Výsledky vyhledávání - "Yan, Longhao"

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Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control

Autor: Zhou, Jingyuan, Yan, Longhao, Liang, Jinhao, 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

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Interaction-Aware Trajectory Prediction for Safe Motion Planning in Autonomous Driving: A Transformer-Transfer Learning Approach

Autor: Liang, Jinhao, Tan, Chaopeng, Yan, Longhao, Zhou, Jingyuan, Yin, Guodong, Yang, Kaidi

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

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Bi-Level Control of Weaving Sections in Mixed Traffic Environments with Connected and Automated Vehicles

Autor: Yan, Longhao, Liang, Jinhao, Yang, Kaidi

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

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Control-Aware Trajectory Predictions for Communication-Efficient Drone Swarm Coordination in Cluttered Environments

Autor: Yan, Longhao, Zhou, Jingyuan, Yang, Kaidi

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

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Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning

Autor: Zhou, Jingyuan, Yan, Longhao, Yang, Kaidi

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

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