ITSC 2025 Paper Abstract

Paper FR-LM-T36.1

nadar, ali (EURECOM), Haerri, Jerome (EURECOM)

Optimizing Roundabout Management Via Deep Reinforcement Learning with Safety and Comfort Constraints

Scheduled for presentation during the Regular Session "S36a-Behavior Modeling and Decision-Making in Traffic Systems" (FR-LM-T36), Friday, November 21, 2025, 10:30−10:50, Surfers Paradise 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Data Analytics and Real-time Decision Making for Autonomous Traffic Management, Cooperative Driving Systems and Vehicle Coordination in Multi-vehicle Scenarios, Autonomous Vehicle Safety and Performance Testing

Abstract

This paper presents a deep reinforcement learning (DRL) framework to optimize autonomous vehicle maneuver during roundabout approaches, with a focus on safety, efficiency, and passenger comfort. The proposed method incorporates a logistic regression-based Roundabout Exit Probability (REP) model to estimate the likelihood that inbound vehicles will exit the roundabout, as well as a regression-based Time-To-Collision (TTC) predictor to model the ego vehicle’s controlled maneuver while maintaining comfort constraints. These predictive models are integrated into a Proximal Policy Optimization (PPO) framework, enhanced with a curriculum learning strategy to gradually shape the agent’s behavior toward balanced, human-like decision-making. The reward function is designed to penalize unsafe or abrupt actions and encourage smooth, efficient maneuvering. Experimental results in the CARLA simulator demonstrate the effectiveness of the proposed strategy in achieving robust, comfort-aware navigation in roundabout scenarios.