety; approximately 92.9% of the total traffic accidents are caused by improper driving operations [1]. By solving the game theory model, we can predict whether the surrounding environment of the target vehicle is in a state suitable for lane changing and quantify the possibility that the driver will perform a lane-changing operation. A deep-learning algorithm composed of long short-term memory (LSTM) and a convolutional neural network (CNN) is established, and historical data are used to establish the training dataset of driver lateral operation. The entire model continuously monitors the driving-state data of all the vehicles in the target road section and analyzes the lateral and longitudinal motion characteristics of the vehicle. When the driving state of any vehicle begins to meet the lanechanging characteristics, the whole model can accurately predict the vehicle’s behavior in a short period of time, and the prediction result can be used to notify surrounding vehicles in time to avoid accidents to the greatest extent.
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