# Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles

arXiv: https://arxiv.org/abs/2207.12380 · HTML: https://mlsanity.com/paper/2207.12380/

## Key facts

- Cited by 11 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 31 of its 48 references that are in the map.
- First posted on arXiv 25 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, 30 of 31)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [Scaling Out-of-Distribution Detection for Real-World Settings](https://mlsanity.com/paper/1911.11132/) (2019)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [End-to-end Interpretable Neural Motion Planner](https://mlsanity.com/paper/2101.06679/) (2021)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [Baidu Apollo EM Motion Planner](https://mlsanity.com/paper/1807.08048/) (2018)
- [Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction](https://mlsanity.com/paper/1710.09483/) (2017)
- [Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2001.10494/) (2020)
- [Open Category Detection with PAC Guarantees](https://mlsanity.com/paper/1808.00529/) (2018)
- [Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks](https://mlsanity.com/paper/2102.12567/) (2021)
- [MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control](https://mlsanity.com/paper/2009.07517/) (2020)
- [Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees](https://mlsanity.com/paper/2201.08355/) (2022)
- [On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions](https://mlsanity.com/paper/2012.03390/) (2020)
- [Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Models](https://mlsanity.com/paper/2109.14700/) (2021)
- [Injecting Planning-Awareness into Prediction and Detection Evaluation](https://mlsanity.com/paper/2110.03270/) (2021)
- [Failure Prediction with Statistical Guarantees for Vision-Based Robot Control](https://mlsanity.com/paper/2202.05894/) (2022)
- [Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty](https://mlsanity.com/paper/2104.12446/) (2021)
- [Control-Aware Prediction Objectives for Autonomous Driving](https://mlsanity.com/paper/2204.13319/) (2022)
- [Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning](https://mlsanity.com/paper/2001.00496/) (2019)
- [Anomaly Detection in Multi-Agent Trajectories for Automated Driving](https://mlsanity.com/paper/2110.07922/) (2021)
- [MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation](https://mlsanity.com/paper/2110.09481/) (2021)
- [RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning](https://mlsanity.com/paper/2104.00543/) (2021)
- [SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction](https://mlsanity.com/paper/2110.00843/) (2021)
- [Policy Entropy for Out-of-Distribution Classification](https://mlsanity.com/paper/2005.12069/) (2020)
- [Reactive motion planning with probabilistic safety guarantees](https://mlsanity.com/paper/2011.03590/) (2020)
- [Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions](https://mlsanity.com/paper/2012.01027/) (2020)

## Built on by (most cited first, 11 of 11)

- [Driving Everywhere with Large Language Model Policy Adaptation](https://mlsanity.com/paper/2402.05932/) (2024)
- [What Truly Matters in Trajectory Prediction for Autonomous Driving?](https://mlsanity.com/paper/2306.15136/) (2023)
- [Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios](https://mlsanity.com/paper/2211.05455/) (2022)
- [iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.06236/) (2023)
- [Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems](https://mlsanity.com/paper/2510.21254/) (2025)
- [Early Warning Signals for OpenVLA Failure under Visual Distribution Shift](https://mlsanity.com/paper/2606.29699/) (2026)
- [CURA: Certified Runtime Alarms for Computer-Use Agents](https://mlsanity.com/paper/2608.27808/) (2026)
- [Learned Tree Search for Long-Horizon Social Robot Navigation in Shared Airspace](https://mlsanity.com/paper/2304.01428/) (2023)
- [Distribution and volume based scoring for Isolation Forests](https://mlsanity.com/paper/2309.11450/) (2023)
- [StepReflect: Structured UI Transition Reflection for Mobile GUI Agents](https://mlsanity.com/paper/2608.05587/) (2026)
- [Auditing Latent-Space Monitors for Autonomous Driving](https://mlsanity.com/paper/2609.30557/) (2026)

## Abstract (arXiv)

In modern autonomy stacks, prediction modules are paramount to planning motions in the presence of other mobile agents. However, failures in prediction modules can mislead the downstream planner into making unsafe decisions. Indeed, the high uncertainty inherent to the task of trajectory forecasting ensures that such mispredictions occur frequently. Motivated by the need to improve safety of autonomous vehicles without compromising on their performance, we develop a probabilistic run-time monitor that detects when a "harmful" prediction failure occurs, i.e., a task-relevant failure detector. We achieve this by propagating trajectory prediction errors to the planning cost to reason about their impact on the AV. Furthermore, our detector comes equipped with performance measures on the false-positive and the false-negative rate and allows for data-free calibration. In our experiments we compared our detector with various others and found that our detector has the highest area under the receiver operator characteristic curve.