Intelligent network vehicle driving risk field modeling and path planning for autonomous obstacle avoidance

Abstract

Autonomous obstacle avoidance and decision-making algorithms for intelligent connected vehicles in a complicated transportation environment are important studies on intelligent driving. However, it is difficult to adapt to a more complicated traffic environment based on safety distance and conventional potential field. Therefore, in this paper, a driving risk field model based on field theory is proposed involving transportation factors and vehicle conditions. A hidden Markov model was used to evaluate and determine the motion state of surrounding vehicles. A safe, feasible, and smooth collision-free path was planned by calculating the magnitude of the potential field forces on the longitudinal and lateral sides of the obstacle vehicles. The results showed that the method can effectively select a suitable path for obstacle avoidance in complex road conditions while satisfying safety and traffic laws.