mlsanityData to 14 Oct 2026

Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture

arXiv 2404.03789 ↗First posted 4 Apr 2024cs.CV cs.AIcited by 6

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Abstract (arXiv)

Safety and robustness are crucial factors in developing trustworthy autonomous vehicles. One essential aspect of addressing these factors is to equip vehicles with the capability to predict future trajectories for all moving objects in the surroundings and quantify prediction uncertainties. In this paper, we propose the Sequential Neural Variational Agent (SeNeVA), a generative model that describes the distribution of future trajectories for a single moving object. Our approach can distinguish Out-of-Distribution data while quantifying uncertainty and achieving competitive performance compared to state-of-the-art methods on the Argoverse 2 and INTERACTION datasets. Specifically, a 0.446 meters minimum Final Displacement Error, a 0.203 meters minimum Average Displacement Error, and a 5.35% Miss Rate are achieved on the INTERACTION test set. Extensive qualitative and quantitative analysis is also provided to evaluate the proposed model. Our open-source code is available at https://github.com/PurdueDigitalTwin/seneva.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 29 of its 45 references that are in the map. First posted on arXiv 4 Apr 2024.

Builds on

29 of 45 references.

References · 29

  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  3. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  4. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 732
  5. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 690
  6. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 530
  7. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 420
  8. 2020Learning Lane Graph Representations for Motion Forecastingcited by 382
  9. 2020TNT: Target-driveN Trajectory Predictioncited by 347
  10. 2018SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraintscited by 298
  11. 2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 295
  12. 2019Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networkscited by 229
17 more
  1. 2021Multimodal Motion Prediction with Stacked Transformerscited by 208
  2. 2017Social Attention: Modeling Attention in Human Crowdscited by 186
  3. 2021GOHOME: Graph-Oriented Heatmap Output for future Motion Estimationcited by 146
  4. 2019Multi-Agent Tensor Fusion for Contextual Trajectory Predictioncited by 131
  5. 2020A Review and Comparative Study on Probabilistic Object Detection in Autonomous Drivingcited by 115
  6. 2017Learning non-maximum suppressioncited by 113
  7. 2022HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encodingcited by 110
  8. 2018Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Drivingcited by 90
  9. 2020Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Drivingcited by 73
  10. 2021Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Drivingcited by 73
  11. 2023Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoderscited by 73
  12. 2018Probabilistic Object Detection: Definition and Evaluationcited by 55
  13. 2017Multimodal Probabilistic Model-Based Planning for Human-Robot Interactioncited by 50
  14. 2018Generative Modeling of Multimodal Multi-Human Behaviorcited by 24
  15. 2021Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Studycited by 7
  16. 2023Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervisioncited by 7
  17. 2024Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approachcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2025Toward Real-world BEV Perception: Depth Uncertainty Estimation via Gaussian Splattingcited by 20
  2. 2024Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecastingcited by 7
  3. 2025Rethinking Efficient Mixture-of-Experts for Remote Sensing Modality-Missing Classificationcited by 2
  4. 2025Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmapscited by 1
  5. 2025PEdger++: Practical Edge Detection via Assembling Cross Informationcited by 0
  6. 2026TaPD: Temporal-adaptive Progressive Distillation for Observation-Adaptive Trajectory Forecasting in Autonomous Drivingcited by 0