FocusDrive: Reasoning with Visual Focus for Autonomous Driving

arXiv:2609.33190 · cs.CV, cs.RO · Submitted 2026-09-27 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/autonomousvision/navsim

Terminology

• AUC — 22× in this paper · explained in 2 episodes
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• GRPO — 4× in this paper · explained in 6 episodes
• Ablation — 3× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 3× in this paper
• KL Regularization — 3× in this paper · explained in 2 episodes
• Multimodal Reasoning — 3× in this paper · explained in 5 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Grounding — 2× in this paper · explained in 3 episodes
• Latent Reasoning — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptive Reasoning — 1× in this paper · explained in 2 episodes
• Agent Interactions — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• International Conference on Pattern Recognition — 1× in this paper
• Joint Attention — 1× in this paper · explained in 2 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Driving risk emerges from the required two-dimensional joint evasive acceleration
• DriveFuture: Future-Aware Latent World Models for Autonomous Driving
• EMMA: End-to-End Multimodal Model for Autonomous Driving
• DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving
• Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
• Human Visual Attention Prediction Boosts Learning & Performance of Autonomous Driving Agents
• Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens
• Towards Pixel-Level VLM Perception via Simple Points Prediction
• Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving

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