Counterfactual-Based Root Cause Analysis for Misconfigurations in Autonomous Driving Systems

As the core system in autonomous vehicles, Autonomous Driving Systems (ADSs) are highly configurable, where misconfigurations can significantly impact control safety, reliability, and overall performance. Although several testing methods have been proposed to detect misconfiguration-induced violations, most primarily focus on identifying the presence of incorrect configurations rather than pinpointing the specific configuration parameters responsible for these violations. However, identifying and understanding the root causes of misconfiguration-induced violations is essential for effective debugging and rapid system recovery. In this paper, we propose a CounterFactual-based Root Cause Analysis (RCA) method, CF-RCA, to identify the root causes of misconfiguration-induced violations by performing counterfactual attribution. Specifically, CF-RCA first formalizes the relationships between various configuration parameters and violations by learning a structural causal model. Then, based on the causal model, CF-RCA employs counterfactual attribution to estimate the impact of each configuration parameter on violations and identifies the most impactful parameter as the RCA result. We evaluate CF-RCA on the MetaDrive simulator with 12,926 driving scenarios, and the results show CF-RCA can efficiently identify violation-causing parameters, achieving 98.3% accuracy. Finally, the experimental comparisons with existing methods and tests across different ADSs further demonstrate the superiority and generalizability of CF-RCA.