Simulating realistic driving behavior is crucial for developing and testing autonomous systems in complex traffic environments. Equally important is the ability to control the behavior of simulated agents to tailor scenarios to specific research needs and safety considerations. This paper extends the general-purpose multi-agent driving behavior model ITRA (Scibior et al., 2021), by introducing a method called Control-ITRA to influence agent behavior through waypoint assignment and target speed modulation. By conditioning agents on these two aspects, we provide a mechanism for them to adhere to specific trajectories and indirectly adjust their aggressiveness. We compare different approaches for integrating these conditions during training and demonstrate that our method can generate controllable, infraction-free trajectories while preserving realism in both seen and unseen locations.
@article{arxiv.2501.12408,
title = {Control-ITRA: Controlling the Behavior of a Driving Model},
author = {Vasileios Lioutas and Adam Scibior and Matthew Niedoba and Berend Zwartsenberg and Frank Wood},
journal= {arXiv preprint arXiv:2501.12408},
year = {2025}
}