The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …

Reinforcement Learning (RL) algorithms have found limited success beyond simulated
applications, and one main reason is the absence of safety guarantees during the learning …

In this paper, we present results regarding the experimental validation of connected
automated vehicle design. In order for a connected automated vehicle to integrate well with …

In this paper, we present a systematic approach for fuel-economy optimization of a
connected automated truck that utilizes motion information from multiple vehicles ahead via …

In this paper, we design a connected cruise controller with safety guarantees. In particular,
we utilize a control safety function in order to guarantee the safety of a given control law. We …

Safety and tracking stability are crucial for safety-critical systems such as self-driving cars,
autonomous mobile robots, and industrial manipulators. To efficiently control safety-critical …

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 …

Learning-based controllers have recently shown impressive results for different robotic tasks
in well-defined environments, successfully solving a Rubiks cube and sorting objects in a …

This paper addresses the co-optimization of speed and gearshift control for battery electric
vehicles using short-range traffic information. To achieve greater electric motor efficiency, a …

This article investigates the theoretical Cramér-Rao bounds on estimation accuracy of
longitudinal vehicle dynamics parameters. This analysis is motivated by the value of …