mlsanityData to 14 Oct 2026

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

arXiv 2410.15819 ↗First posted 21 Oct 2024cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 26 references that are in the map. First posted on arXiv 21 Oct 2024.

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22 of 26 references.

References · 22

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  4. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 537
  5. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 530
  6. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  7. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 420
  8. 2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 295
  9. 2022Wayformer: Motion Forecasting via Simple & Efficient Attention Networkscited by 280
  10. 2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 270
  11. 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
  12. 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
10 more
  1. 2021IntentNet: Learning to Predict Intention from Raw Sensor Datacited by 158
  2. 2023MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Queryingcited by 140
  3. 2022SWFormer: Sparse Window Transformer for 3D Object Detection in Point Cloudscited by 92
  4. 2022LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perceptioncited by 74
  5. 2023Optimal Linear Decay Learning Rate Schedules and Further Refinementscited by 43
  6. 2020MultiXNet: Multiclass Multistage Multimodal Motion Predictioncited by 31
  7. 2023WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecastingcited by 28
  8. 2023Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypointscited by 12
  9. 2023MGTR: Multi-Granular Transformer for Motion Prediction with LiDARcited by 9
  10. 2023Robustness Benchmark of Road User Trajectory Prediction Models for Automated Drivingcited by 1

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