llmbev/talk2bev
Talk2BEV: Language-Enhanced Bird's Eye View Maps (ICRA'24)
This project helps autonomous vehicle engineers interpret complex driving scenes using natural language. It takes bird's-eye view (BEV) maps, which are common in self-driving systems, and allows you to ask questions about them in plain English. The output is answers that help understand traffic actor intentions and make decisions based on visual information. Autonomous vehicle developers and safety engineers would use this to enhance scene understanding.
119 stars. No commits in the last 6 months.
Use this if you need to perform advanced visual and spatial reasoning on autonomous driving BEV maps using natural language queries, without needing to train for specific object categories.
Not ideal if your primary need is for a system that relies solely on predefined, closed sets of object categories and driving scenarios without natural language interaction.
Stars
119
Forks
11
Language
Python
License
BSD-3-Clause
Category
Last pushed
Nov 04, 2024
Commits (30d)
0
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