ReVAD: From Imitation to Reasoning in Vectorized Autonomous Driving via Latent Space Search

Abstract

Vectorized end-to-end (E2E) autonomous driving models predominantly rely on reactive imitation learning. By failing to model underlying environmental dynamics, these agents lack counterfactual reasoning capabilities and struggle with uncertainty in out-of-distribution (OOD) scenarios. To bridge the gap between reactive imitation and deliberative decision-making, we introduce Reasoning-enhanced VAD (ReVAD), empowering agents with a “System 2” cognitive framework via Latent Space Search. Unlike computationally expensive pixel-level world models, ReVAD integrates a probabilistic Token Dynamics Model with Latent Monte Carlo Tree Search to perform efficient lookahead planning entirely within a sparse semantic token space. By simulating future states to evaluate risk and utilizing imitation policies solely as search priors, ReVAD effectively mitigates the distribution shift inherent in pure imitation learning, demonstrating significantly improved robustness and safety in high-uncertainty environments.