Summary of Parkinge2e: Camera-based End-to-end Parking Network, From Images to Planning, by Changze Li et al.
ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning
by Changze Li, Ziheng Ji, Zhe Chen, Tong Qin, Ming Yang
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 neural-network-based method for autonomous parking uses imitation learning to perform end-to-end planning from RGB images to path planning by imitating human driving trajectories. The approach utilizes a target query encoder to fuse images and target features, and a transformer-based decoder to autoregressively predict future waypoints. Experimental results demonstrate an average parking success rate of 87.8% across four different real-world garages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new method for self-parking cars that works by copying how humans park their vehicles. They used a lot of data on human driving patterns to train a computer program that can see and follow the same steps as a human driver. The program is able to predict where the car will go next based on what it sees, and it’s really good at parking – 87.8% of the time, in fact! They tested it in real-world garages and it worked well. |
Keywords
» Artificial intelligence » Decoder » Encoder » Neural network » Transformer
