We propose a data-driven control framework for autonomous driving which combines
learning-based risk assessment with personalized, safety-focused, predictive control …

Car-following control is a fundamental application of autonomous driving. This control has
multiple objectives, including tracking a safe distance to a preceding vehicle and enhancing …

Autonomous vehicles face the challenge of providing safe transportation while efficiently
maneuvering in an uncertain environment. Considering surrounding vehicles, two types of …

Autonomous vehicle driving systems face the challenge of providing safe, feasible and
human-like driving policy quickly and efficiently. The traditional approach usually involves a …

Most current research in the field of autonomous vehicle control assumes that all vehicles
will follow the same patterns of automated driving behavior, resulting in systems with …

For highly automated driving above SAE level 3, behavior generation algorithms must
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …

The advent of autonomous vehicles (AVs) has complicated traffic flow and raised safety
concerns, there is a pressing need to enhance traffic efficiency alongside ensuring robust …

Despite the rapid improvement of autonomous driving technology in recent years,
automotive manufacturers must resolve liability issues to commercialize autonomous …

We propose a stochastic prediction-control framework to promote safety in automated
driving by directly controlling the joint state probability density functions (PDFs) subject to the …

In this study, we explore the problem of adaptive vehicle trajectory control for different risk
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …