Can I read Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control on EtoBox?

Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control by Zhao, Zhouqiao; Liao, Xishun; Abdelraouf, Amr; Han, Kyungtae; Gupta, Rohit; Barth, Matthew J.; Wu, Guoyuan is a scholarly article available to read on EtoBox.

What is Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control about?

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

Author

Zhao, Zhouqiao; Liao, Xishun; Abdelraouf, Amr; Han, Kyungtae; Gupta, Rohit; Barth, Matthew J.; Wu, Guoyuan

Published

2023

Language

EN