Analyzing the impact of mixed vehicle platoon formations on vehicle energy and traffic efficiencies

H Dong, J Shi, W Zhuang, Z Li, Z Song - Applied Energy, 2025‏ - Elsevier
Connected and automated vehicles (CAVs) offer promising prospects for the future of
transportation. However, the longstanding dominance of human-driven vehicles (HDVs) in …

An eco-driving strategy for autonomous electric vehicles crossing continuous speed-limit signalized intersections

J Liu, C Wang, W Zhao - Energy, 2024‏ - Elsevier
The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents
opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco …

[PDF][PDF] Projection-Optimal Monotonic Value Function Factorization in Multi-Agent Reinforcement Learning.

Y Mei, H Zhou, T Lan - AAMAS, 2024‏ - researchgate.net
Reinforcement learning has demonstrated its capability to solve challenging real-world
problems, ranging from autonomous driving to robotics and planning [1–12]. In some …

DCoMA: A dynamic coordinative merging assistant strategy for on-ramp vehicles with mixed traffic conditions

L Li, C Qian, J Gan, D Zhang, X Qu, F **ao… - … Research Part C …, 2024‏ - Elsevier
Merging sections on highways are identified as traffic bottlenecks, leading to congestion and
accidents. The emergence of Connected and Autonomous Vehicles (CAVs) technology …

Progressive virtual risk-based vehicle trajectory optimization in mixed traffic flow

H Ma, C Qian, L Li, X Qu, B Ran - Transportation Research Part C …, 2024‏ - Elsevier
Ramp-merging zones have perennially served as bottlenecks for both safety and efficiency
within road traffic. Addressing the inherent uncertainties and anomalous behaviors …

Stochastic time-optimal trajectory planning for connected and automated vehicles in mixed-traffic merging scenarios

VA Le, B Chalaki, FN Tzortzoglou… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Addressing safe and efficient interaction between connected and autonomous vehicles
(CAVs) and human-driven vehicles (HDVs) in a mixed-traffic environment has attracted …

Iterative learning-based cooperative motion planning and decision-making for connected and autonomous vehicles coordination at on-ramps

B Wang, X Gong, P Lyu, S Liang - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
This paper proposes an iterative learning-based cooperative motion planning and decision-
making approach to achieve time-optimal coordination control of connected and …

Cooperative bus eco-approaching and lane-changing strategy in mixed connected and automated traffic environment

Y Yuan, Y Yuan, B Yuan, X Li - Transportation Research Part C: Emerging …, 2024‏ - Elsevier
In mixed traffic environments, existing bus eco-approaching and lane-changing methods fail
to adequately consider the uncontrollability of human-driven vehicles and their interactions …

Eco-Driving Strategy Design of Connected Vehicle among Multiple Signalized Intersections Using Constraint-enforced Reinforcement Learning

H Ding, W Zhuang, H Dong, G Yin… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Optimizing speed profiles at urban signalized intersections, commonly referred to as an eco-
driving strategy, is acknowledged as a promising approach to improving vehicle energy …

Bayesian optimization through gaussian cox process models for spatio-temporal data

Y Mei, M Imani, T Lan - arxiv preprint arxiv:2401.14544, 2024‏ - arxiv.org
Bayesian optimization (BO) has established itself as a leading strategy for efficiently
optimizing expensive-to-evaluate functions. Existing BO methods mostly rely on Gaussian …