TM-fuzzer: fuzzing autonomous driving systems through traffic management

Abstract

Simulation testing of Autonomous Driving Systems (ADS) is crucial for ensuring the safety of autonomous vehicles. Currently, scenarios searched by ADS simulation testing tools are less likely to expose ADS issues and highly similar. In this paper, we propose TM-fuzzer, a novel approach for searching ADS test scenarios, which utilizes real-time traffic management and diversity analysis to search security-critical and unique scenarios within the infinite scenario space. TM-fuzzer dynamically manages traffic flow by manipulating non-player characters near autonomous vehicle throughout the simulation process to enhance the efficiency of test scenarios. Additionally, the TM-fuzzer utilizes clustering analysis on vehicle trajectory graphs within scenarios to increase the diversity of test scenarios. Compared to the baseline, the TM-fuzzer identified 29 unique violated scenarios more than four times faster and enhanced the incidence of ADS-caused violations by 26.26%. Experiments suggest that the TM-fuzzer demonstrates improved efficiency and accuracy.