Latent-Centroid Steering: Single-Pass Classifier-Free Guidance for Command-Aligned Autonomous Driving

arXiv:2608.00237 · cs.CV, cs.RO · Submitted 2026-07-31 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-07-31

Updated: 2026-09-16

Comments: IROS 2026

Code: https://github.com/codingmlinprocess/LCS

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Steering — 64× in this paper · explained in 2 episodes
• Navigation Command — 16× in this paper
• Latent-Centroid Steering — 11× in this paper
• Alignment — 9× in this paper · explained in 1 episode
• Driving Score — 8× in this paper
• Benchmark — 7× in this paper · explained in 2 episodes
• Success Rate — 7× in this paper
• Latent Space — 5× in this paper · explained in 7 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Action Space — 3× in this paper · explained in 1 episode
• Clustering — 3× in this paper · explained in 2 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Multimodal Inputs — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• World Model — 2× in this paper · explained in 7 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptive Steering — 1× in this paper · explained in 1 episode
• Alignment Problem — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• PID Controller — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Target Point — 1× in this paper
• Transformer — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
• EMMA: End-to-End Multimodal Model for Autonomous Driving
• MindDriver: Introducing Progressive Multimodal Reasoning for Autonomous Driving
• DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
• NoRD: A Data-Efficient Vision-Language-Action Model that Drives without Reasoning
• HERMES: A Holistic End-to-End Risk-Aware Multimodal Embodied System with Vision-Language Models for Long-Tail Autonomous Driving
• Classifier-Free Diffusion Guidance
• A Language Agent for Autonomous Driving
• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
• Generative Scenario Rollouts for End-to-End Autonomous Driving
• UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving
• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
• OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
• GPT-Driver: Learning to Drive with GPT
• VILTA: A VLM-in-the-Loop Adversary for Enhancing Driving Policy Robustness
• Natural Language Instructions for Scene-Responsive Human-in-the-Loop Motion Planning in Autonomous Driving using Vision-Language-Action Models
• Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning
• DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking
• Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

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