ICSFuzz: Collision Detector Bug Discovery in Autonomous Driving Simulators
Weiwei Fu, Heqing Huang, Yifan Zhang, Ke Zhang, Jin Huang, Wei-Bin, Lee, Jianping Wang

TL;DR
ICSFuzz is a black-box fuzzing method that systematically discovers ignored collision scenarios in autonomous driving simulators, significantly improving their reliability and safety testing capabilities.
Contribution
The paper introduces ICSFuzz, a novel approach for efficiently finding ignored collision scenarios in simulators, enhancing autonomous vehicle safety verification.
Findings
ICSFuzz finds 10-20x more ignored collision scenarios than previous methods.
ICSFuzz achieves a 20-70x speedup in discovering collision scenarios.
All discovered ignored collisions were confirmed by developers, with one CVE ID assigned.
Abstract
With the increasing adoption of autonomous vehicles, ensuring the reliability of autonomous driving systems (ADSs) deployed on autonomous vehicles has become a significant concern. Driving simulators have emerged as crucial platforms for testing autonomous driving systems, offering realistic, dynamic, and configurable environments. However, existing simulation-based ADS testers have largely overlooked the reliability of the simulators, potentially leading to overlooked violation scenarios and subsequent safety security risks during real-world deployment. In our investigations, we identified that collision detectors in simulators could fail to detect and report collisions in certain collision scenarios, referred to as ignored collision scenarios. This paper aims to systematically discover ignored collision scenarios to improve the reliability of autonomous driving simulators. To this…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Transportation and Mobility Innovations · Vehicular Ad Hoc Networks (VANETs)
