ICSE2024

SCTrans: Constructing a Large Public Scenario Dataset for Simulation Testing of Autonomous Driving Systems

Jiarun Dai, Bufan Gao, Mingyuan Luo, Zongan Huang, Zhongrui Li, Yuan Zhang, Min Yang

8 citations

Abstract

For the safety assessment of autonomous driving systems (ADS), simulation testing has become an important complementary technique to physical road testing. In essence, simulation testing is a scenario-driven approach, whose effectiveness is highly dependent on the quality of given simulation scenarios. Moreover, simulation scenarios should be encoded into well-formatted files, otherwise, ADS simulation platforms cannot take them as inputs. Without large public datasets of simulation scenario files, both industry and academic applications of ADS simulation testing are hindered.