Learning optimal robust control of connected vehicles in mixed traffic flow
Connected and automated vehicles (CAVs) technologies promise to attenuate undesired
traffic disturbances. However, in mixed traffic where human-driven vehicles (HDVs) also …
traffic disturbances. However, in mixed traffic where human-driven vehicles (HDVs) also …
A hierarchical framework for multi-lane autonomous driving based on reinforcement learning
This paper proposes a hierarchical framework integrating deep reinforcement learning
(DRL) and rule-based methods for multi-lane autonomous driving. We define an …
(DRL) and rule-based methods for multi-lane autonomous driving. We define an …
[HTML][HTML] A Review of Multi-vehicle Cooperative Control System in Intelligent Transportation
Multi-vehicle cooperative control (MVCC) system has the potential to improve traffic flow,
reduce congestion, and increase safety. This paper reviews the progress achieved by …
reduce congestion, and increase safety. This paper reviews the progress achieved by …
A deep learning based traffic state estimation method for mixed traffic flow environment
Traffic state estimation plays a fundamental role in traffic control and management. In the
connected vehicles (CVs) environment, more traffic‐related data perceived and interacted …
connected vehicles (CVs) environment, more traffic‐related data perceived and interacted …
A computation offloading method with distributed double deep Q‐network for connected vehicle platooning with vehicle‐to‐infrastructure communications
Y Shi, J Chu, X Sun, S Ning - IET Intelligent Transport Systems, 2024 - Wiley Online Library
Current connected vehicle applications, such as platooning require heavy‐load computing
capability. Although mobile edge computing (MEC) servers connected to the roadside …
capability. Although mobile edge computing (MEC) servers connected to the roadside …
Augmented Mixed Vehicular Platoon Control With Dense Communication Reinforcement Learning for Traffic Oscillation Alleviation
Traffic oscillations present significant challenges to road transportation systems, resulting in
reduced fuel efficiency, heightened crash risks, and severe congestion. Recently emerging …
reduced fuel efficiency, heightened crash risks, and severe congestion. Recently emerging …
Cooperative traffic optimization with multi-agent reinforcement learning and evolutionary strategy: Bridging the gap between micro and macro traffic control
The emergence of connected and autonomous vehicles (CAVs) holds promise for fine-
grained traffic control. However, due to the longevity of future mixed traffic scenarios, there is …
grained traffic control. However, due to the longevity of future mixed traffic scenarios, there is …
Dynamic traffic graph based risk assessment of multivehicle lane change interaction scenarios
Y Guo, Y Chen, X Gu, J Guo, S Zheng… - Physica A: Statistical …, 2024 - Elsevier
Vehicles' lane-changing behavior can potentially result in traffic conflicts and crash risks,
particularly in scenarios with interactions among multiple vehicles. To assess the crash risk …
particularly in scenarios with interactions among multiple vehicles. To assess the crash risk …
Collaborative Control of Vehicle Platoon based on Deep Reinforcement Learning
J Chen, X Wu, Z Lv, Z Xu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
We propose a novel vehicle platoon collaborative control algorithm based on deep
reinforcement learning. Aiming at the slow convergence speed of traditional reinforcement …
reinforcement learning. Aiming at the slow convergence speed of traditional reinforcement …
Preference-Based Reinforcement Learning for Autonomous Vehicle Control Considering the Benefits of Following Vehicles
Most studies developing car-following controllers for AVs in mixed traffic primarily focus on
maximizing the utility of the AVs. However, the utility of the entire mixed traffic flow is largely …
maximizing the utility of the AVs. However, the utility of the entire mixed traffic flow is largely …