LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration

Camiel Oerlemans, Bram Grooten, Michiel Braat, Alaa Alassi, Emilia Silvas +1 more
10/21/2024
cs.LGcs.AIcs.CV

No official code found. Best implementation: Cing2/LiMTR (6 stars, MIT, last commit 2024-10-22).

Abstract

Predicting the behavior of road users accurately is crucial to enable the safe operation of autonomous vehicles in urban or densely populated areas. Therefore, there has been a growing interest in time series motion prediction research, leading to significant advancements in state-of-the-art techniques in recent years. However, the potential of using LiDAR data to capture more detailed local features, such as a person's gaze or posture, remains largely unexplored. To address this, we develop a novel multimodal approach for motion prediction based on the PointNet foundation model architecture, incorporating local LiDAR features. Evaluation on the Waymo Open Dataset shows a performance improvement of 6.20% and 1.58% in minADE and mAP respectively, when integrated and compared with the previous state-of-the-art MTR. We open-source the code of our LiMTR model.

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Code Implementations(1)

Cing2/LiMTRCommunity100%
MIT

Code base of LiMTR architecture

61C++, CudaApr 12, 20241 years agoMIT

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Cite this paper

@article{oerlemans2024limtr,
  title  = {LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration},
  author = {Camiel Oerlemans and Bram Grooten and Michiel Braat and Alaa Alassi and Emilia Silvas and Decebal Constantin Mocanu},
  year   = {2024},
  eprint = {2410.15819},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.15819v1}
}

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