Abstract Automated Driving Systems (ADSs) coordinate multiple modules (e.g., perception, prediction, planning, and control) and expose a large configuration surface that engineers must tune for different platforms and operational conditions. In industrial practice, misconfigured parameters are a common source of unsafe or law-violating behaviors, yet fixing such configuration bugs remains largely overlooked. Debugging is difficult because parameter effects propagate through long, nonlinear pipelines (often involving learning-based components), and manual trial-and-error provides little guidance in high-dimensional spaces while risking regressions in previously passing scenarios. We propose ConFixer , an automated approach for repairing ADS configuration bugs against formal correctness specifications, including traffic laws encoded in signal temporal logic (STL). ConFixer uses robustness semantics to derive gradient-like signals that localize bug-relevant parameters and guide fine-tuning toward compliance. To support adoption in scenario-based validation workflows, ConFixer evaluates candidate fixes on large scenario suites and prioritizes regression-safe solutions. Our evaluation on Baidu Apollo with the LGSVL simulator shows that ConFixer fixes 173 configuration bugs without introducing regressions.

Motion planning is a difficult task, especially when generating feasible future trajectories in complex and interactive scenarios. While recent advancements in imitation-based planning have shown significant progress, this approach often encounters causal confusion in dynamic traffic environments. This confusion will cause the planner to incorrectly associate certain actions with outcomes, leading to suboptimal or unsafe plans. To address this, we introduce a novel framework called$\overline{C}^{2}L$, which improves the planner's latent Causal understanding by incorporating Contrastive Learning and counterfactual data augmentation. Additionally, we propose a shortcut eliminator to extract copycat-free features from history states, reducing the impact of temporal spurious correlations. We validate our method on the nuPlan and interPlan benchmarks, with extensive experiments demonstrating that$C^{2}L$delivers highly competitive performance compared to state-of-the-art methods.