Year: 2025
Type: article
Abstract: Safety remains the primary obstacle to the widespread deployment of high-level autonomous driving systems (ADS). Therefore, manufacturers are in severe need of techniques to detect and mitigate safety-critical ADS faults. Although various ADS testing techniques have been proposed to identify accident-prone driving scenarios, reliable ADS-oriented accident diagnosis techniques remain scarce. This gap significantly hinders fault confirmation and mitigation. In light of this, we propose ADSDx , an automated tool dedicated to diagnosing safety-critical accidents of high-level ADSs. ADSDx is built on top of the high-fidelity ADS simulation platform. Given a reproducible accident scenario, ADSDx utilizes what-if reasoning to incrementally narrow down the candidate scope of accident-triggering conditions and accident-responsible ADS modules. In our evaluation, ADSDx demonstrated high effectiveness in diagnosing safety-critical accidents of industry-grade ADSs (e.g., Apollo and Autoware), achieving 95.0% precision and 100% recall in identifying accident-triggering driving conditions and 96.3% accuracy in locating faulty ADS modules. Furthermore, we demonstrated that ADSDx can help filter out 87.9–91.4% of fault-irrelevant messages, which is essential for investigating the in-depth root causes of ADS-involved accidents. Owing to this, we identified 10 code-level root causes of Apollo and Autoware, each of which can be stably exploited to cause collision accidents in diverse driving situations. To date, six of them have been confirmed by developers and one patch has been merged into the released version of ADS.
DOI: 10.1145/3783993
Authors: Zongan Huang, Jiarun Dai, Mingyuan Luo, Yuqi Chen, Huijia Sun, Yuan Zhang, Min Yang
Institutions: Fudan University, ShanghaiTech University
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: G371022529, G8586212894, G8590833815
OpenAlex ID: W7114897690