勿忽视显而易见的内容——RISC：一种用于安全自动驾驶的风险感知切片覆盖协议
Do Not Forget the Obvious - RISC: A Risk-Informed Slice-Coverage Protocol for Safe Autonomous Driving
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中文总结 AI 辅助
该研究提出RISC协议，将安全问题转化为风险切片，通过风险引导选择提升自动驾驶关键故障发现率，可应用于多类自动驾驶子系统，补充现有测试验证流程。
中文摘要 AI 辅助
聚合指标可能无法完全反映在未充分检验的高风险驾驶场景中的性能。我们提出RISC（Risk-Informed Slice Coverage，风险感知切片覆盖），一种用于风险引导的压力测试和覆盖合格评估的实用协议。风险引导的压力测试将有限的审计预算导向与风险相关的子数据集，即风险切片；而覆盖合格评估则报告结果，同时明确说明哪些切片得到了充分或不充分的覆盖。该协议将安全问题转化为机器可读的风险切片，使用轻量信号标记候选数据，按风险选择紧凑的审计集，并利用覆盖证据对结果进行验证。LLM可在测试规划过程中通过揭示相关但可能被忽视的条件来可选地支持这一过程，从而帮助工程师不忽视显而易见的内容。RISC与模型无关，可应用于感知模块、驾驶模型及其他自动驾驶子系统。我们使用Zenseact开放数据集的1000帧图像、图像统计数据和基于YOLO的检测器代理，为单目行人感知实例化该协议。在这项概念验证研究中，风险引导选择将关键故障发现率从随机采样下的34.0%提升至98.5%。RISC提供了一个轻量的、面向保证的评估层，可补充场景分类、覆盖评估及更广泛的测试与验证工作流程。
英文摘要
Aggregate metrics may not fully reflect performance in insufficiently examined high-risk driving conditions. We propose RISC (Risk-Informed Slice Coverage), a practical protocol for risk-guided stress testing and coverage-qualified evaluation. Risk-guided stress testing directs a finite audit budget toward risk-relevant sub-datasets, called risk slices, while coverage-qualified evaluation reports results together with explicit statements about which slices are sufficiently or insufficiently covered. The protocol translates safety concerns into machine-readable risk slices, uses lightweight signals to tag candidate data, selects a compact audit set by risk, and qualifies the results using coverage evidence. An LLM can optionally support this process by surfacing relevant but potentially overlooked conditions during test planning, thereby helping engineers not to forget the obvious. RISC is model-agnostic and can be applied to perception modules, driving models, and other autonomous-driving subsystems. We instantiate the protocol for monocular pedestrian perception using 1,000 frames from the Zenseact Open Dataset, image statistics, and a YOLO-based detector proxy. In this proof-of-concept study, risk-guided selection increases critical failure discovery from 34.0% under random sampling to 98.5%. RISC provides a lightweight, assurance-oriented evaluation layer that complements scenario categorization, coverage assessment, and broader testing-and-verification workflows.
发表机构
- CARIAD SE
机构由 AI 辅助整理，请以论文原文为准。
