# Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge

arXiv: https://arxiv.org/abs/2006.04767 · HTML: https://mlsanity.com/paper/2006.04767/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss](https://mlsanity.com/paper/2206.08641/) (2022)
- [PBP: Path-based Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2309.03750/) (2023)
- [Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification](https://mlsanity.com/paper/2504.13111/) (2025)
- [Informed Priors for Knowledge Integration in Trajectory Prediction](https://mlsanity.com/paper/2211.00348/) (2022)
- [Informed Spectral Normalized Gaussian Processes for Trajectory Prediction](https://mlsanity.com/paper/2403.11966/) (2024)
- [Maneuver-based Anchor Trajectory Hypotheses at Roundabouts](https://mlsanity.com/paper/2104.11180/) (2021)
- [A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses](https://mlsanity.com/paper/2411.19747/) (2024)

## Abstract (arXiv)

Predicting the future motion of vehicles has been studied using various techniques, including stochastic policies, generative models, and regression. Recent work has shown that classification over a trajectory set, which approximates possible motions, achieves state-of-the-art performance and avoids issues like mode collapse. However, map information and the physical relationships between nearby trajectories is not fully exploited in this formulation. We build on classification-based approaches to motion prediction by adding an auxiliary loss that penalizes off-road predictions. This auxiliary loss can easily be pretrained using only map information (e.g., off-road area), which significantly improves performance on small datasets. We also investigate weighted cross-entropy losses to capture spatial-temporal relationships among trajectories. Our final contribution is a detailed comparison of classification and ordinal regression on two public self-driving datasets.