Conference proceeding
AV-FUZZER: Finding Safety Violations in Autonomous Driving Systems
2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), Vol.2020-, pp.25-36
10/2020
DOI: 10.1109/ISSRE5003.2020.00012
Abstract
This paper proposes AV-FUZZER, a testing framework, to find the safety violations of an autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the driving maneuvers of traffic participants to create situations in which an AV can run into safety violations. To optimally search for the perturbations to be introduced, we leverage domain knowledge of vehicle dynamics and genetic algorithm to minimize the safety potential of an AV over its projected trajectory. The values of the perturbation determined by this process provide parameters that define participants' trajectories. To improve the efficiency of the search, we design a local fuzzer that increases the exploitation of local optima in the areas where highly likely safety-hazardous situations are observed. By repeating the optimization with significantly different starting points in the search space, AV-FUZZER determines several diverse AV safety violations. We demonstrate AV-FUZZER on an industrial-grade AV platform, Baidu Apollo, and find five distinct types of safety violations in a short period of time. In comparison, other existing techniques can find at most two. We analyze the safety violations found in Apollo and discuss their overarching causes.
Details
- Title: Subtitle
- AV-FUZZER: Finding Safety Violations in Autonomous Driving Systems
- Creators
- Guanpeng Li - University of Illinois Urbana-ChampaignYiran Li - University of Illinois Urbana-ChampaignSaurabh Jha - University of Illinois Urbana-ChampaignTimothy Tsai - NvidiaMichael Sullivan - NvidiaSiva Kumar Sastry Hari - NvidiaZbigniew Kalbarczyk - University of Illinois Urbana-ChampaignRavishankar Iyer - University of Illinois Urbana-Champaign
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), Vol.2020-, pp.25-36
- DOI
- 10.1109/ISSRE5003.2020.00012
- ISSN
- 1071-9458
- eISSN
- 2332-6549
- Publisher
- IEEE
- Language
- English
- Date published
- 10/2020
- Academic Unit
- Computer Science
- Record Identifier
- 9984259426702771
Metrics
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