# A survey on robustness in trajectory prediction for autonomous vehicles

arXiv: https://arxiv.org/abs/2402.01397 · HTML: https://mlsanity.com/paper/2402.01397/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2024.

## Built on by (most cited first, 4 of 4)

- [Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments](https://mlsanity.com/paper/2409.01971/) (2024)
- [Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics](https://mlsanity.com/paper/2506.08963/) (2025)
- [Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network](https://mlsanity.com/paper/2409.18399/) (2024)
- [Deep Fictitious Play-Based Potential Differential Games for Learning Human-Like Interaction at Unsignalized Intersections](https://mlsanity.com/paper/2506.12283/) (2025)

## Abstract (arXiv)

Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance. However, current trajectory prediction models show signs of overfitting, which may lead to unsafe or suboptimal behavior. To address these challenges, this paper presents a comprehensive framework that categorizes and assesses the definitions and strategies used in the literature on evaluating and improving the robustness of trajectory prediction models. This involves a detailed exploration of various approaches, including data slicing methods, perturbation techniques, model architecture changes, and post-training adjustments. In the literature, we see many promising methods for increasing robustness, which are necessary for safe and reliable autonomous driving.