Summary of Toposd: Topology-enhanced Lane Segment Perception with Sdmap Prior, by Sen Yang and Minyue Jiang and Ziwei Fan and Xiaolu Xie and Xiao Tan and Yingying Li and Errui Ding and Liang Wang and Jingdong Wang
TopoSD: Topology-Enhanced Lane Segment Perception with SDMap Prior
by Sen Yang, Minyue Jiang, Ziwei Fan, Xiaolu Xie, Xiao Tan, Yingying Li, Errui Ding, Liang Wang, Jingdong Wang
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel approach to training autonomous driving perception models using standard definition maps (SDMaps) to improve long-range perception and reduce reliance on high-definition maps (HDMaps). The model encodes SDMap elements into neural spatial map representations and instance tokens, which are then incorporated as prior information to enhance bird’s-eye-view features for lane geometry and topology decoding. The approach is tested on the OpenLane-V2 dataset, outperforming state-of-the-art methods by a significant margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed an AI model that helps self-driving cars better understand road structures from above. This is useful because current maps are expensive to create and maintain. The new method uses simpler maps called SDMaps and combines them with information from the car’s cameras. This allows the car to make more accurate predictions about lanes, roads, and intersections. The approach was tested on a large dataset and outperformed existing methods. |
