mlsanityData to 7 Oct 2026

Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles

arXiv 2207.00788 ↗First posted 2 Jul 2022cs.AIcited by 22

On the map: End-to-end autonomous driving → End-to-end system architecture

Abstract (arXiv)

A typical trajectory planner of autonomous driving commonly relies on predicting the future behavior of surrounding obstacles. Recently, deep learning technology has been widely adopted to design prediction models due to their impressive performance. However, such models may fail in the "long-tail" driving cases where the training data is sparse or unavailable, leading to planner failures. To this end, this work proposes a trajectory planner to consider the prediction model uncertainty arising from insufficient data for safer performance. Firstly, an ensemble network structure estimates the prediction model's uncertainty due to insufficient training data. Then a trajectory planner is designed to consider the worst-case arising from prediction uncertainty. The results show that the proposed method can improve the safety of trajectory planning under the prediction uncertainty caused by insufficient data. At the same time, with sufficient data, the framework will not lead to overly conservative results. This technology helps to improve the safety and reliability of autonomous vehicles under the long-tail data distribution of the real world.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 12 of its 27 references that are in the map. First posted on arXiv 2 Jul 2022.

Builds on

12 of 27 references.

References · 12

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  3. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
  4. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
  5. 2021Deep Long-Tailed Learning: A Surveycited by 416
  6. 2020TNT: Target-driveN Trajectory Predictioncited by 345
  7. 2021Ensemble deep learning: A reviewcited by 283
  8. 2021Delving into Deep Imbalanced Regressioncited by 157
  9. 2021On Exposing the Challenging Long Tail in Future Prediction of Traffic Actorscited by 48
  10. 2019Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Reviewcited by 31
  11. 2021Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networkscited by 14
  12. 2020Safe planning and control under uncertainty for self-drivingcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 5; 2025: 9; 2026: 2.

22 papers.

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  1. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarioscited by 124
  2. 2024CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Drivingcited by 74
  3. 2022Behavioral Intention Prediction in Driving Scenes: A Surveycited by 30
  4. 2025Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigationcited by 30
  5. 2023Dynamically Conservative Self-Driving Planner for Long-Tail Casescited by 13
  6. 2023Visual Data-Type Understanding does not emerge from Scaling Vision-Language Modelscited by 9
  7. 2023Identify, Estimate and Bound the Uncertainty of Reinforcement Learning for Autonomous Drivingcited by 6
  8. 2024Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Networkcited by 6
  9. 2023CC-SGG: Corner Case Scenario Generation using Learned Scene Graphscited by 4
  10. 2025AMD: Adaptive Momentum and Decoupled Contrastive Learning Framework for Robust Long-Tail Trajectory Predictioncited by 4
  11. 2024Den-TP: A Density-Balanced Data Curation and Evaluation Framework for Trajectory Predictioncited by 3
  12. 2026ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Drivingcited by 3
10 more
  1. 2024Critical Example Mining for Vehicle Trajectory Prediction using Flow-based Generative Modelscited by 1
  2. 2025CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous Drivingcited by 1
  3. 2025PIE: Perception and Interaction Enhanced End-to-End Motion Planning for Autonomous Drivingcited by 1
  4. 2024GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulationcited by 0
  5. 2025Dynamically Local-Enhancement Planner for Large-Scale Autonomous Drivingcited by 0
  6. 2025Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictionscited by 0
  7. 2025TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learningcited by 0
  8. 2025AdaSports-Traj: Role- and Domain-Aware Adaptation for Multi-Agent Trajectory Modeling in Sportscited by 0
  9. 2025A Trajectory Generator for High-Density Traffic and Diverse Agent-Interaction Scenarioscited by 0
  10. 2026SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehiclescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Risk-Informed Diffusion Transformer for Long-Tail Trajectory Prediction in the Crash Scenario
  2. 2024AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction
  3. 2022Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts
  4. 2023Navigating Uncertainty: The Role of Short-Term Trajectory Prediction in Autonomous Vehicle Safety
  5. 2019Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections
  6. 2024Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving
  7. 2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions
  8. 2026Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving