EvoDriveVLA: Evolving Autonomous Driving Vision-Language-Action Model via Collaborative Perception-Planning Distillation

• URL: http://arxiv.org/abs/2603.09465v2
• Date: Sat, 14 Mar 2026 03:54:24 GMT
• Title: EvoDriveVLA: Evolving Autonomous Driving Vision-Language-Action Model via Collaborative Perception-Planning Distillation
• Abstract summary: EvoDriveVLA is a novel collaborative perception-planning framework.<n>It integrates self-anchored perceptual constraints and oracle-guided trajectory optimization.<n>EvoDriveVLA achieves SOTA performance in open-loop evaluation and significantly enhances performance in closed-loop evaluation.
• Score: 58.84721000276226
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 oracle-guided 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, oracle-guided trajectory distillation employs a future-aware oracle teacher with coarse-to-fine trajectory refinement and Monte Carlo dropout sampling to produce high-quality trajectory candidates, thereby selecting the optimal trajectory to guide the student's prediction. EvoDriveVLA achieves SOTA performance in open-loop evaluation and significantly enhances performance in closed-loop evaluation. Our code is available at: https://github.com/hey-cjj/EvoDriveVLA.

Related papers

• Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs [28.861025642391155]
  We show that teacher models rationalize the revealed outcome rather than infer a decision from scene evidence.<n>Removing the GT trajectory eliminates this shortcut, but open-ended trajectory generation entangles high-level decision-making with precise geometric synthesis and low-level dynamics.<n>We propose Deferred Exposure of Future Trajectories for RLVR (DEFT-RLVR) to transform future trajectories from pre-decision anchors into post-decision verification targets.
  arXiv  Detail & Related papers  (2026-08-03T06:24:36Z)
• LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model [16.87741001074065]
  Vision-Language-Action (VLA) models have emerged as a promising framework for end-to-end autonomous driving.<n>Recent attempts to incorporate dense visual supervision via world modeling often overemphasize pixel-level image reconstruction.<n>We propose LVDrive, a Latent Visual representation enhanced VLA framework for autonomous driving.
  arXiv  Detail & Related papers  (2026-05-21T07:31:49Z)
• UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving [48.461267171124945]
  We propose UniDriveVLA, a Unified Driving Vision-Language-Action model for autonomous driving.<n>It comprises three experts for driving understanding, scene perception, and action planning, which are coordinated through masked joint attention.<n>It achieves state-of-the-art performance in open-loop evaluation on nuScenes and closed-loop evaluation on Bench2Drive.
  arXiv  Detail & Related papers  (2026-04-02T15:48:45Z)
• VISTA: Enhancing Visual Conditioning via Track-Following Preference Optimization in Vision-Language-Action Models [26.542479606920423]
  Vision-Language-Action (VLA) models have demonstrated strong performance across a wide range of robotic manipulation tasks.<n>Despite the success, extending large pretrained VLA models to the action space can induce vision-action misalignment.<n>We propose a training framework that explicitly strengthens visual conditioning in VLA models.
  arXiv  Detail & Related papers  (2026-02-04T20:59:29Z)
• DAP: A Discrete-token Autoregressive Planner for Autonomous Driving [34.32497598431514]
  We introduce DAP, a discrete-token autoregressive planner that jointly forecasts BEV semantics and ego trajectories.<n>We incorporate a reinforcement-learning-based fine-tuning, which preserves supervised behavior cloning priors while injecting reward-guided improvements.<n>DAP achieves state-of-the-art performance on open-loop metrics and delivers competitive closed-loop results on the NAVSIM benchmark.
  arXiv  Detail & Related papers  (2025-11-17T12:31:33Z)
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving [49.07731497951963]
  ReCogDrive is a novel Reinforced Cognitive framework for end-to-end autonomous driving.<n>We introduce a hierarchical data pipeline that mimics the sequential cognitive process of human drivers.<n>We then address the language-action mismatch by injecting the VLM's learned driving priors into a diffusion planner.
  arXiv  Detail & Related papers  (2025-06-09T03:14:04Z)
• SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving [51.47621083057114]
  SOLVE is an innovative framework that synergizes Vision-Language Models with end-to-end (E2E) models to enhance autonomous vehicle planning.<n>Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive interaction between VLM and E2E components.
  arXiv  Detail & Related papers  (2025-05-22T15:44:30Z)
• VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision [17.36342349850825]
  Vision-language models (VLMs) as teachers to enhance training by providing additional supervision.<n>VLM-AD achieves significant improvements in planning accuracy and reduced collision rates on the nuScenes dataset.
  arXiv  Detail & Related papers  (2024-12-19T01:53:36Z)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving.<n>Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner.<n>Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• Enhancing End-to-End Autonomous Driving with Latent World Model [78.22157677787239]
  We propose a novel self-supervised learning approach using the LAtent World model (LAW) for end-to-end driving.<n> LAW predicts future scene features based on current features and ego trajectories.<n>This self-supervised task can be seamlessly integrated into perception-free and perception-based frameworks.
  arXiv  Detail & Related papers  (2024-06-12T17:59:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.