Branch-Level Fault Localization in ADS Planning via Temporal Coverage Analysis

Abstract

Planning failures in Automated Driving Systems (ADS) are increasingly detected through simulation-based testing, yet localizing their root causes within planning code remains a major challenge. Planning modules execute complex rule-based decision logic over hundreds of frames in a closed-loop interaction with the environment, where faults trigger observable failures only after temporal gaps and under specific execution contexts. These characteristics make traditional spectrum-based fault localization ineffective, as faulty behavior is obscured by execution-level coverage aggregation and limited test diversity. In this paper, we study the problem of debugging planning failures and present a temporal coverage analysis approach for localizing faults in rule-based planning modules. Our key insight is that, while execution-aggregated coverage masks fault behavior, frame-level execution dynamics reveal distinctive temporal signatures that indicate when and how faulty branches activate. Leveraging this insight, our approach first identifies a suspicious frame using planning semantics, and then ranks candidate branches by analyzing their execution behavior within a localized temporal window. We evaluate our approach on 221 reproducible non-collision Apollo planning failures, covering immobility and emergency mission failures. Our results show that temporal coverage analysis enables accurate suspicious-frame identification and substantially reduces branch inspection effort compared to oracle-based and random baselines, effectively localizing faults from a single failing execution. We further analyze failure cases that lack observable execution signals to clarify the fundamental limits of execution-based localization. Overall, this work demonstrates that temporal execution analysis provides a practical and effective foundation for debugging planning failures in rule-based ADS planning modules.