Improving Car-Following Control in Mixed Traffic: A Deep Reinforcement Learning Framework with Aggregated Human-Driven Vehicles
X Chen, PH Tiu, Y Zhang, M Zhu… - 2024 IEEE Intelligent …, 2024 - ieeexplore.ieee.org
Traffic oscillations pose safety and efficiency challenges in mixed scenarios involving
connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Existing …
connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Existing …
Robust Longitudinal Control for Vehicular Platoons Using Deep Reinforcement Learning
AA Neto, LA Mozelli - IEEE Transactions on Intelligent …, 2024 - ieeexplore.ieee.org
In the last few years, researchers have applied ML strategies in the context of cooperative
transportation to increase safety and efficiency. The RL paradigm has been successfully …
transportation to increase safety and efficiency. The RL paradigm has been successfully …
Uncertainty modeling of connected and automated vehicle penetration rate under mixed traffic environment
Accurate knowledge of the penetration rate of connected and automated vehicles (CAVs) is
crucial for effective control applications during the transition from mixed traffic to full CAV …
crucial for effective control applications during the transition from mixed traffic to full CAV …
Generative adversarial network for car following trajectory generation and anomaly detection
Car-following trajectory generation and anomaly detection are critical functions in the
sensing module of an automated vehicle. However, developing models that capture realistic …
sensing module of an automated vehicle. However, developing models that capture realistic …
Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach
A substantial number of vehicles nowadays are equipped with adaptive cruise control
(ACC), which adjusts the vehicle speed automatically. However, experiments have found …
(ACC), which adjusts the vehicle speed automatically. However, experiments have found …
Data‐Driven Approach for Modeling the Nonlane‐Based Mixed Traffic Conditions
The diverse nature of vehicle categories and the resultant lane discipline in mixed
(heterogeneous) traffic cause complex spatial interactions. As a result, the driving behavior …
(heterogeneous) traffic cause complex spatial interactions. As a result, the driving behavior …
Model-data-driven control for human-leading vehicle platoon
This paper proposes a model-data-driven control method for a human-leading vehicle
platoon, comprising a human-driven vehicle (HDV) as the leader and connected automated …
platoon, comprising a human-driven vehicle (HDV) as the leader and connected automated …
Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach
X Jiang, J Zhang, D Li - arXiv preprint arXiv:2206.12065, 2022 - arxiv.org
This paper proposes an eco-driving framework for electric connected vehicles (CVs) based
on reinforcement learning (RL) to improve vehicle energy efficiency at signalized …
on reinforcement learning (RL) to improve vehicle energy efficiency at signalized …
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control
S Jiang, S Choi, L Sun - arXiv preprint arXiv:2407.08964, 2024 - arxiv.org
Cooperative Adaptive Cruise Control (CACC) plays a pivotal role in enhancing traffic
efficiency and safety in Connected and Autonomous Vehicles (CAVs). Reinforcement …
efficiency and safety in Connected and Autonomous Vehicles (CAVs). Reinforcement …
Impact of Driver Compliance and Aggressiveness in Connected Vehicles on Mixed Traffic Flow Efficiency: A Simulation Study
C Qian, T Feng, Z Li, Y Ye… - Journal of Advanced …, 2024 - Wiley Online Library
Connected vehicles (CVs) are becoming increasingly prevalent in today's transportation
systems, and understanding their behavior in mixed traffic flow is crucial for enhancing traffic …
systems, and understanding their behavior in mixed traffic flow is crucial for enhancing traffic …