29 citations · 32 across the 14 of their papers we have counts for
6 papers · 1 filter
Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control
Jingyuan Zhou, Longhao Yan, Jinhao Liang +1
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 exi…
Bi-Level Control of Weaving Sections in Mixed Traffic Environments with Connected and Automated Vehicles
Longhao Yan, Jinhao Liang, Kaidi Yang
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 approa…
Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning
Jingyuan Zhou, Longhao Yan, Kaidi Yang
Connected and automated vehicles (CAVs) have recently gained prominence in traffic research due to advances in communication technology and autonomous driving. Various longitudinal…
Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances
Ke Lu, Dongjun Li, Qun Wang +3
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-sys…
A Parameter Privacy-Preserving Strategy for Mixed-Autonomy Platoon Control
Jingyuan Zhou, Kaidi Yang
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 longitud…
Control-Aware Trajectory Predictions for Communication-Efficient Drone Swarm Coordination in Cluttered Environments
Longhao Yan, Jingyuan Zhou, Kaidi Yang
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 i…