Paper#Autonomous Driving, Vision-Language Models, Trajectory Planning🔬 ResearchAnalyzed: Jan 3, 2026 19:25
ColaVLA: Cognitive Latent Reasoning for Autonomous Driving
Analysis
This paper addresses key challenges in VLM-based autonomous driving, specifically the mismatch between discrete text reasoning and continuous control, high latency, and inefficient planning. ColaVLA introduces a novel framework that leverages cognitive latent reasoning to improve efficiency, accuracy, and safety in trajectory generation. The use of a unified latent space and hierarchical parallel planning is a significant contribution.
Key Takeaways
- •Proposes ColaVLA, a unified vision-language-action framework.
- •Uses cognitive latent reasoning to bridge the gap between text reasoning and continuous control.
- •Employs a hierarchical, parallel trajectory decoder for efficiency.
- •Achieves state-of-the-art performance on the nuScenes benchmark.
Reference
“ColaVLA achieves state-of-the-art performance in both open-loop and closed-loop settings with favorable efficiency and robustness.”