ITSC 2025 Paper Abstract

Paper FR-LA-T35.3

Kang, Minhee (KAIST), Choi, Yoojin (Korea Advanced Institute of Science and Technology (KAIST)), Choi, Eunseo (KAIST), Ahn, Heejin (KAIST)

Shockwave Minimization within the Mesoscopic: Infrastructure-Guided Optimal Acceleration for Autonomous Vehicles

Scheduled for presentation during the Regular Session "S35c-Optimization, Control, and Learning for Efficient and Resilient ITS" (FR-LA-T35), Friday, November 21, 2025, 16:40−17:00, Surfers Paradise 2

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 Transportation Optimization Techniques and Multi-modal Urban Mobility, Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) Communication Applications for Traffic Management, Autonomous Vehicle Safety and Performance Testing

Abstract

In this paper, we propose a novel mesoscopic shockwave mitigation framework, uniquely leveraging infrastructure-guided acceleration commands to Connected Autonomous Vehicles (CAVs). We formulate a nonlinear optimization problem to minimize upstream shockwave propagation and speed deviations from free-flow. Notably, by integrating classical macroscopic traffic models (LWR and Greenshields) with microscopic vehicle dynamics (IDM and Krauss), we define ``segment" as a mesoscopic concept in which infrastructure monitors individual vehicles (micro), computes average segment speed (macro), detects shockwaves, and transmits optimal reference accelerations to CAVs. We validate through simulations that our approach effectively mitigates shockwaves, maintains segment speed closer to free-flow, and reduces shockwave duration.