Interaction-Aware Probabilistic Behavior Prediction in Urban Environments

TL;DR

This paper introduces a probabilistic, interaction-aware prediction framework for autonomous driving in urban environments, utilizing a dynamic Bayesian network to model dependencies among traffic participants and predict their trajectories.

Contribution

The novel framework models interdependent behaviors of traffic agents using a dynamic Bayesian network with context-dependent motion models, improving prediction accuracy.

Findings

Outperforms interaction-unaware physics- and map-based methods in simulations.

Handles various road layouts and traffic participant numbers.

Effective in both online simulations and real-world scenarios.

Abstract

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.