KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System

TL;DR

KnowVal is an autonomous driving system that integrates visual-language reasoning, comprehensive driving knowledge, and value alignment to improve safety and decision-making, achieving state-of-the-art results and low collision rates.

Contribution

It introduces a knowledge graph and a value-guided framework for autonomous driving, enhancing interpretability and performance over existing data-driven methods.

Findings

Lowest collision rate on nuScenes

State-of-the-art results on Bench2Drive

Effective knowledge and value integration

Abstract

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.