mlsanityData to 7 Oct 2026

Physics Constrained Motion Prediction with Uncertainty Quantification

arXiv 2302.01060 ↗First posted 2 Feb 2023cs.RO cs.CV cs.LGcited by 11

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Abstract (arXiv)

Predicting the motion of dynamic agents is a critical task for guaranteeing the safety of autonomous systems. A particular challenge is that motion prediction algorithms should obey dynamics constraints and quantify prediction uncertainty as a measure of confidence. We present a physics-constrained approach for motion prediction which uses a surrogate dynamical model to ensure that predicted trajectories are dynamically feasible. We propose a two-step integration consisting of intent and trajectory prediction subject to dynamics constraints. We also construct prediction regions that quantify uncertainty and are tailored for autonomous driving by using conformal prediction, a popular statistical tool. Physics Constrained Motion Prediction achieves a 41% better ADE, 56% better FDE, and 19% better IoU over a baseline in experiments using an autonomous racing dataset.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 2 Feb 2023.

Builds on

12 of 28 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  3. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  4. 2019Conformalized Quantile Regressioncited by 620
  5. 2020Learning Lane Graph Representations for Motion Forecastingcited by 380
  6. 2022Wayformer: Motion Forecasting via Simple & Efficient Attention Networkscited by 279
  7. 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
  8. 2022Safe Planning in Dynamic Environments using Conformal Predictioncited by 112
  9. 2021THOMAS: Trajectory Heatmap Output with learned Multi-Agent Samplingcited by 101
  10. 2022On Adversarial Robustness of Trajectory Prediction for Autonomous Vehiclescited by 89
  11. 2022Adaptive Conformal Prediction for Motion Planning among Dynamic Agentscited by 46
  12. 2021Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modelingcited by 40

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 4; 2025: 1; 2026: 2.

11 papers.

  1. 2023Credal Bayesian Deep Learningcited by 45
  2. 2023Conformal Prediction Regions for Time Series using Linear Complementarity Programmingcited by 33
  3. 2023Conformal Contextual Robust Optimizationcited by 25
  4. 2025Four Principles for Physically Interpretable World Modelscited by 10
  5. 2023Multi-Modal Conformal Prediction Regions with Simple Structures by Optimizing Convex Shape Templatescited by 4
  6. 2024Physically Interpretable World Models via Weakly Supervised Representation Learningcited by 3
  7. 2024Single Trajectory Conformal Predictioncited by 1
  8. 2026A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Drivingcited by 1
  9. 2024Towards Safe and Reliable Autonomous Driving: Dynamic Occupancy Set Predictioncited by 0
  10. 2024Differentiable Predictive Control for Large-Scale Urban Road Networkscited by 0
  11. 2026AdaptNC: Adaptive Nonconformity Scores for Conformal Prediction under Distribution Shiftcited by 0