一句话：构建面向自动驾驶系统仿真测试的大规模公共场景数据集，并以规范文件格式供仿真平台使用。
自动驾驶系统（ADS）的安全评估中，仿真测试已成为物理道路测试的重要补充。仿真测试本质上是场景驱动的方法，其有效性高度依赖给定仿真场景的质量；同时，仿真场景必须被编码为格式良好的文件，否则 ADS 仿真平台无法将其作为输入。缺乏大规模公开的仿真场景文件数据集，会同时制约工业界与学术界对 ADS 仿真测试的应用。针对这一问题，论文提出构建 SCTrans 这一大规模公共场景数据集，旨在提供格式规范、可被仿真平台读取的仿真场景文件，从而为 ADS 仿真测试提供公共数据基础。摘要未给出具体评估指标，但明确了该数据集对推进仿真测试应用的意义。
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.
DOI 原文 ·
@inproceedings{paperbot11367,
title = {SCTrans: Constructing a Large Public Scenario Dataset for Simulation Testing of Autonomous Driving Systems},
author = {Jiarun Dai and Bufan Gao and Mingyuan Luo and Zongan Huang and Zhongrui Li and Yuan Zhang and Min Yang},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3623350}
}