Explicit Geometric Chain-of-Thought for Vision-Language-Action in Autonomous Driving

arXiv:2610.10390 · cs.CV · Submitted 2026-10-07 · Read on arXiv

cs.CV

Submitted: 2026-10-07

Updated: 2026-10-07

Code: https://github.com/TabGuigui/GeoCoTDrive

Terminology

• Grounding — 84× in this paper · explained in 3 episodes
• Foundation Model — 18× in this paper · explained in 3 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Alignment — 6× in this paper · explained in 1 episode
• Foundation Models — 6× in this paper · explained in 7 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Two-stage training — 3× in this paper · explained in 2 episodes
• VLA Models — 3× in this paper · explained in 3 episodes
• Visual Grounding — 3× in this paper · explained in 2 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Driving Score — 2× in this paper
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Success Rate — 2× in this paper
• Trace — 2× in this paper · explained in 2 episodes
• Action Decoder — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Predictive Driver Model Score — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Two-Stage Training Strategy — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• Qwen Technical Report
• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
• Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects
• TrajDiff: End-to-end Autonomous Driving without Perception Annotation
• Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future
• G$^2$VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning
• Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
• Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model
• Thinking with Geometry: Active Geometry Integration for Spatial Reasoning
• SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving
• SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
• UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving
• Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
• Depth Anything 3: Recovering the Visual Space from Any Views
• Spatial Reasoning in Multimodal Large Language Models: A Survey of Tasks, Benchmarks and Methods
• DrivePI: Spatial-aware 4D MLLM for Unified Autonomous Driving Understanding, Perception, Prediction and Planning
• LaST-VLA: Thinking in Latent Spatio-Temporal Space for Vision-Language-Action in Autonomous Driving
• AdaThinkDrive: Adaptive Thinking via Reinforcement Learning for Autonomous Driving
• SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model

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