Summary of Hint-ad: Holistically Aligned Interpretability in End-to-end Autonomous Driving, by Kairui Ding et al.
Hint-AD: Holistically Aligned Interpretability in End-to-End Autonomous Driving
by Kairui Ding, Boyuan Chen, Yuchen Su, Huan-ang Gao, Bu Jin, Chonghao Sima, Wuqiang Zhang, Xiaohui Li, Paul Barsch, Hongyang Li, Hao Zhao
First submitted to arxiv on: 10 Sep 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 In this paper, researchers tackle the challenge of interpretability in end-to-end architectures for autonomous driving (AD). They propose Hint-AD, an integrated AD-language system that generates natural language aligned with the intermediate outputs of the AD model. This approach improves interpretability by establishing a connection between language and the AD system’s outputs. The authors demonstrate state-of-the-art results on driving explanation, 3D dense captioning, and command prediction tasks using nuScenes dataset. Additionally, they introduce a human-labeled dataset, Nu-X, for further study on the driving explanation task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hint-AD is a new way to explain how self-driving cars work. It connects natural language with the car’s understanding of its surroundings, making it easier for humans to understand what the car is doing. The system does this by generating text that aligns with the car’s thoughts and plans. Hint-AD performs better than other systems on tasks like describing what a car sees and responding to commands. It also creates a new dataset, Nu-X, for studying how cars can explain their actions. |
