As highly automated vehicles reach higher deployment rates, they find themselves in increasingly dangerous situations. Knowing that the consequence of a crash is significant for the health of occupants, bystanders, and properties, as well as to the viability of autonomy and adjacent businesses, we must search for more efficacious ways to comprehensively and reliably train autonomous vehicles to better navigate the complex scenarios with which they struggle. We therefore introduce a taxonomy of potentially adversarial elements that may contribute to poor performance or system failures as a means of identifying and elucidating lesser-seen risks. This taxonomy may be used to characterize failures of automation, as well as to support simulation and real-world training efforts by providing a more comprehensive classification system for events resulting in disengagement, collision, or other negative consequences. This taxonomy is created from and tested against real collision events to ensure comprehensive coverage with minimal class overlap and few omissions. It is intended to be used both for the identification of harm-contributing adversarial events and in the generation thereof (to create extreme edge- and corner-case scenarios) in training procedures.

翻译：随着高度自动化车辆部署率的提升，它们正面临日益危险的境况。鉴于碰撞事故对乘员、行人与财产安全，乃至自动驾驶技术及其相关产业生存能力具有重大影响，我们必须探索更有效的方式，全面且可靠地训练自动驾驶车辆，使其能够更好地应对复杂场景中的挑战。为此，我们提出一套潜在对抗性要素的分类体系，这些要素可能导致性能下降或系统故障，从而识别并阐明较罕见的风险。该分类体系可用于表征自动化系统的失效模式，同时通过为导致系统退出、碰撞或其他负面后果的事件提供更全面的分类框架，支持仿真训练与真实场景训练工作。本分类体系基于真实碰撞事件构建并经过验证，以确保全面覆盖性、最小类别重叠及较少遗漏。其设计目的既用于识别导致危害的对抗性事件，也可在训练流程中生成此类事件（以构建极端边界与边缘场景）。