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

no code implementations • 10 Sep 2023 • Zhouqiao Zhao, Xishun Liao, Amr Abdelraouf, Kyungtae Han, Rohit Gupta, Matthew J. Barth, Guoyuan Wu

In the online component, driver feedback is used to update the driving gap preference in real time.

Incremental Learning

Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior

no code implementations • 2 Nov 2022 • Xishun Liao, Xuanpeng Zhao, Ziran Wang, Zhouqiao Zhao, Kyungtae Han, Rohit Gupta, Matthew J. Barth, Guoyuan Wu

The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network.

Evaluating Cybersecurity Risks of Cooperative Ramp Merging in Mixed Traffic Environments

no code implementations • 18 Nov 2021 • Xuanpeng Zhao, Ahmed Abdo, Xishun Liao, Matthew J. Barth, Guoyuan Wu

In this study, we investigate cybersecurity risks of a representative cooperative traffic management application, i. e., highway on-ramp merging, in a mixed traffic environment.

Management

End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning

no code implementations • 24 Jan 2020 • Zhensong Wei, Yu Jiang, Xishun Liao, Xuewei Qi, Ziran Wang, Guoyuan Wu, Peng Hao, Matthew Barth

This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system.

reinforcement-learning Reinforcement Learning (RL) +2