Summary of Limtr: Time Series Motion Prediction For Diverse Road Users Through Multimodal Feature Integration, by Camiel Oerlemans et al.
LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration
by Camiel Oerlemans, Bram Grooten, Michiel Braat, Alaa Alassi, Emilia Silvas, Decebal Constantin Mocanu
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 novel approach for motion prediction in autonomous vehicles using LiDAR data. The authors develop a multimodal model based on the PointNet architecture, incorporating local features such as gaze and posture. They evaluate their method on the Waymo Open Dataset, achieving improvements of 6.20% and 1.58% in minADE and mAP respectively compared to the previous state-of-the-art MTR. The code for their LiMTR model is open-sourced. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make self-driving cars safer by better predicting what people will do on roads. It uses special sensors called LiDAR to get more detailed information about how people move and behave. The researchers create a new way of using this data, which improves the accuracy of predictions by 6.20% and 1.58%. This can help prevent accidents and make cities safer. |
