AI RESEARCH
Senna-2: Aligning VLM and End-to-End Driving Policy for Consistent Decision Making and Planning
arXiv CS.CV
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ArXi:2603.11219v1 Announce Type: new Vision-language models (VLMs) enhance the planning capability of end-to-end (E2E) driving policy by leveraging high-level semantic reasoning. However, existing approaches often overlook the dual-system consistency between VLM's high-level decision and E2E's low-level planning. As a result, the generated trajectories may misalign with the intended driving decisions, leading to weakened top-down guidance and decision-following ability of the system.