SCTrans: Constructing a Large Public Scenario Dataset for Simulation Testing of Autonomous Driving Systems
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.
BibTeX
@inproceedings{Dai-al:ICSE24,
author = {Jiarun Dai and
Bufan Gao and
Mingyuan Luo and
Zongan Huang and
Zhongrui Li and
Yuan Zhang and
Min Yang},
title = {{SCTrans:} Constructing a Large Public Scenario Dataset for Simulation Testing of Autonomous Driving Systems},
booktitle = {ICSE},
pages = {50:1--50:13},
publisher = {{ACM}},
year = {2024},
}