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TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving
arXiv – CS AI|Tugrul Gorgulu, Atakan Dag, M. Esat Kalfaoglu, Halil Ibrahim Kuru, Baris Can Cam, Ozsel Kilinc||1 views
🤖AI Summary
Researchers have released TaCarla, a comprehensive dataset containing over 2.85 million frames from CARLA simulation environment designed for end-to-end autonomous driving research. The dataset addresses limitations in existing autonomous driving datasets by providing both perception and planning data with diverse behavioral scenarios for comprehensive model training and evaluation.
Key Takeaways
- →TaCarla dataset contains over 2.85 million frames collected using CARLA simulation environment for autonomous driving research.
- →The dataset supports multiple tasks including planning, object detection, lane detection, traffic light recognition, and visual language action models.
- →Existing autonomous driving datasets are often incomplete, lacking either perception or planning data with limited behavioral diversity.
- →The dataset is designed for both open-loop and closed-loop evaluation setups to better assess autonomous driving models.
- →Researchers provide numerical rarity scores to help understand the frequency of different driving scenarios in the dataset.
#autonomous-driving#dataset#carla#simulation#machine-learning#computer-vision#planning#perception#end-to-end
Read Original →via arXiv – CS AI
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