Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning

Author:

Jia Shuo1ORCID,Hui Fei1ORCID,Wei Cheng1ORCID,Zhao Xiangmo1,Liu Jianbei2

Affiliation:

1. School of Information Engineering, Chang’an University, Xi’an 710000, China

2. Research and Development Center on Emergency Support Technologies for Transport, CCCC First Highway Consultants Co., Ltd., Xi’an 710000, China

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.

Funder

National Key Research and Development Program of China

Publisher

Hindawi Limited

Subject

Strategy and Management,Computer Science Applications,Mechanical Engineering,Economics and Econometrics,Automotive Engineering

Reference30 articles.

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