Summary of Automatic Parking Planning Control Method Based on Improved A* Algorithm, by Yuxuan Zhao
Automatic parking planning control method based on improved AuthorLineProcess.function algorithm
by Yuxuan Zhao
First submitted to arxiv on: 24 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 proposed algorithm is an improvement over traditional planning algorithms for autonomous driving, specifically designed to handle high real-time, precision, and trajectory quality requirements for automatic parking. The A* algorithm serves as a foundation, enhanced by Model Predictive Control (MPC) as the control module. The improvements include optimizing heuristic functions, binary heap optimization, and bidirectional search, as well as dynamically loading obstacles and considering the vehicle’s volume during planning. Additionally, neighborhood expansion and Bezier curve optimization methods are used to enhance trajectory quality. The algorithm is evaluated using real driving environment perception results converted into maps, demonstrating its effectiveness in meeting special requirements for automatic parking under local maps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way for self-driving cars to park automatically. It’s like a game where the car plans a route and then follows it to find a parking spot. The algorithm uses some fancy math and computer programming to make sure the car gets to the right place quickly and safely. It also takes into account things like obstacles on the road and the shape of the parking space. The results show that this method is very good at finding a parking spot and can even handle tricky situations. |
Keywords
» Artificial intelligence » Optimization » Precision
