Summary of End-to-end Driving in High-interaction Traffic Scenarios with Reinforcement Learning, by Yueyuan Li et al.
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning
by Yueyuan Li, Mingyang Jiang, Songan Zhang, Wei Yuan, Chunxiang Wang, Ming Yang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 This paper proposes a reinforcement learning (RL) approach for autonomous driving in dynamic and interactive traffic scenarios. The authors leverage RL’s ability to explore beyond predefined conditions, enabling the development of more effective driving policies. To address the challenge of extracting spatial and temporal features from high-dimensional observations, the proposed method uses a novel feature extractor that minimizes error accumulation over time. Additionally, an efficient training procedure is developed to guide large-scale RL models towards optimal policies without frequent failures during training. The paper evaluates its approach on various benchmarks, including the Carla simulation environment and the KITTI odometry dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine self-driving cars navigating through busy streets with other cars, pedestrians, and unexpected events. This paper develops a new way for self-driving cars to learn how to drive in these complex situations using a type of artificial intelligence called reinforcement learning. The approach helps the car figure out what works best by exploring different options and learning from its mistakes. It also has a special feature that helps reduce errors when processing information from cameras, lidars, and other sensors. This method is tested on simulated driving scenarios and real-world datasets to show it can improve the performance of self-driving cars. |
Keywords
* Artificial intelligence * Reinforcement learning
