KnowVal：知识增强与价值引导的自动驾驶系统
KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System
将开放世界感知与交通法规/防御性驾驶知识图谱通过 LLM 检索结合，并用人类偏好训练的价值模型对候选轨迹做可解释打分，可作为视觉端到端规划器中「规则约束+价值对齐」的即插即用评估模块。
原文依据 we construct a comprehensive driving knowledge graph that encodes traffic laws, defensive driving principles, and ethical norms
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉语言推理、驾驶知识与价值对齐对于先进的自动驾驶系统至关重要。然而，现有方法在很大程度上依赖数据驱动学习，难以通过模仿学习或有限的强化奖励捕捉决策背后的复杂逻辑。为解决这一问题，我们提出 KnowVal，一种新的自动驾驶系统，通过开放世界感知与知识检索的协同集成实现视觉语言推理。具体而言，我们构建了一个全面的驾驶知识图谱，编码交通法规、防御性驾驶原则与伦理规范，并辅以一个面向驾驶场景定制的高效 LLM 检索机制。此外，我们构建了一个人类偏好数据集并训练了一个价值模型，以引导可解释、价值对齐的轨迹评估。实验结果表明，我们的方法在显著提升规划性能的同时，仍与现有架构保持兼容。值得注意的是，KnowVal 在 nuScenes 上取得了最低的碰撞率，并在 Bench2Drive 与 NAVSIM 上取得了最优结果。
英文原文摘要 1141 字符
Visual-language reasoning, driving knowledge, and value alignment are essential for advanced autonomous driving systems. However, existing approaches largely rely on data-driven learning, making it difficult to capture the complex logic underlying decision-making through imitation or limited reinforcement rewards. To address this, we propose KnowVal, a new autonomous driving system that enables visual-language reasoning through the synergistic integration of open-world perception and knowledge retrieval. Specifically, we construct a comprehensive driving knowledge graph that encodes traffic laws, defensive driving principles, and ethical norms, complemented by an efficient LLM-based retrieval mechanism tailored for driving scenarios. Furthermore, we develop a human-preference dataset and train a Value Model to guide interpretable, value-aligned trajectory assessment. Experimental results show that our method substantially improves planning performance while remaining compatible with existing architectures. Notably, KnowVal achieves the lowest collision rate on nuScenes and state-of-the-art results on Bench2Drive and NAVSIM.