Current LLM-based driving agents that rely on unstructured plain-text memory suffer from low-precision scene retrieval and inefficient reflection. To address this limitation, we present RESPOND, a structured decision-making framework for LLM-driven agents grounded in explicit risk patterns. RESPOND represents each ego-centric scene using a unified 5 by 3 matrix that encodes spatial topology and road constraints, enabling consistent and reliable retrieval of spatial risk configurations. Based on this representation, a hybrid rule and LLM decision pipeline is developed with a two-tier memory mechanism. In high-risk contexts, exact pattern matching enables rapid and safe reuse of verified actions, while in low-risk contexts, sub-pattern matching supports personalized driving style adaptation. In addition, a pattern-aware reflection mechanism abstracts tactical corrections from crash and near-miss frames to update structured memory, achieving one-crash-to-generalize learning. Extensive experiments demonstrate the effectiveness of RESPOND. In highway-env, RESPOND outperforms state-of-the-art LLM-based and reinforcement learning based driving agents while producing substantially fewer collisions. With step-wise human feedback, the agent acquires a Sporty driving style within approximately 20 decision steps through sub-pattern abstraction. For real-world validation, RESPOND is evaluated on 53 high-risk cut-in scenarios extracted from the HighD dataset. For each event, intervention is applied immediately before the cut-in and RESPOND re-decides the driving action. Compared to recorded human behavior, RESPOND reduces subsequent risk in 84.9 percent of scenarios, demonstrating its practical feasibility under real-world driving conditions. These results highlight RESPONDs potential for autonomous driving, personalized driving assistance, and proactive hazard mitigation.

翻译：当前依赖非结构化纯文本记忆的LLM驾驶智能体存在场景检索精度低与反思效率不足的问题。为克服这一局限，本文提出RESPOND——一种基于显式风险模式的LLM驱动智能体结构化决策框架。RESPOND采用统一的5×3矩阵表示每个以自车为中心的驾驶场景，该矩阵编码空间拓扑与道路约束，实现了空间风险配置的一致可靠检索。基于此表征，我们构建了融合规则与LLM的双层记忆决策流程：在高风险情境中，精确模式匹配支持已验证安全行为的快速复用；在低风险情境中，子模式匹配则支持个性化驾驶风格适配。此外，框架引入模式感知反思机制，从碰撞与险情帧中抽象战术修正以更新结构化记忆，实现“一次碰撞，泛化学习”的效果。大量实验验证了RESPOND的有效性：在highway-env环境中，RESPOND在显著降低碰撞次数的同时，性能优于当前最先进的基于LLM与强化学习的驾驶智能体；通过逐步人类反馈，智能体能在约20个决策步内通过子模式抽象习得Sporty驾驶风格。为进行真实世界验证，我们从HighD数据集中提取53个高风险切入场景进行评估：在每个切入事件发生前立即施加干预，由RESPOND重新决策驾驶行为。与记录的人类驾驶行为相比，RESPOND在84.9%的场景中降低了后续风险，证明了其在真实驾驶条件下的实用可行性。这些结果凸显了RESPOND在自动驾驶、个性化驾驶辅助及主动风险缓解领域的应用潜力。