This study compares Deep Reinforcement Learning (DRL) and Model Predictive Control
(MPC) for Adaptive Cruise Control (ACC) design in car-following scenarios. A first-order …

In order to forward the development of transportation electrification and reach the goal of
carbon neutrality, eco-driving techniques for electric vehicles (EVs) are widely concerned …

Purpose-This study aims to propose an enhanced eco-driving strategy based on
reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the …

Appropriate coordination among multiple power components is essential to improve energy
efficiency, traffic safety and driving comfort simultaneously for hybrid electric vehicles …

Eco-cruising control of vehicles is a potential approach for improving vehicle energy
efficiency and reducing travel time. However, many eco-cruising studies merely focused on …

Purpose-This paper aims to present a cooperative adaptive cruise control, called stable
smart driving model (SSDM), for connected and autonomous vehicles (CAVs) in mixed traffic …

Transportation systems face a variety of problems, especially in the aspects of traffic
efficiency and energy consumption. The emerging of connected and automated vehicles …

Today, advanced driver-assistance systems (ADAS) come up with different abilities. One of
them is the adaptive cruise control (ACC) system. The ACC system is a continuation of …

Eco-driving methods incorporating lateral motion exhibit enhanced energy-saving prospects
in multi-lane traffic contexts, yet the randomly distributed obstructing vehicles and sparse …

Autodriver algorithm aims to develop a path-following algorithm for autonomous vehicles
using road geometry data and vehicle dynamics. In this study, a novel smart Autodriver …