Lane malicious intrusion detection based on recurrent neural network

Kaijun Mai; Xinghua Lu; Yifu Pan; Haoyu Xu; Fuxu Liu; Haiming Qiao

doi:10.1117/12.2647058

7 September 2022 Lane malicious intrusion detection based on recurrent neural network

Kaijun Mai, Xinghua Lu, Yifu Pan, Haoyu Xu, Fuxu Liu, Haiming Qiao

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123291T (2022) https://doi.org/10.1117/12.2647058
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

Aiming at the problem that the vehicle cannot accurately determine whether there is a malicious lane change of the preceding vehicle during high-speed cruise, a method based on recurrent neural network (RNN) is proposed to automatically determine the malicious lane change of the vehicle during high-speed cruise. Use HOG, Hear-like, SURF, LBP features to be fused into the SVM algorithm to detect the front sight distance environment, simulate different malicious lane changing scenarios as input features, preprocess the data and import it into a recurrent neural network to build an RNN model. Learning and predictability determine the tendency of vehicles to change direction maliciously and use the grey prediction method (GM model) to predict the behavior of vehicles maliciously changing lanes, so as to maintain a minimum safe distance between vehicles, thereby improving the safety of high-speed cruising and reducing the loss of traffic efficiency in the application process of variable speed limit control. Through the simulation experiment, the system can correctly judge the vehicle malicious lane change with an accuracy of 99.2%.

Citation Download Citation

Kaijun Mai, Xinghua Lu, Yifu Pan, Haoyu Xu, Fuxu Liu, and Haiming Qiao "Lane malicious intrusion detection based on recurrent neural network", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123291T (7 September 2022); https://doi.org/10.1117/12.2647058

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