A Systematic Framework to Identify Violations of Scenario-dependent Driving Rules in Autonomous Vehicle Software
Abstract
Safety compliance is paramount to the safe deployment of autonomous vehicle (AV) technologies in real-world transportation systems. As AVs will share road infrastructures with human drivers and pedestrians, it is an important requirement for AVs to obey standard driving rules. Existing AV software testing methods, including simulation and road testing, only check fundamental safety rules such as collision avoidance and safety distance. Scenario-dependent driving rules, including crosswalk and intersection rules, are more complicated because the expected driving behavior heavily depends on the surrounding circumstances. However, a testing framework is missing for checking scenario-dependent driving rules on various AV software.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 01, 2021
- Source ID
- 10.1145/3460082
Entities
People
- David Hong
- Qi Alfred Chen
- Qingzhao Zhang
- Scott Mahlke
- Z. Morley Mao
- Ze Zhang
Organizations
- National Science Foundation
- Office of Naval Research
- University of California
- University of Michigan