The safety-critical nature of autonomous vehicle (AV) operation necessitates development of task-relevant algorithms that can reason about safety at the system level and not just at the component level. To reason about the impact of a perception failure on the entire system performance, such task-relevant algorithms must contend with various challenges: complexity of AV stacks, high uncertainty in the operating environments, and the need for real-time performance. To overcome these challenges, in this work, we introduce a Q-network called SPARQ (abbreviation for Safety evaluation for Perception And Recovery Q-network) that evaluates the safety of a plan generated by a planning algorithm, accounting for perception failures that the planning process may have overlooked. This Q-network can be queried during system runtime to assess whether a proposed plan is safe for execution or poses potential safety risks. If a violation is detected, the network can then recommend a corrective plan while accounting for the perceptual failure. We validate our algorithm using the NuPlan-Vegas dataset, demonstrating its ability to handle cases where a perception failure compromises a proposed plan while the corrective plan remains safe. We observe an overall accuracy and recall of 90% while sustaining a frequency of 42Hz on the unseen testing dataset. We compare our performance to a popular reachability-based baseline and analyze some interesting properties of our approach in improving the safety properties of an AV pipeline.

翻译：自动驾驶车辆（AV）运行的安全关键性要求开发任务相关算法，这些算法能够在系统层面而非仅组件层面进行安全推理。为评估感知失效对整个系统性能的影响，此类任务相关算法必须应对多重挑战：自动驾驶软件栈的复杂性、运行环境的高度不确定性以及对实时性能的需求。为克服这些挑战，本研究提出一种名为SPARQ（Safety evaluation for Perception And Recovery Q-network的缩写）的Q网络，该网络能够评估规划算法生成路径方案的安全性，同时考虑规划过程可能忽略的感知失效问题。该系统可在运行时调用该Q网络，以判定拟执行路径方案是否安全或存在潜在风险。若检测到安全违规，该网络可结合感知失效情况推荐修正方案。我们使用NuPlan-Vegas数据集验证算法性能，证明其能有效处理感知失效危及原始方案而修正方案仍保持安全的场景。在未见测试数据集上，该方法达到90%的整体准确率与召回率，并保持42Hz的运行频率。通过与基于可达性的主流基线方法对比，我们分析了本方法在提升自动驾驶流程安全特性方面的若干重要特性。