ITSC 2025 Paper Abstract

Paper TH-LM-T21.2

Sun, Weihao (Cornell University), Bang, Heeseung (University of Delaware), Malikopoulos, Andreas (Cornell University)

AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning

Scheduled for presentation during the Invited Session "S21a-Energy-Efficient Connected Mobility" (TH-LM-T21), Thursday, November 20, 2025, 10:50−11:10, 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 Energy-efficient Motion Control for Autonomous Vehicles, Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks, Traffic Management for Autonomous Multi-vehicle Operations

Abstract

In this paper, we present an adherence-aware reinforcement learning (RL) approach aimed at seeking optimal lane-changing recommendations within a semi-autonomous driving environment to enhance a single vehicle's travel efficiency. The problem is framed within a Markov decision process setting and is addressed through an adherence-aware deep Q network, which takes into account the partial compliance of human drivers with the recommended actions. This approach is evaluated within CARLA's driving environment under realistic scenarios.