DH-VLM: Dual-Horizon Cooperative Latent Reasoning for Autonomous Driving

Large-scale language models for autonomous driving enableenhanced global understanding and long-horizon planning. However, whendeployed in isolated vehicles, limited sensing range and occlusions re-strict reliable decision-making, and the substantial computational andlatency overhead makes on-board deployment impractical. Cooperativedriving provides a potential solution by leveraging external agents forinformation exchange, but existing methods remain limited in semanticreasoning capability under practical constraints. To address these chal-lenges, we propose DH-VLM, a dual-horizon cooperative latent reasoningframework that enables asymmetric semantic cooperation between theinfrastructure and ego vehicle. The infrastructure aggregates multi-layerhidden states to form a global-reasoning horizon latent guidance, whichis integrated into the ego model through an Infrastructure-Driven LatentEvolution mechanism for conditional latent refinement. This enables theego vehicle to leverage long-range contextual understanding while pre-serving autonomous decision-making within its local planning horizon.Furthermore, we construct a cooperation-oriented question–answer (QA)dataset covering fundamental scene understanding and ego-personalizedcomprehension to support counterfactual and safety-aware reasoning.Extensive experiments demonstrate that DH-VLM achieves state-of-the-art planning performance, outperforming the previous state of the art by14.6% in L2 error and 26.9% in collision rate. Compared with query-basedend-to-end cooperative driving methods, our approach reduces the com-munication cost by 57.3% and GPU memory usage by 25.5%, while main-taining strong robustness against infrastructure guidance errors, provid-ing a practical and robust paradigm for cooperative autonomous driving.