Summary of Hidden Biases Of End-to-end Driving Datasets, by Julian Zimmerlin et al.
Hidden Biases of End-to-End Driving Datasets
by Julian Zimmerlin, Jens Beißwenger, Bernhard Jaeger, Andreas Geiger, Kashyap Chitta
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a first attempt to apply end-to-end driving systems to the challenging CARLA Leaderboard 2.0, focusing on analyzing the training dataset rather than architectures or training strategies. The analysis reveals three key insights: expert style significantly affects downstream policy performance, frame weighting should not rely on simplistic criteria like class frequencies, and estimating frame changes can reduce the dataset size without removing important information. The authors’ model achieves state-of-the-art results on the map and sensors tracks of the 2024 CARLA Challenge and sets a new benchmark on the Bench2Drive test routes. Additionally, the paper uncovers a design flaw in the current evaluation metrics and proposes a modification for future challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers develop an end-to-end driving system that performs well on the challenging CARLA Leaderboard 2.0. Instead of trying out different architectures or training strategies, they focus on understanding how the training dataset affects the results. They find some surprising things! For example, the way the data was collected matters a lot. They also figure out how to make their model better by looking at the changes between frames in the video data. Their system is really good and sets new records on several tasks. |
