We propose a Model Predictive Control (MPC) for collision avoidance between an
autonomous agent and dynamic obstacles with uncertain predictions. The collision …

The capacity of machine-learning methods to handle large and complex datasets makes
them suitable for applications in precision medicine. Current methods automate data …

Motion planning algorithms for urban automated driving must handle uncertainty due to
unknown intention and future motion of Dynamic Obstacles (DOs). Considering a single …

An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic
scenarios is proposed. The proposed method is based on model predictive control (MPC) …

The full deployment of autonomous driving systems on a worldwide scale requires that the
self-driving vehicle can be operated in a provably safe manner, ie, the vehicle must be able …

This article investigates the control problem of making an autonomous vehicle modeled as a
nonlinear affine system, achieve both temporal logic tasks and obstacle avoidance. A new …

One of the outstanding challenges for the widespread deployment of robotic systems like
autonomous vehicles is ensuring safe interaction with humans without sacrificing efficiency …

Handling uncertainty in model predictive control comes with various challenges, especially
when considering state constraints under uncertainty. Most methods focus on either the …

For autonomous vehicles, scene understanding is still one of the major challenges, which
needs to be well handled to avoid jittery decisions and unsmooth trajectories. Furthermore …

Autonomous driving requires efficient and safe decision making and motion planning in
dynamic and uncertain environments. Future movement of surrounding vehicles is often …