mlsanityData to 7 Oct 2026

Control-ITRA: Controlling the Behavior of a Driving Model

arXiv 2501.12408 ↗First posted 17 Jan 2025cs.AI cs.LG cs.ROcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 35 of its 53 references that are in the map. First posted on arXiv 17 Jan 2025.

Builds on

35 of 53 references.

References · 35

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23 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Improving Crash Frequency Prediction from Simulated Traffic Conflicts Using Machine Learning Based Microsimulationcited by 0
  2. 2026Mirror Learningcited by 0