在不确定性中驾驶：利用大语言模型常识实现风险厌恶控制以应对自动驾驶中的感知缺陷
Driving Through Uncertainty: Risk-Averse Control with LLM Commonsense for Autonomous Driving under Perception Deficits
AI总结：
本文提出LLM-RCO框架，利用大语言模型整合人类驾驶常识，以提升自动驾驶在感知缺陷下的风险规避与适应性控制能力。
摘要：
部分感知缺陷可能会通过破坏环境理解来威胁自动驾驶车辆的安全。现有协议通常默认采用完全风险规避的行为，如立即停车，这会损害导航目标，并且在罕见驾驶场景中缺乏灵活性。然而，在风险较小时，停车可能是不必要的，更适应性的反应是可取的。在本文中，我们提出了LLM-RCO，一种利用大语言模型（LLMs）将人类驾驶常识整合到面临感知缺陷的自动驾驶系统中的风险规避框架。LLM-RCO具有四个关键模块，与动态驾驶环境互动：危险推断、短期运动规划器、动作条件验证器和安全约束生成器，使系统能够在这些具有挑战性的条件下实现主动和情境感知的行为。为了增强LLM的驾驶决策能力，我们构建了DriveLM-Deficit，一个包含53,895个视频片段的数据集，这些片段展示了安全关键对象的缺陷，用于LLM微调以进行危险检测和运动规划。在CARLA模拟器中进行的大量实验表明，LLM-RCO在感知缺陷场景中促进了主动操作，而不是纯粹的风险规避行为，突显了其在提升自动驾驶对感知损失挑战的韧性方面的价值。
英文摘要：
Partial perception deficits can compromise autonomous vehicle safety by disrupting environmental understanding. Existing protocols typically default to entirely risk-avoidant actions such as immediate stops, which are detrimental to navigation goals and lack flexibility for rare driving scenarios. Yet, in cases of minor risk, halting the vehicle may be unnecessary, and more adaptive responses are preferable. In this paper, we propose LLM-RCO, a risk-averse framework leveraging large language models (LLMs) to integrate human-like driving commonsense into autonomous systems facing perception deficits. LLM-RCO features four key modules interacting with the dynamic driving environment: hazard inference, short-term motion planner, action condition verifier, and safety constraint generator, enabling proactive and context-aware actions in such challenging conditions. To enhance the driving decision-making of LLMs, we construct DriveLM-Deficit, a dataset of 53,895 video clips featuring deficits of safety-critical objects, annotated for LLM fine-tuning in hazard detection and motion planning. Extensive experiments in adverse driving conditions with the CARLA simulator demonstrate that LLM-RCO promotes proactive maneuvers over purely risk-averse actions in perception deficit scenarios, underscoring its value for boosting autonomous driving resilience against perception loss challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
