# Stay on Track: A Frenet Wrapper to Overcome Off-road Trajectories in Vehicle Motion Prediction

arXiv: https://arxiv.org/abs/2306.00605 · HTML: https://mlsanity.com/paper/2306.00605/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2023.

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

- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Pre-training on Synthetic Driving Data for Trajectory Prediction](https://mlsanity.com/paper/2309.10121/) (2023)
- [Conditional Unscented Autoencoders for Trajectory Prediction](https://mlsanity.com/paper/2310.19944/) (2023)
- [AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework](https://mlsanity.com/paper/2507.13729/) (2025)
- [Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving](https://mlsanity.com/paper/2403.11304/) (2024)
- [Motion Forecasting via Model-Based Risk Minimization](https://mlsanity.com/paper/2409.10585/) (2024)
- [Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems](https://mlsanity.com/paper/2410.10653/) (2024)
- [Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback](https://mlsanity.com/paper/2504.13785/) (2025)
- [Geometry-Guided Adversarial Prompt Detection via Curvature and Local Intrinsic Dimension](https://mlsanity.com/paper/2503.03502/) (2025)
- [Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving](https://mlsanity.com/paper/2505.06740/) (2025)

## Abstract (arXiv)

Predicting the future motion of observed vehicles is a crucial enabler for safe autonomous driving. The field of motion prediction has seen large progress recently with state-of-the-Art (sotA) models achieving impressive results on large-scale public benchmarks. However, recent work revealed that learning-based methods are prone to predict off-road trajectories in challenging scenarios. These can be created by perturbing existing scenarios with additional turns in front of the target vehicle while the motion history is left unchanged. We argue that this indicates that SotA models do not consider the map information sufficiently and demonstrate how this can be solved by representing model inputs and outputs in a Frenet frame defined by lane centreline sequences. To this end, we present a general wrapper that leverages a Frenet representation of the scene, and that can be applied to SotA models without changing their architecture. We demonstrate the effectiveness of this approach in a comprehensive benchmark using two SotA motion prediction models. Our experiments show that this reduces the off-road rate on challenging scenarios by more than 90% without sacrificing average performance.