Autonomous driving technology pledges safety, convenience, and energy efficiency. Its
challenges include the unknown intentions of other road users: communication between …

The increasing functional and nonfunctional requirements of real-time applications, the
advent of mixed criticality computing, and the necessity of reducing costs are leading to an …

The optimal performance of robotic systems is usually achieved near the limit of state and
input bounds. Model predictive control (MPC) is a prevalent strategy to handle these …

In this letter, we present a learning-based control approach for autonomous racing with an
application to the AMZ Driverless race car gotthard. One major issue in autonomous racing …

Autonomous vehicles are expected to play a key role in the future of urban transportation
systems, as they offer potential for additional safety, increased productivity, greater …

This article focuses on the design, test, and validation of a deep neural network (DNN)-
based control scheme capable of predicting optimal motion commands for autonomous …

Autonomous car racing is a major challenge in robotics. It raises fundamental problems for
classical approaches such as planning minimum-time trajectories under uncertain dynamics …

This paper presents the algorithms and system architecture of an autonomous racecar. The
introduced vehicle is powered by a software stack designed for robustness, reliability, and …

This paper develops a two-stage nonlinear nonconvex control approach for autonomous
vehicle driving during highway cruise conditions. The goal of the controller is to track the …

We present a learning model predictive controller (LMPC) for autonomous racing. We model
the autonomous racing problem as a minimum time iterative control task, where an iteration …