A lightweight social cognitive risk potential field model for path planning with dedicated dynamic and static traffic factors

Driving risk assessment is crucial for autonomous vehicles to guarantee driving safety and traffic efficiency. Risk field models with insufficient consideration of traffic factors are not reliable enough to provide effective support for automated driving tasks, and those highly complex models with numerous uncertain coefficients also limit the execution of automated driving tasks. Inspired by Coulomb's law, this paper proposes a new lightweight social cognitive driving risk potential field model by leveraging interaction forces between charges to explore the effects of dynamic and static traffic factors on driving risks. Through complexity analysis, the number of coefficients in the proposed model was reduced by 36%–50% compared to other models. With parametric analysis and sensitivity analysis, the model's reliability was demonstrated. A path planner was designed by integrating the proposed driving risk field model into a model predictive controller for validating the efficacy of the proposed risk potential field model. The planned path with the proposed risk field model was also compared with existing risk potential field models. Results indicate that the proposed model can effectively account for both dynamic and static traffic factors, thereby supporting the path planner to generate highly adaptable paths for complex traffic scenarios.