# Map-Adaptive Goal-Based Trajectory Prediction

arXiv: https://arxiv.org/abs/2009.04450 · HTML: https://mlsanity.com/paper/2009.04450/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2020.

## Built on by (most cited first, 30 of 30)

- [GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2109.01827/) (2021)
- [HOME: Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2105.10968/) (2021)
- [Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals](https://mlsanity.com/paper/2106.15004/) (2021)
- [THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling](https://mlsanity.com/paper/2110.06607/) (2021)
- [LOKI: Long Term and Key Intentions for Trajectory Prediction](https://mlsanity.com/paper/2108.08236/) (2021)
- [GANet: Goal Area Network for Motion Forecasting](https://mlsanity.com/paper/2209.09723/) (2022)
- [Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2305.14715/) (2023)
- [HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention](https://mlsanity.com/paper/2404.06351/) (2024)
- [MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction](https://mlsanity.com/paper/2308.10280/) (2023)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement](https://mlsanity.com/paper/2211.08609/) (2022)
- [Trajectory Prediction with Graph-based Dual-scale Context Fusion](https://mlsanity.com/paper/2111.01592/) (2021)
- [Uncertainty estimation for Cross-dataset performance in Trajectory prediction](https://mlsanity.com/paper/2205.07310/) (2022)
- [HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling](https://mlsanity.com/paper/2110.02344/) (2021)
- [Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction](https://mlsanity.com/paper/2302.10873/) (2023)
- [Physically Feasible Vehicle Trajectory Prediction](https://mlsanity.com/paper/2104.14679/) (2021)
- [Exploiting map information for self-supervised learning in motion forecasting](https://mlsanity.com/paper/2210.04672/) (2022)
- [Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks](https://mlsanity.com/paper/2303.16109/) (2023)
- [PBP: Path-based Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2309.03750/) (2023)
- [FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding](https://mlsanity.com/paper/2406.14422/) (2024)
- [DESTINE: Dynamic Goal Queries with Temporal Transductive Alignment for Trajectory Prediction](https://mlsanity.com/paper/2310.07438/) (2023)
- [Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation](https://mlsanity.com/paper/2210.15449/) (2022)
- [Den-TP: A Density-Balanced Data Curation and Evaluation Framework for Trajectory Prediction](https://mlsanity.com/paper/2409.17385/) (2024)
- [Jointly Learning Agent and Lane Information for Multimodal Trajectory Prediction](https://mlsanity.com/paper/2111.13350/) (2021)
- [Multi-modal Transformer Path Prediction for Autonomous Vehicle](https://mlsanity.com/paper/2208.07256/) (2022)
- [Graph-based Trajectory Prediction with Cooperative Information](https://mlsanity.com/paper/2310.15692/) (2023)
- [MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles](https://mlsanity.com/paper/2407.05811/) (2024)
- [Trajectory Prediction in Dynamic Object Tracking: A Critical Study](https://mlsanity.com/paper/2506.19341/) (2025)
- [Learn to Quantify Social Interaction with Constraints for Pedestrian Walking](https://mlsanity.com/paper/2606.17897/) (2026)

## Abstract (arXiv)

We present a new method for multi-modal, long-term vehicle trajectory prediction. Our approach relies on using lane centerlines captured in rich maps of the environment to generate a set of proposed goal paths for each vehicle. Using these paths -- which are generated at run time and therefore dynamically adapt to the scene -- as spatial anchors, we predict a set of goal-based trajectories along with a categorical distribution over the goals. This approach allows us to directly model the goal-directed behavior of traffic actors, which unlocks the potential for more accurate long-term prediction. Our experimental results on both a large-scale internal driving dataset and on the public nuScenes dataset show that our model outperforms state-of-the-art approaches for vehicle trajectory prediction over a 6-second horizon. We also empirically demonstrate that our model is better able to generalize to road scenes from a completely new city than existing methods.