Seo, Juwon (Hanyang University), Ko, ChanHyeok (Hanyang University), SUNG, JIHO (Hanyang university), YUN, DONGGEUN (Hanyang University), Lee, Byeongyu (Hanyang University), Kim, Jin Sung (Hanyang University), Park, Taewoong (Hyundai Motor Company), Park, Ho Sung (Hyundai Motor Company), Ju, seong pil (Hyundai motor company), Chung, Chung Choo (Hanyang University)

Data-Driven Human Modeling Based on Temporal Information and Nonlinear Model Predictive Control for Adaptive Cruise Control Reducing Motion Sickness

Scheduled for presentation during the Poster Session "Human Drivers in Intelligent Transportation Systems" (FrAT15), Friday, September 27, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

This paper proposes a novel nonlinear system modeling technique using a data-driven approach with temporal information for adaptive cruise control (ACC) focused on reducing motion sickness through model predictive control (MPC). We develop an approximated human model from real-world data to enhance motion prediction and integrate it into MPC's cost function and constraints, emphasizing tracking performance, control effort, and motion sickness reduction. Using the ISO 2631-1:1977 standard, motion sickness is evaluated with the motion sickness dose value (MSDV) in the longitudinal axis of human motion. Validated through MATLAB/Simulink simulations, our method improves low-frequency human motion prediction accuracy, reduces RMSE and maximum error by 5.23% and 23.4%, lowers MSDV by 17% in ACC scenarios, and increases car-following performance by 42.3% compared to previous methods.