pqh22/ColaVLA
[CVPR2026] ColaVLA: Leveraging Cognitive Latent Reasoning for Hierarchical Parallel Trajectory Planning in Autonomous Driving
This project offers an advanced trajectory planning system for self-driving vehicles, allowing them to make faster, more reliable decisions. It takes in real-time environmental data (like what a car's sensors 'see') and outputs a precise, multi-scale driving path in a single step. The primary users are autonomous driving engineers and researchers focused on developing safer and more efficient self-driving technology.
Use this if you need to improve the real-time decision-making and planning capabilities of an autonomous vehicle system, especially for complex or high-stakes driving scenarios.
Not ideal if you are looking for a system that relies solely on human-readable text-based reasoning for planning decisions.
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27
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1
Language
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License
MIT
Category
Last pushed
Feb 21, 2026
Commits (30d)
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