# Joint Out-of-Distribution Detection and Uncertainty Estimation for Trajectory Prediction

arXiv: https://arxiv.org/abs/2308.01707 · HTML: https://mlsanity.com/paper/2308.01707/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2023.

## Built on by (most cited first, 10 of 10)

- [Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimation](https://mlsanity.com/paper/2412.00205/) (2024)
- [Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction](https://mlsanity.com/paper/2410.01628/) (2024)
- [Evidential Uncertainty Estimation for Multi-Modal Trajectory Prediction](https://mlsanity.com/paper/2503.05274/) (2025)
- [Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehicles](https://mlsanity.com/paper/2409.17277/) (2024)
- [DECODE: Domain-aware Continual Domain Expansion for Motion Prediction](https://mlsanity.com/paper/2411.17917/) (2024)
- [Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmaps](https://mlsanity.com/paper/2501.16480/) (2025)
- [Collision Risk Estimation via Loss Prediction in End-to-End Autonomous Driving](https://mlsanity.com/paper/2503.07425/) (2025)
- [Adaptive Multi-Mode Out-of-Distribution Detection for Trajectory Prediction in Autonomous Vehicles](https://mlsanity.com/paper/2509.13577/) (2025)
- [Forecasting the Past: Gradient-Based Distribution Shift Detection in Trajectory Prediction](https://mlsanity.com/paper/2604.12425/) (2026)
- [Auditing Latent-Space Monitors for Autonomous Driving](https://mlsanity.com/paper/2609.30557/) (2026)

## Abstract (arXiv)

Despite the significant research efforts on trajectory prediction for automated driving, limited work exists on assessing the prediction reliability. To address this limitation we propose an approach that covers two sources of error, namely novel situations with out-of-distribution (OOD) detection and the complexity in in-distribution (ID) situations with uncertainty estimation. We introduce two modules next to an encoder-decoder network for trajectory prediction. Firstly, a Gaussian mixture model learns the probability density function of the ID encoder features during training, and then it is used to detect the OOD samples in regions of the feature space with low likelihood. Secondly, an error regression network is applied to the encoder, which learns to estimate the trajectory prediction error in supervised training. During inference, the estimated prediction error is used as the uncertainty. In our experiments, the combination of both modules outperforms the prior work in OOD detection and uncertainty estimation, on the Shifts robust trajectory prediction dataset by 2.8 % and 10.1 %, respectively. The code is publicly available.