用知识图谱和价值观引导，让自动驾驶更懂交通规则与伦理。
KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System
- 融合驾驶知识图谱与语言推理，提升决策逻辑性。
- 在nuScenes上碰撞率最低，多场景性能达顶尖水平。
- 适合研究可解释、安全合规的自动驾驶系统者。
视觉-语言推理、驾驶知识与价值对齐是先进自动驾驶系统的关键。现有方法主要依赖数据驱动学习，难以通过模仿或有限强化奖励捕捉复杂决策逻辑。为此，我们提出KnowVal，一种通过开放世界感知与知识检索协同整合实现视觉-语言推理的新系统。具体而言，构建了涵盖交通法规、防御性驾驶原则及伦理规范的综合驾驶知识图谱，并设计了面向驾驶场景的高效基于大模型的检索机制。此外，我们构建了人类偏好数据集并训练价值模型，以指导可解释、价值对齐的轨迹评估。实验表明，该方法显著提升规划性能，且兼容现有架构。尤为突出的是，KnowVal在nuScenes上碰撞率最低，在Bench2Drive和NVISIM上达到最先进水平。
原文摘要 · Abstract (English)
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 NVISIM.
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