EvoDriveVLA: Evolving Driving VLA Models via Collaborative Perception-Planning Distillation
Vision-Language-Action models have shown great promise for autonomous driving, yet they suffer from degraded perception after unfreezing the visual encoder and struggle with accumulated instability in long-term planning. To address these challenges, we propose EvoDriveVLA-a novel collaborative perception-planning distillation framework that integrates self-anchored perceptual constraints and future-informed trajectory optimization. Specifically, self-anchored visual distillation leverages self-anchor teacher to deliver visual anchoring constraints, regularizing student representations via trajectory-guided key-region awareness. In parallel, future-informed trajectory distillation employs ...
| Authors | Jiajun Cao, Xiaoan Zhang, Xiaobao Wei et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 March 2026 |
| Last updated at source | 11 May 2026 |
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