Zobrazeno 1 - 10
of 254
pro vyhledávání: '"YANG Kaidi"'
It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be
Externí odkaz:
http://arxiv.org/abs/2411.10031
A critical aspect of safe and efficient motion planning for autonomous vehicles (AVs) is to handle the complex and uncertain behavior of surrounding human-driven vehicles (HDVs). Despite intensive research on driver behavior prediction, existing appr
Externí odkaz:
http://arxiv.org/abs/2411.01475
Recent advancements in Connected Vehicle (CV) technology have prompted research on leveraging CV data for more effective traffic management. Despite the low penetration rate, such detailed CV data has demonstrated great potential in improving traffic
Externí odkaz:
http://arxiv.org/abs/2406.14108
High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruisi
Externí odkaz:
http://arxiv.org/abs/2404.14713
Connected and automated vehicles (CAVs) can be beneficial for improving the operation of highway bottlenecks such as weaving sections. This paper proposes a bi-level control approach based on an upper-level deep reinforcement learning controller and
Externí odkaz:
http://arxiv.org/abs/2403.16225
This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing safety during real-system operations. Even though reinforcement learning (RL) is capable of
Externí odkaz:
http://arxiv.org/abs/2401.17837
Autor:
Zhou, Jingyuan, Yang, Kaidi
It has been demonstrated that leading cruise control (LCC) can improve the operation of mixed-autonomy platoons by allowing connected and automated vehicles (CAVs) to make longitudinal control decisions based on the information provided by surroundin
Externí odkaz:
http://arxiv.org/abs/2401.15561
Swarms of Unmanned Aerial Vehicles (UAV) have demonstrated enormous potential in many industrial and commercial applications. However, before deploying UAVs in the real world, it is essential to ensure they can operate safely in complex environments,
Externí odkaz:
http://arxiv.org/abs/2401.12852
Autor:
Wang, Qiqing, Yang, Kaidi
This paper proposes a privacy-preserving data fusion method for traffic state estimation (TSE). Unlike existing works that assume all data sources to be accessible by a single trusted party, we explicitly address data privacy concerns that arise in t
Externí odkaz:
http://arxiv.org/abs/2401.11836
Connected and automated vehicles (CAVs) have recently gained prominence in traffic research due to advances in communication technology and autonomous driving. Various longitudinal control strategies for CAVs have been developed to enhance traffic ef
Externí odkaz:
http://arxiv.org/abs/2401.11148