Paper ID: 2410.15819 • Published Oct 21, 2024
LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration
Camiel Oerlemans, Bram Grooten, Michiel Braat, Alaa Alassi, Emilia Silvas, Decebal Constantin Mocanu
TL;DR
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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.