Related papers: Eco-Driving at Signalized Intersections: A Multipl…
Connected and Automated Vehicles (CAVs) have real-time information from the surrounding environment by using local on-board sensors, V2X (Vehicle-to-Everything) communications, pre-loaded vehicle-specific lookup tables, and map database.…
This paper proposes a simplified version of classical models for urban transportation networks, and studies the problem of controlling intersections with the goal of optimizing network-wide congestion. Differently from traditional…
Automated vehicles can gather information about surrounding traffic and plan safe and energy-efficient driving behavior, which is known as eco-driving. Conventional eco-driving designs only consider preceding vehicles in the same lane as…
This paper focuses on the velocity planning and energy management problems for Connected and Automated Vehicles (CAVs) with hybrid electric powertrains. The eco-driving problem is formulated in the spatial domain as a nonlinear dynamic…
Urban traffic congestion, particularly at intersections, significantly affects travel time, fuel consumption, and emissions. Traditional fixed-time signal control systems often lack the adaptability to effectively manage dynamic traffic…
The rapid development of cyber-physical systems is driving a transition toward mixed traffic environments comprising both human-driven and connected and automated vehicles (CAVs). This shift presents a unique opportunity to leverage the…
Connected Vehicles (CVs) have the potential to significantly increase the safety, mobility, and environmental benefits of transportation applications. In this research, we have developed a real time adaptive traffic signal control algorithm…
Connected automated driving has the potential to significantly improve urban traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative behavior planning can be employed to jointly optimize the motion of multiple…
Platooning connected and autonomous vehicles (CAVs) can improve traffic and fuel efficiency. However, scalable platooning operations require junction-level coordination, which has not been well studied. In this paper, we study the…
We study the problem of eco-routing for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. We propose an algorithm which can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and…
Recent advancements in Connected Vehicle (CV) technology have prompted research on leveraging CV data for more effective traffic management. Despite the low penetration rate, such detailed CV data has demonstrated great potential in…
This work introduces an integrated approach to optimizing urban traffic by combining predictive modeling of vehicle flow, adaptive traffic signal control, and a modular integration architecture through distributed messaging. Using real-time…
This paper proposes a centralized multi-vehicle coordination scheme serving unsignalized intersections. The whole process consists of three stages: a) target velocity optimization: formulate the collision-free vehicle coordination as a…
We experimentally and theoretically study the thermodynamically optimal control of interacting multiple-particle systems, focusing on collections of colloidal particles individually confined in optical traps. We investigate protocols that…
A path-planning algorithm for connected and non-connected automated road vehicles on multilane motorways is derived from the opportune formulation of an optimal control problem. In this framework, the objective function to be minimized…
The upgrading and updating of vehicles have accelerated in the past decades. Out of the need for environmental friendliness and intelligence, electric vehicles (EVs) and connected and automated vehicles (CAVs) have become new components of…
Connected and Automated Hybrid Electric Vehicles have the potential to reduce fuel consumption and travel time in real-world driving conditions. The eco-driving problem seeks to design optimal speed and power usage profiles based upon…
Ill-managed intersections are the primary reasons behind the increasing traffic problem in urban areas, leading to nonoptimal traffic-flow and unnecessary deadlocks. In this paper, we propose an automated intersection management system that…
Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the Eco-Driving problem seeks…
Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem. However, existing RL-based methods are rarely…