The IEEE International Conference on Intelligent Transportation Systems (ITSC2025) will be held on November 18-21, 2025, Gold Coast, Australia.
ITSC is an annual flagship conference of the IEEE Intelligent Transportation Systems Society (ITSS).

The following presentation will be delivered.

Continuous Inverse Reinforcement Learning with State-wise Safety Constraints for Stable Driving Behavior Prediction

Abstract:
Inverse reinforcement learning (IRL) is a promising approach for modeling human driving behaviors by learning underlying reward functions from expert demonstrations. While recent studies have incorporated failed demonstrations to improve learning robustness, most existing methods enforce safety constraints only at the trajectory level, which is insufficient for real-world autonomous driving scenarios requiring per-state safety.
This paper proposes a novel IRL framework that introduces state-wise safety constraints via a behavior discriminator, which generates safety labels for each state based on environmental context. By integrating the discriminator into the main reward optimization loop, the proposed method avoids additional computational complexity while ensuring safety at every decision point.
Experimental results in the CARLA simulator across multiple driving scenarios demonstrate improved performance in both behavior imitation and driving task requirements. The results confirm that enforcing state-wise safety significantly enhances stability and reliability in driving behavior prediction in static contextual environments, providing a viable direction for safer autonomous decision-making.

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Presentation information(Program)

November 21, 2025, 16:00−16:20 Session “S42c-Safety and Risk Assessment for Autonomous Driving Systems”
Title：Continuous Inverse Reinforcement Learning with State-Wise Safety Constraints for Stable Driving Behavior Prediction
Authors：Zhao, Minglu (Tokyo Institute of Technology), Shimosaka, Masamichi (Tokyo Institute of Technology)
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