mlsanityData to 7 Oct 2026

MultiXNet: Multiclass Multistage Multimodal Motion Prediction

arXiv 2006.02000 ↗First posted 3 Jun 2020cs.CV cs.LG eess.IVcited by 31

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Abstract (arXiv)

One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future. To address this task we propose MultiXNet, an end-to-end approach for detection and motion prediction based directly on lidar sensor data. This approach builds on prior work by handling multiple classes of traffic actors, adding a jointly trained second-stage trajectory refinement step, and producing a multimodal probability distribution over future actor motion that includes both multiple discrete traffic behaviors and calibrated continuous position uncertainties. The method was evaluated on large-scale, real-world data collected by a fleet of SDVs in several cities, with the results indicating that it outperforms existing state-of-the-art approaches.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2020.

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Citing papers per year: 2021: 10; 2022: 6; 2023: 7; 2024: 5; 2025: 3.

31 papers.

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  1. 2022Planning-oriented Autonomous Drivingcited by 1,068
  2. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  3. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 372
  4. 2021FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Camerascited by 239
  5. 2022ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planningcited by 63
  6. 2022StretchBEV: Stretching Future Instance Prediction Spatially and Temporallycited by 47
  7. 2021PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulationcited by 29
  8. 2022Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Predictioncited by 28
  9. 2023Learning and Aggregating Lane Graphs for Urban Automated Drivingcited by 20
  10. 2021Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertaintycited by 19
  11. 2021MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Datacited by 18
  12. 2024DeTra: A Unified Model for Object Detection and Trajectory Forecastingcited by 15
19 more
  1. 2022StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Drivingcited by 14
  2. 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
  3. 2023Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypointscited by 12
  4. 2023VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformercited by 10
  5. 2021Self-Supervised Action-Space Prediction for Automated Drivingcited by 9
  6. 2023MGTR: Multi-Granular Transformer for Motion Prediction with LiDARcited by 9
  7. 2024MoST: Multi-modality Scene Tokenization for Motion Predictioncited by 8
  8. 2024M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Drivingcited by 7
  9. 2024Joint Perception and Prediction for Autonomous Driving: A Surveycited by 7
  10. 2023Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detectioncited by 6
  11. 2025Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Drivingcited by 5
  12. 2021Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Modelscited by 3
  13. 2021Convolutions for Spatial Interaction Modelingcited by 2
  14. 2023Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene Flowcited by 1
  15. 2023LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learningcited by 1
  16. 2025To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Softwarecited by 1
  17. 2021Motion Prediction Performance Analysis for Autonomous Driving Systems and the Effects of Tracking Noisecited by 0
  18. 2021Trajectory Prediction using Generative Adversarial Network in Multi-Class Scenarioscited by 0
  19. 2024LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integrationcited by 0