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 …

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

The rapid advancement of edge and cloud computing platforms, vehicular ad-hoc networks,
and machine learning techniques have brought both opportunities and challenges for next …

Model-based reinforcement learning has been widely studied for controller synthesis in
cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …

Reinforcement Learning (RL) in the context of safe exploration has long grappled with the
challenges of the delicate balance between maximizing rewards and minimizing safety …

Connectivity technology has shown great potentials in improving the safety and efficiency of
transportation systems by providing information beyond the perception and prediction …

Autonomous Driving (AD) faces crucial hurdles for commercial launch, notably in the form of
diminished public trust and safety concerns from long-tail unforeseen driving scenarios. This …

When the traffic stream is extremely congested and surrounding vehicles are not
cooperative, the mandatory lane changing can be significantly difficult. In this work, we …

The recent development of cloud computing and edge computing shows great promise for
the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on …

Abstract Design and development of connected and autonomous vehicles (CAVs) are
accompanied by a growing concern over the safety of these systems. This chapter will …