It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an
unknown and stochastic environment under hard constraints that require the system state …

In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure
occupant safety. However, state-of-the-art methods using deep convolutional networks …

Challenges related to automated driving are no longer focused on just the construction of
such automated vehicles (AVs) but also on assuring the safety of operation. Recent …

The combination of data-driven learning methods with formal reasoning has seen a surge of
interest, as either area has the potential to bolstering the other. For instance, formal methods …

Neural networks (NNs) playing the role of controllers have demonstrated impressive
empirical performance on challenging control problems. However, the potential adoption of …

Future autonomous systems will employ complex sensing, computation, and communication
components for their perception, planning, control, and coordination, and could operate in …

The robustness of deep neural networks has received significant interest recently, especially
when being deployed in safety-critical systems, as it is important to analyze how sensitive …

Neural networks have been increasingly applied to control in learning-enabled cyber-
physical systems (LE-CPSs) and demonstrated great promises in improving system …

Neural networks have shown great promises in planning, control, and general decision
making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving …

Future autonomous systems will employ sophisticated machine learning techniques for the
sensing and perception of the surroundings and the making corresponding decisions for …