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Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control

Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control

Authors :
Zhao, Zhouqiao
Liao, Xishun
Abdelraouf, Amr
Han, Kyungtae
Gupta, Rohit
Barth, Matthew J.
Wu, Guoyuan
Publication Year :
2023

Abstract

Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used. However, pre-defined ACC settings may not always align with driver's preferences and habits, leading to discomfort and potential safety issues. Personalized ACC (P-ACC) has been proposed to address this problem, but most existing research uses historical driving data to imitate behaviors that conform to driver preferences, neglecting real-time driver feedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACC framework that incorporates driver feedback adaptation in real time. The framework is divided into offline and online parts. The offline component records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. In the online component, driver feedback is used to update the driving gap preference in real time. The model is then retrained on the cloud with driver's takeover trajectories, achieving incremental learning to better match driver's preference. Human-in-the-loop (HuiL) simulation experiments demonstrate that our proposed method significantly reduces driver intervention in automatic control systems by up to 62.8%. By incorporating real-time driver feedback, our approach enhances the comfort and safety of P-ACC, providing a personalized and adaptable driving experience.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2309.05115
Document Type :
Working Paper