面向无人驾驶系统的仿真模糊测试：现状、挑战与展望

无人驾驶技术是交通运载领域中最具颠覆性的创新技术，其广泛的应用前景推动了众多科技企业和整车厂商的争相布局. 安全性，作为无人驾驶重要的预期属性，是其大规模落地的必要前提. 目前，在无人驾驶系统安全测评领域，仿真模糊测试技术因其高效的无人驾驶事故场景挖掘能力而备受关注. 然而，面向该技术的研究仍在起步阶段，相关的研究成果还无法可靠地缓解无人驾驶系统的功能安全问题. 在此背景下，首先介绍了仿真模糊测试的基本架构以及研究现状. 随后，尝试总结已有工作的不足与面临的挑战，并提出针对性的优化方案. 为验证这些优化方案的有效性和先进性，进一步将其用于主流开源无人驾驶系统Apollo和Autoware的安全评测中. 结果显示，这些方案可以极大地提升现有仿真模糊测试技术的事故挖掘与分析能力. 在此基础上，进一步展望了该领域中可能的研究方向，为后续工作提供指导性建议.

J3016 - Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems

Autonomous driving has become one of the most revolutionary innovations in the field of transportation. Its extensive application prospect drives many manufacturers to develop and deploy autonomous vehicles on public roads. However, given the fact that traffic accidents involving autonomous vehicles continue to occur, safety has become the main obstacle for their widespread adoption. To tackle this issue, simulation-based fuzzing is gaining attention due to its capability to automatically identify flaws of autonomous driving systems which may cause traffic accidents. However, this technology is still in its early stages of research, and existing work are far from faithfully addressing the potential safety issues of autonomous driving systems. Considering this, we first introduce the basic architecture and research status of this technology. After that, we summarize the shortcomings of existing work, as well as the challenges to achieve effective simulation-based fuzzing. Accordingly, we try to propose solutions which can potentially tackle these challenges. To showcase the effectiveness of these solutions, we apply them in the safety assessment of popular open-source autonomous driving systems (i.e., Apollo and Autoware). Results show that, these solutions can boost the capability of existing simulation-based fuzzers in identifying and diagnosing safety-related flaws of Apollo and Autoware. Finally, we try to pinpoint the future research directions, so as to ease follow-up research.