用线性时序逻辑让自动驾驶更像人一样权衡各类风险
Risk-Aware Autonomous Driving with Linear Temporal Logic Specifications
- 将人类驾驶风险认知扩展到线性时序逻辑，融合事件时机与严重程度
- 通过线性规划求解，实现碰撞与违规风险的平衡控制
- 适合关注安全合规与人性化决策的自动驾驶研究者
人类驾驶员在驾驶中自然权衡各类风险，包括交通规则违反、轻微事故和致命事故。然而，在自动驾驶系统中实现类似行为仍是开放问题。本文将已验证于人类驾驶研究的风险度量方法扩展至由线性时序逻辑（LTL）定义的复杂场景，不仅涵盖碰撞风险，还包含事件的时间与严重性。通过采用安全与共安全公式构成的LTL规范，保持交通规则表达力的同时，将控制合成问题转化为可达性问题。借助占用测度，进一步将其建模为线性规划（LP）问题。由此生成的策略可平衡多种驾驶风险，包括碰撞与规则违反。在Carla模拟器中的三个典型交通场景验证了该方法的有效性。
原文摘要 · Abstract (English)
Human drivers naturally balance the risks of different concerns while driving, including traffic rule violations, minor accidents, and fatalities. However, achieving the same behavior in autonomous driving systems remains an open problem. This paper extends a risk metric that has been verified in human-like driving studies to encompass more complex driving scenarios specified by linear temporal logic (LTL) that go beyond just collision risks. This extension incorporates the timing and severity of events into LTL specifications, thereby reflecting a human-like risk awareness. Without sacrificing expressivity for traffic rules, we adopt LTL specifications composed of safety and co-safety formulas, allowing the control synthesis problem to be reformulated as a reachability problem. By leveraging occupation measures, we further formulate a linear programming (LP) problem for this LTL-based risk metric. Consequently, the synthesized policy balances different types of driving risks, including both collision risks and traffic rule violations. The effectiveness of the proposed approach is validated by three typical traffic scenarios in Carla simulator.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。