Summary of Drivingdojo Dataset: Advancing Interactive and Knowledge-enriched Driving World Model, by Yuqi Wang et al.
DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model
by Yuqi Wang, Ke Cheng, Jiawei He, Qitai Wang, Hengchen Dai, Yuntao Chen, Fei Xia, Zhaoxiang Zhang
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 DrivingDojo, a novel dataset, is designed to train interactive world models that can simulate complex driving dynamics. Unlike existing datasets, DrivingDojo provides a comprehensive set of video clips featuring diverse driving maneuvers, multi-agent interactions, and open-world driving knowledge. This allows for the development of more advanced world models capable of modeling real-world driving scenarios. The paper also introduces an action instruction following (AIF) benchmark to evaluate the performance of these models in generating future predictions controlled by human actions. The proposed dataset demonstrates its effectiveness in improving the accuracy of these predictions, making it a valuable resource for researchers and developers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to predict what will happen next in a video game or movie. Scientists are working on creating computers that can do just that. They’ve created a new dataset called DrivingDojo that helps them train these super smart computer models. This dataset has lots of videos showing different driving scenarios, like cars turning corners and merging onto highways. The goal is to make the computer models so good they can simulate real-world driving. The scientists also came up with a way to test how well these models work by giving them instructions on what actions to take next. |
