Autonomous driving has attracted considerable attention from research and industry
communities. Although prototypes of automated vehicles (AVs) are developed, remaining …

The prevalence of motion sickness among passengers using personal electronic devices,
such as smartphones, during vehicle journeys has become a growing concern. This issue is …

Naturalistic driving data were applied to study driver acceleration behaviour, and a
probability model of the driver was proposed. First, the question of whether the database is …

The acceptance of automated driving is under the potential threat of motion sickness. It
hinders the passengers' willingness to perform secondary activities. In order to mitigate …

High risk driving scenarios are critical for the deployment of highly automated vehicles
virtual test. In this study, we have proposed a deep learning method to identify high risk …

This paper discusses the feasibility of data captured in a long-term Naturalistic Driving Study
(NDS) for identification of vehicle dynamics. Driving data were captured for over a year. In …

Human‐like automated driving strategies could have advantages in traffic safety and
comfort. However, the primary features of human‐like driving behaviors are not clear yet. To …

In this paper, the personalized and common acceleration distribution characteristics of the
human driver are presented by using China-FOT. Firstly, how much data can get a …

Ensuring the resilience of deep learning algorithms against adversarial attacks during edge-
cloud data transmission between edge and cloud systems is crucial. Although significant …

This paper presents an analysis of the euroFot data set to determine limits for the typical
lateral acceleration behavior of drivers. Since recent studies indicate that lateral …