VECTOR-DRIVE uses shared self-attention with semantic-aware expert routing of tokens to VL and trajectory experts plus flow-matching action decoding to reach 88.91 driving score on Bench2Drive.
Drive- r1: Bridging reasoning and planning in vlms for autonomous driving IEEE ROBOTICS AND AUTOMATION LETTERS 9 with reinforcement learning
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VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving
VECTOR-DRIVE uses shared self-attention with semantic-aware expert routing of tokens to VL and trajectory experts plus flow-matching action decoding to reach 88.91 driving score on Bench2Drive.