arXiv:2605.21061v1 Announce Type: cross Abstract: Existing Driving VLAs predict trajectories while largely ignoring their visual tokens -- a phenomenon we trace not to insufficient training but to a structurally ill-posed task formulation. We show that trajectory recovery, when viewed through the lens of inverse kinematics, requires both a current and a future visual state as boundary conditions; existing VLAs supply only the former, which encourages the model to shortcut through ego status and text commands alone. To address this, we re-design Driving VLA in the style of an inverse kinematics
The continuous development and refinement of Vision-Language Models (VLMs) for autonomous systems necessitate addressing fundamental limitations to improve real-world performance.
Improving the grounding of Driving VLAs will lead to more robust and reliable autonomous driving systems, accelerating their deployment and market adoption.
This research redefines how Driving VLAs interpret visual information for trajectory prediction, moving beyond simple input to more sophisticated 'boundary condition' integration, potentially unlocking more accurate control and safety.
- · Autonomous vehicle developers
- · AI research institutions
- · Robotics companies
- · Semiconductor manufacturers
- · Companies relying on less sophisticated VLM approaches
- · Traditional robotics control systems
Driving VLAs will become more adept at predicting complex trajectories by better integrating visual context.
Enhanced trajectory prediction capabilities could reduce the need for extensive human supervision in autonomous vehicles and robotic systems.
More reliable autonomous systems could accelerate the development of robotic last-mile delivery and automated logistics, impacting various sectors.
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Read at arXiv cs.AI