In recent years, formal methods have been extensively used in the design of autonomous
systems. By employing mathematically rigorous techniques, formal methods can provide …

Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …

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

Connected autonomous vehicles (CAVs) are revolutionizing the development of
transportation due to their potential to improve transportation performance in many ways …

This article investigates the control problem of making an autonomous vehicle modeled as a
nonlinear affine system, achieve both temporal logic tasks and obstacle avoidance. A new …

We consider perception-based control using state estimates that are obtained from high-
dimensional sensor measurements via learning-enabled perception maps. However, these …

Ensuring safety in dynamic multi-agent systems is challenging due to limited information
about the other agents. Control Barrier Functions (CBFs) are showing promise for safety …

In this work, we investigate the data-driven safe control synthesis problem for unknown
dynamic systems. We first formulate the safety synthesis problem as a robust convex …

Hybrid dynamical systems are ubiquitous as practical robotic applications often involve both
continuous states and discrete switchings. Safety is a primary concern for hybrid robotic …

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 …