AI RESEARCH
AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
arXiv CS.CV
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ArXi:2601.01762v2 Announce Type: replace-cross Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes. While state-of-the-art (SOTA) methods use parallel planning architectures, they fail to explicitly couple speed decisions with agent behavior along the driving path, leading to suboptimal coordination. To address this, we propose a cascaded framework that transforms longitudinal planning from an independent prediction task into a path-conditioned reasoning process. On the model side, we.