Intelligent Vehicle Lane Change Risk Prediction Model Based on Deep Learning and Interactive Features
Keywords:
Interactive features, Intelligent vehicles, Risk prediction for lane changing, LSTM modelAbstract
With the development of autonomous driving technology, predicting the risk of vehicle lane changing has become one of the key tasks to improve the safety and performance of intelligent vehicles. Traditional lane change risk prediction methods often overlook the complex interaction characteristics between lane changing vehicles and the surrounding environment, resulting in insufficient prediction accuracy. This article proposes an intelligent vehicle lane change risk prediction method based on interactive features. Firstly, based on the high-D dataset, the trajectory data of surrounding interactive vehicles is extracted. The parking distance index is used to establish a risk assessment index, and the risk level is divided through clustering algorithm. Finally, the LSTM model was used for risk prediction, and the experimental results showed that the LSTM model with added interactive features had better prediction performance.
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