Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles
This study seeks to develop a cooperative platoon control for a platoon mixed with
connected and autonomous vehicles (CAVs) and human-drive vehicles (HDVs), aiming to …
connected and autonomous vehicles (CAVs) and human-drive vehicles (HDVs), aiming to …
Learning-based adaptive optimal control for connected vehicles in mixed traffic: Robustness to driver reaction time
Through vehicle-to-vehicle (V2V) communication, both human-driven and autonomous
vehicles can actively exchange data, such as velocities and bumper-to-bumper distances …
vehicles can actively exchange data, such as velocities and bumper-to-bumper distances …
Design of intelligent connected cruise control with vehicle-to-vehicle communication delays
Connected cruise control (CCC) refers to a type of advanced driver assistance system
combined with wireless vehicle-to-vehicle (V2V) communication technology to improve …
combined with wireless vehicle-to-vehicle (V2V) communication technology to improve …
A survey on urban traffic control under mixed traffic environment with connected automated vehicles
Efficient traffic control can alleviate traffic congestion, reduce fuel consumption, and improve
traffic safety. With the development of communication and automation technologies, regular …
traffic safety. With the development of communication and automation technologies, regular …
Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles
A connected autonomous vehicle (CAV) network can be defined as a set of connected
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
Inefficient traffic control may cause numerous problems such as traffic congestion and
energy waste. This paper proposes a novel multi-agent reinforcement learning method …
energy waste. This paper proposes a novel multi-agent reinforcement learning method …
Cooperative multi-agent actor–critic control of traffic network flow based on edge computing
Y Zhang, Y Zhou, H Lu, H Fujita - Future Generation Computer Systems, 2021 - Elsevier
Most of the existing traffic signal control strategies are hard to satisfy the real-time
requirements of traffic big data analysis, knowledge reasoning and decision making for …
requirements of traffic big data analysis, knowledge reasoning and decision making for …
Deep-learning-based intelligent intervehicle distance control for 6G-enabled cooperative autonomous driving
Research on the sixth-generation cellular networks (6G) is gaining huge momentum to
achieve ubiquitous wireless connectivity. Connected autonomous vehicles (CAVs) is a …
achieve ubiquitous wireless connectivity. Connected autonomous vehicles (CAVs) is a …
Leading cruise control in mixed traffic flow: System modeling, controllability, and string stability
Connected and autonomous vehicles (CAVs) have great potential to improve road
transportation systems. Most existing strategies for CAVs' longitudinal control focus on …
transportation systems. Most existing strategies for CAVs' longitudinal control focus on …
A deep reinforcement learning-based cooperative approach for multi-intersection traffic signal control
Abstract Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …