AutoVLA-D: Learn a Trajectory-Geometric Condition from Reasoning for Autonomous Driving VLA
Overview
This paper presents AutoVLA-D, a method that introduces TrajCond—a discrete trajectory-geometric condition—to enhance autoregressive VLA planning for autonomous driving. By representing the condition as a 1D-Brohan differential action trajectory and using a two-stage training framework, the approach simultaneously generates discrete and numeric trajectories. Experiments on NAVSIM navtest, nuScenes, and NeuroNCAP show that while discrete trajectories are less effective as direct outputs, they serve as more effective conditions for improving final continuous trajectory prediction in both closed-loop and open-loop driving scenarios. The code is available at https://github.com/ShivaTeam/AutoVLA-D.
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“d, consistently improving final continuous trajectory prediction across closed-loop and open-loop driving benchmarks. Code is available at https://github.com/ShivaTeam/AutoVLA-D.”
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- arXiv Robotics — research abstractsSignalAutoVLA-D: Learn a Trajectory-Geometric Condition from Reasoning for Autonomous Driving VLA
This paper presents AutoVLA-D, a method that introduces TrajCond—a discrete trajectory-geometric condition—to enhance autoregressive VLA planning for autonomous driving. By representing the condition as a 1D-Brohan differential action trajectory and using a two-stage training framework, the approach simultaneously generates discrete and numeric trajectories. Experiments on NAVSIM navtest, nuScenes, and NeuroNCAP show that while discrete trajectories are less effective as direct outputs, they serve as more effective conditions for improving final continuous trajectory prediction in both closed-loop and open-loop driving scenarios. The code is available at https://github.com/ShivaTeam/AutoVLA-D.
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