X. Yao
3 records found
1
This study proposes a general framework to investigate car-following heterogeneity and its impacts on traffic safety and sustainability. The framework incorporates rigorous driving style classification using a semi-supervised learning technique and a micro-simulation process that
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Cooperative lane-changing in mixed traffic
A deep reinforcement learning approach
Deep Reinforcement Learning (DRL) has made remarkable progress in autonomous vehicle decision-making and execution control to improve traffic performance. This paper introduces a DRL-based mechanism for cooperative lane changing in mixed traffic (CLCMT) for connected and automate
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Current approaches to identifying driving heterogeneity face challenges in capturing the diversity of driving characteristics and understanding the fundamental patterns from a driving behaviour mechanism standpoint. This study introduces a comprehensive framework for identifying
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