TaCarla: New Comprehensive Dataset for End-to-End Autonomous Driving Research
Researchers have created TaCarla, a new dataset containing over 2.85 million frames from CARLA simulation for autonomous driving research. The dataset addresses limitations in existing datasets by combining perception and planning tasks with diverse scenarios and proper closed-loop evaluation capabilities. This resource aims to advance autonomous driving research by supporting multiple tasks including object detection, traffic light recognition, and prediction models.
TaCarla is a newly collected dataset comprising over 2.85 million frames generated using the CARLA simulation environment, specifically designed to support end-to-end autonomous driving research. The dataset was created to address significant gaps in existing autonomous driving datasets, which typically either lack planning data when they include perception information, or consist primarily of forward-driving sequences with limited behavioral diversity. TaCarla supports multiple research tasks including dynamic object detection, lane divider detection, centerline detection, traffic light recognition, prediction tasks, and visual language action models. The dataset is built on CARLA Leaderboard 2.0 challenge scenarios, which provide diverse long-tail problem scenarios, and includes both open-loop and closed-loop evaluation setups. Additionally, the researchers provide rarity scores to help understand how frequently different driving states occur in the dataset, enhancing its utility for training and evaluating autonomous driving models.
What's missing
The paper does not specify the exact sensor configurations included in the dataset, the geographic or scenario diversity represented in the 2.85 million frames, baseline performance metrics for models trained on TaCarla, or the timeline and availability for public access to the dataset.
What different sources said
- arXiv cs.AICenter
TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving
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