Ensuring safety and achieving human-level driving performance remain challenges for
autonomous vehicles, especially in safety-critical situations. As a key component of artificial …

Ensuring safety is important for the practical deployment of reinforcement learning (RL).
Various challenges must be addressed, such as handling stochasticity in the environments …

Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen
driving scenarios, largely stemming from the non-interpretability and poor generalization of …

Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their
potential for many real-world tasks. However, vanilla RL and most safe RL approaches do …

Ensuring safe and capable motion planning is paramount for automated vehicles.
Traditional methods are limited in their ability to handle complex and unpredictable traffic …

Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real
world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is …

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

Formal certification of Neural Networks (NNs) is crucial for ensuring their safety, fairness,
and robustness. Unfortunately, on the one hand, sound and complete certification algorithms …

Featured Application Synchromodal optimisation; decision support systems; dynamical
transport optimisation. Abstract Intermodal freight transport (IFT) requires a large number of …

In this work, we investigate the reach-avoid problem of a class of time-varying analytic
systems with disturbances described by uncertain parameters. Firstly, by proposing the …