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

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