Model Predictive Control (MPC) has shown to be a successful method for many applications
that require control. Especially in the presence of prediction uncertainty, various types of …

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 …

Despite recent advances in reinforcement learning (RL), its application in safety critical
domains like autonomous vehicles is still challenging. Although penalizing RL agents for …

The automotive industry as well as academia are currently conducting a lot of research
related to autonomous driving. Autonomous driving is an interesting topic that holds the …

In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …

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

Abstract autonomous driving systems not only provide services for human drivers, but also
need to consider the personalized driving requirements of human beings. In current road …

Safety and efficiency are two key elements for planning and control in autonomous driving.
Theoretically, model-based optimization methods, such as Model Predictive Control (MPC) …

Autonomous driving is one of the promising technologies to tackle traffic accident and
congestion problems nowadays. Even though an autonomous vehicle is operated without …

Automated vehicles require efficient and safe planning to maneuver in uncertain
environments. Largely this uncertainty is caused by other traffic participants, eg, surrounding …