# Interaction-Aware Probabilistic Behavior Prediction in Urban Environments

arXiv: https://arxiv.org/abs/1804.10467 · HTML: https://mlsanity.com/paper/1804.10467/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

## Built on by (most cited first, 7 of 7)

- [TPCN: Temporal Point Cloud Networks for Motion Forecasting](https://mlsanity.com/paper/2103.03067/) (2021)
- [GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving](https://mlsanity.com/paper/2101.06543/) (2021)
- [Bootstrap Motion Forecasting With Self-Consistent Constraints](https://mlsanity.com/paper/2204.05859/) (2022)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR](https://mlsanity.com/paper/2406.10722/) (2024)
- [A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering](https://mlsanity.com/paper/2108.12820/) (2021)
- [Real-Time Capable Decision Making for Autonomous Driving Using Reachable Sets](https://mlsanity.com/paper/2309.12289/) (2023)

## Abstract (arXiv)

Planning for autonomous driving in complex, urban scenarios requires accurate prediction of the trajectories of surrounding traffic participants. Their future behavior depends on their route intentions, the road-geometry, traffic rules and mutual interaction, resulting in interdependencies between their trajectories. We present a probabilistic prediction framework based on a dynamic Bayesian network, which represents the state of the complete scene including all agents and respects the aforementioned dependencies. We propose Markovian, context-dependent motion models to define the interaction-aware behavior of drivers. At first, the state of the dynamic Bayesian network is estimated over time by tracking the single agents via sequential Monte Carlo inference. Secondly, we perform a probabilistic forward simulation of the network's estimated belief state to generate the different combinatorial scene developments. This provides the corresponding trajectories for the set of possible, future scenes. Our framework can handle various road layouts and number of traffic participants. We evaluate the approach in online simulations and real-world scenarios. It is shown that our interaction-aware prediction outperforms interaction-unaware physics- and map-based approaches.