Abrupt maneuvers by surrounding vehicles (SVs) can typically lead to safety concerns and
affect the task efficiency of the ego vehicle (EV), especially with model uncertainties …

Autonomous driving depends on perception systems to understand the environment and to
inform downstream decision-making. While advanced perception systems utilizing black-box …

Learning-based control algorithms optimize the driving performance of autonomous vehicles
by maximizing cumulative rewards or imitating expert maneuvers. However, these …

This paper introduces an adaptive beyond-the-limit controller, aimed at striking a balance
between high-performance maneuvers, such as transient drift, and ensuring safety in …

The deployment of autonomous vehicles (AVs) has faced hurdles due to the dominance of
rare but critical corner cases within the long-tail distribution of driving scenarios, which …

We propose the use of Bayesian networks, which provide both a mean value and an
uncertainty estimate as output, to enhance the safety of learned control policies under …

Autonomous driving has the potential to revolutionize transportation, but developing safe
and reliable systems remains a significant challenge. Reinforcement learning (RL) has …

This paper presents an adaptive safe control method that can adapt to changing
environments, tolerate large uncertainties, and exploit predictions in autonomous driving …

Approaches to keeping a dynamical system within state constraints typically rely on a model-
based safety condition to limit the control signals. In the face of significant modeling …

Autonomous vehicles face tremendous challenges while interacting with human drivers in
different kinds of scenarios. Developing control methods with safety guarantees while …