Benefiting from the rapid development of communication and intelligent vehicle technology
in recent years, most traffic information is capable of being collected, processed, and …

Drivers' mistakes may cause some traffic accidents, and such accidents can be avoided if
prompt advice could be given to drivers. So, how to detect driving risk is the key factor …

Driving risk field is regarded as an effective method to evaluate the driving safety for
Connected and Automated Vehicles (CAVs). The existing driving risk field models do not …

Under the traffic environment of the Internet of Vehicles, especially with the development of
vehicle-to-vehicle technologies (ie, V2V), drivers not only frequently perceive the vehicle …

Several past studies showed that Autonomous Vehicles (AVs) can reduce crash risk, stop-
and-go traffic, and travel time. To analyze the safety benefits of AVs, most of the researchers …

Potential field theory, as a theory that can also be applied to vehicle control, is an emerging
risk quantification approach to accommodate the connected and self-driving vehicle …

This study proposes a motion planning and control system based on collision risk potential
prediction characteristics of experienced drivers. Recently, automatic braking systems have …

Long short-term memory network is one of the most important network architectures of
decision-making for self-driving vehicles. Nevertheless, the decision-making accuracy of …

Objective: Autonomous driving technology eliminates human errors, and thus it is a
promising solution for reducing road traffic fatalities and injuries. While future autonomous …

In order to ensure the smooth, reliable and safe driving process of intelligent driving cars,
this paper analyzes the environmental information from the decision-making perception …