为自动驾驶系统设计安全决策框架,防错降级更可靠
Better Safe Than Sorry: Enhancing Arbitration Graphs for Safe and Robust Autonomous Decision-Making
- 在仲裁图中加入验证与容错层,确保指令安全执行
- 仿真和实测均显著降低事故风险,提升系统鲁棒性
- 适合需高安全性的自主系统开发,如无人驾驶
本文提出一种扩展的仲裁图框架,用于提升复杂动态环境中自主系统的安全性与鲁棒性。在保持原有灵活性与可扩展性的基础上,新方法引入验证步骤与结构化降级层,确保仅经验证的安全指令被执行,并在意外故障或缺陷出现时实现平稳退化。通过Pac-Man模拟与自动驾驶场景验证,该方法显著降低事故风险并提升整体安全性。仲裁图的自底向上设计支持逐步集成新行为模块,本工作所提扩展可在不牺牲安全的前提下,纳入实验性或不成熟的行为组件,明确界定其安全触发条件。相关实现以头文件形式提供,开源于MIT许可证下,代码与演示项目见github.com/KIT-MRT/arbitration_graphs。
原文摘要 · Abstract (English)
This paper introduces an extension to the arbitration graph framework designed to enhance the safety and robustness of autonomous systems in complex, dynamic environments. Building on the flexibility and scalability of arbitration graphs, the proposed method incorporates a verification step and structured fallback layers in the decision-making process. This ensures that only verified and safe commands are executed while enabling graceful degradation in the presence of unexpected faults or bugs. The approach is demonstrated using a Pac-Man simulation and further validated in the context of autonomous driving, where it shows significant reductions in accident risk and improvements in overall system safety. The bottom-up design of arbitration graphs allows for an incremental integration of new behavior components. The extension presented in this work enables the integration of experimental or immature behavior components while maintaining system safety by clearly and precisely defining the conditions under which behaviors are considered safe. The proposed method is implemented as a ready to use header-only C++ library, published under the MIT License. Together with the Pac-Man demo, it is available at github.com/KIT-MRT/arbitration_graphs.
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