AI RESEARCH
Multimodal Action Diffusion for Robust End-to-End Autonomous Driving
arXiv CS.CV
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ArXi:2606.02105v1 Announce Type: new End-to-End Autonomous Driving (E2E-AD) systems have largely converged on predicting intermediate trajectory waypoints, delegating final control to hand-crafted controllers with GPS access. Direct control-signal prediction (outputting throttle, steer and brake in an end-to-end fashion) remains underexplored, and critically, the role of action multimodality in such systems is not well understood.