Vehicle Motion Prediction Algorithm Based on Artificial Potential Field Correction and Fuzzy C-Mean Driving Intention Classification

Abstract

Predicting the trajectories of surrounding vehicles plays an important role in the driving safety of autonomous vehicles. It impacts the decision making, path planning, and vehicle motion control process in autonomous vehicles. However, due to the uncertainty of vehicle dynamics, driving intention, and the complexity of the surrounding environment, there are interactions between vehicles and other issues, and their motion prediction faces great challenges. This paper proposes a trajectory prediction algorithm combining driving intention classification and environmental interaction correction to overcome the leading vehicle movement prediction problem. In order to solve the problems of uncertainty in predicting vehicle driving intention and nonlinearity between future vehicle movements and the environment, a driving intention recognition based on the Fuzzy C-mean algorithm and a forward vehicle motion prediction algorithm combining multi-model prediction results are proposed. The artificial potential field method is also used to model vehicle interaction and correct the trajectory prediction results. Finally, the real vehicle data validation proves that this algorithm has high prediction accuracy.