In this work, the trajectory tracking problem of an autonomous racing electrical vehicle is
addressed. Accordingly, a two-layer control scheme is developed, such that stability …

The control of complex nonlinear systems (such as autonomous vehicles) usually requires
models which might be unavailable or inaccurate. In this paper, a novel data-driven Model …

This paper explores the eco-driving problem of parallel hybrid electric vehicles, intended to
drive a certain distance within a limited amount of time, where the longitudinal vehicle …

Combining data-driven applications with control systems plays a key role in recent
Autonomous Car research. This thesis offers a structured review of the latest literature on …

The field of automatic driving has always been a research hotspot. Based on inertial and
odometer, dead reckoning is used to navigate for L4 and above level autonomous vehicles …

The transportation system of those countries has a huge traffic flow is bearing great pressure
on transportation planning and management. Vehicle path planning is one of the effective …

Lung cancer is a highly heterogeneous disease, with diverse genetic, molecular, and
cellular drivers that can vary significantly between individual patients and even within a …

Significant progress has recently been made in transportation automation to alleviate human
faults in traffic flow. Recent breakthroughs in artificial intelligence have provided justification …

Improving driver behaviors through driving education and coaching is well-recognized as
being necessary and efficient for driving safely and reducing traffic accidents, as they …

Deep reinforcement learning is the technology of artificial neural networks in the field of
decision‐making and control. The traditional model‐free reinforcement learning algorithm …