A Causal Risk Analysis Approach for Enhancing Robustness in Autonomous Driving

The safety and reliability of autonomous vehicles represent critical prerequisites for their widespread deployment, yet operational risks remain diverse and complex. This paper employs causal reasoning techniques to investigate driving risk causality in self-driving cars, aiming to identify latent risk sources and quantify their impacts on driving behavior. We propose the RF-LM (Random Forest-Linear Model) framework, a novel two-stage machine learning approach for causal effect identification and estimation, specifically addressing human-vehicle congregation scenarios with weather-related confounding factors. Validated through the AVOID autonomous vehicle incident dataset, our methodology demonstrates significant accuracy improvements in causal modeling after confounder adjustment. Comprehensive sensitivity analysis, robustness testing, and heterogeneity examination further confirm the stability and reliability of the proposed risk source model.