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Analyzing the impact of mixed vehicle platoon formations on vehicle energy and traffic efficiencies
Connected and automated vehicles (CAVs) offer promising prospects for the future of
transportation. However, the longstanding dominance of human-driven vehicles (HDVs) in …
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
The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents
opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco …
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.
Reinforcement learning has demonstrated its capability to solve challenging real-world
problems, ranging from autonomous driving to robotics and planning [1–12]. In some …
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
Merging sections on highways are identified as traffic bottlenecks, leading to congestion and
accidents. The emergence of Connected and Autonomous Vehicles (CAVs) technology …
accidents. The emergence of Connected and Autonomous Vehicles (CAVs) technology …
Progressive virtual risk-based vehicle trajectory optimization in mixed traffic flow
Ramp-merging zones have perennially served as bottlenecks for both safety and efficiency
within road traffic. Addressing the inherent uncertainties and anomalous behaviors …
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
Addressing safe and efficient interaction between connected and autonomous vehicles
(CAVs) and human-driven vehicles (HDVs) in a mixed-traffic environment has attracted …
(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
This paper proposes an iterative learning-based cooperative motion planning and decision-
making approach to achieve time-optimal coordination control of connected and …
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
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 …
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
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 …
driving strategy, is acknowledged as a promising approach to improving vehicle energy …
Bayesian optimization through gaussian cox process models for spatio-temporal data
Bayesian optimization (BO) has established itself as a leading strategy for efficiently
optimizing expensive-to-evaluate functions. Existing BO methods mostly rely on Gaussian …
optimizing expensive-to-evaluate functions. Existing BO methods mostly rely on Gaussian …