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 …

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 …

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 …

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 …

Learning-based neural network (NN) control policies have shown impressive empirical
performance in a wide range of tasks in robotics and control. However, formal (Lyapunov) …

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 …

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 …

Reinforcement learning is challenging in delayed scenarios, a common real-world situation
where observations and interactions occur with delays. State-of-the-art (SOTA) state …

Ensuring safety for vehicle overtaking systems is one of the most fundamental and
challenging tasks in autonomous driving. This task is particularly intricate when the vehicle …

Autonomous driving has the potential to revolutionize transportation, but developing safe
and reliable systems remains a significant challenge. Reinforcement learning (RL) has …