# Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles arXiv: https://arxiv.org/abs/2207.00788 ยท HTML: https://mlsanity.com/paper/2207.00788/ ## Key facts - Cited by 22 papers in the map (data through 14 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. ## Placed in - End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/) ## Builds on (most cited first, 12 of 12) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019) - [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020) - [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019) - [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021) - [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020) - [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021) - [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021) - [On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors](https://mlsanity.com/paper/2103.12474/) (2021) - [Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review](https://mlsanity.com/paper/1912.11676/) (2019) - [Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks](https://mlsanity.com/paper/2109.07365/) (2021) - [Safe planning and control under uncertainty for self-driving](https://mlsanity.com/paper/2010.11063/) (2020) ## Built on by (most cited first, 22 of 22) - [Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios](https://mlsanity.com/paper/2212.11419/) (2022) - [CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Driving](https://mlsanity.com/paper/2408.10845/) (2024) - [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022) - [Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigation](https://mlsanity.com/paper/2501.06693/) (2025) - [Dynamically Conservative Self-Driving Planner for Long-Tail Cases](https://mlsanity.com/paper/2305.07497/) (2023) - [Visual Data-Type Understanding does not emerge from Scaling Vision-Language Models](https://mlsanity.com/paper/2310.08577/) (2023) - [Identify, Estimate and Bound the Uncertainty of Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2305.07487/) (2023) - [Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network](https://mlsanity.com/paper/2407.18551/) (2024) - [CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs](https://mlsanity.com/paper/2309.09844/) (2023) - [AMD: Adaptive Momentum and Decoupled Contrastive Learning Framework for Robust Long-Tail Trajectory Prediction](https://mlsanity.com/paper/2507.01801/) (2025) - [Den-TP: A Density-Balanced Data Curation and Evaluation Framework for Trajectory Prediction](https://mlsanity.com/paper/2409.17385/) (2024) - [ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving](https://mlsanity.com/paper/2603.15221/) (2026) - [Critical Example Mining for Vehicle Trajectory Prediction using Flow-based Generative Models](https://mlsanity.com/paper/2410.16083/) (2024) - [CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/2503.00783/) (2025) - [PIE: Perception and Interaction Enhanced End-to-End Motion Planning for Autonomous Driving](https://mlsanity.com/paper/2509.18609/) (2025) - [GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulation](https://mlsanity.com/paper/2410.13514/) (2024) - [Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving](https://mlsanity.com/paper/2502.21134/) (2025) - [Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions](https://mlsanity.com/paper/2505.12327/) (2025) - [TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning](https://mlsanity.com/paper/2509.13579/) (2025) - [AdaSports-Traj: Role- and Domain-Aware Adaptation for Multi-Agent Trajectory Modeling in Sports](https://mlsanity.com/paper/2509.16095/) (2025) - [A Trajectory Generator for High-Density Traffic and Diverse Agent-Interaction Scenarios](https://mlsanity.com/paper/2510.02627/) (2025) - [SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehicles](https://mlsanity.com/paper/2604.04573/) (2026) ## 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.