Quantitative research on vehicle driving risk based on dynamic interaction potential field

With the rapid advancement of autonomous driving technology, modeling and predicting inter-vehicle interactions and associated risks have become critical for enhancing road safety and traffic efficiency. A dynamic interaction potential field model is proposed in this paper to address these challenges. The model incorporates inter-vehicle interactions and spatio-temporal conflicts, dynamically adjusting elliptical interaction regions to quantify and predict interaction risks. The effectiveness of the model in quantifying and predicting inter-vehicle risk is evaluated using actual vehicle trajectory data (including velocity, acceleration, and position) from the INTERACTION dataset to extract key interaction features and perform risk analysis. The simulation results in MATLAB demonstrate that the dynamic interaction potential field model can significantly improve the average speed, shorten the driving time, and exhibit good robustness under different initial conditions. In the lane changing scenario, the model effectively reduces the interaction risk, enhances the driving stability and improves the road access efficiency, which provides a strong theoretical support and practical guidance for the safe decision-making of the automatic driving system.