融合点云局部特征，提升多类型路权使用者轨迹预测精度
LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration
- 基于PointNet架构，融合激光雷达的局部空间特征进行轨迹预测
- 在Waymo数据集上，minADE降低6.20%，mAP提升1.58%
- 适合关注自动驾驶行为预测与多模态感知的研究者
准确预测道路使用者的行为对于实现自动驾驶车辆在城市或高密度区域的安全运行至关重要。近年来，时间序列运动预测研究取得显著进展。然而，利用激光雷达数据捕捉更精细的局部特征（如人体姿态、视线方向）的潜力尚未被充分挖掘。为此，我们提出一种基于PointNet基础模型架构的新型多模态运动预测方法，融入了局部激光雷达特征。在Waymo Open Dataset上的评估显示，相较于先前的最先进模型MTR，本方法在minADE和mAP上分别提升了6.20%和1.58%。相关代码已开源。
原文摘要 · Abstract (English)
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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