Planning in the Backbone: DiffAdapterVLA for Native Continuous Trajectory Generation with Driving VLMs
cs.RO, cs.AI
Submitted: 2026-09-14
Updated: 2026-09-22
Comments: 21 pages, 8 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
- Devil is in Narrow Policy: Unleashing Exploration in Driving VLA Models
- A Survey for Foundation Models in Autonomous Driving
- CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving
- MindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving
- Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
- Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
- GPT-Driver: Learning to Drive with GPT
- Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
- Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving
- dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought
- BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving
- DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving
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