# Goal-Directed Occupancy Prediction for Lane-Following Actors

arXiv: https://arxiv.org/abs/2009.12174 · HTML: https://mlsanity.com/paper/2009.12174/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [MC-BEVRO: Multi-Camera Bird Eye View Road Occupancy Detection for Traffic Monitoring](https://mlsanity.com/paper/2502.11287/) (2025)
- [Deep Occupancy-Predictive Representations for Autonomous Driving](https://mlsanity.com/paper/2303.04218/) (2023)

## Abstract (arXiv)

Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. Many existing approaches to this problem strive to distill all possible vehicle behaviors into a simplified set of high-level actions. However, these action categories do not suffice to describe the full range of maneuvers possible in the complex road networks we encounter in the real world. To combat this deficiency, we propose a new method that leverages the mapped road topology to reason over possible goals and predict the future spatial occupancy of dynamic road actors. We show that our approach is able to accurately predict future occupancy that remains consistent with the mapped lane geometry and naturally captures multi-modality based on the local scene context while also not suffering from the mode collapse problem observed in prior work.