This project aims to transform the software development process in modern cars, which are witnessing significant innovation with many new autonomous functions being introduced, culminating in a fully autonomous vehicle. Most of these new features are indeed implemented in software, at the heart of which lies several control algorithms. Such control algorithms operate in a feedback loop, involving sensing the state of the plant or the system to be controlled, computing a control input, and actuating the plant in order to enforce a desired behavior on it.

In avionics, an evolution is underway to endow aircraft with ?thinking? capabilities through the use of artificial-intelligence (AI) techniques. This evolution is being fueled by the availability of high-performance embedded hardware platforms, typically in the form of multicore machines augmented with accelerators that can speed up certain computations. Unfortunately, avionics software certification processes have not kept pace with this evolution.

Deep Reinforcement Learning (RL) has emerged as prominent tool to control cyber-physical systems (CPS) with highly non-linear, stochastic, and unknown dynamics. Nevertheless, our current lack of understanding of when, how, and why RL works necessitates the need for new synthesis and analysis tools for safety-critical CPS driven by RL controllers; this is the main scope of this project. The primary focus of this research is on mobile robot systems.

New vulnerabilities arise in Cyber-Physical Systems (CPS) as new technologies are integrated to interact and control physical systems. In addition to software and network attacks, sensor attacks are a crucial security risk in CPS, where an attacker alters sensing information to negatively interfere with the physical system. Acting on malicious sensor information can cause serious consequences. While many research efforts have been devoted to protecting CPS from sensor attacks, several critical problems remain unresolved.

Human interaction with autonomous cyber-physical systems is becoming ubiquitous in consumer products, transportation systems, manufacturing, and many other domains. This project seeks constructive methods to answer the question: How can we design cyber-physical systems to be responsive and personalized, yet also provide high-confidence assurances of reliability?

Complex cyber-physical systems (CPS) that operate in dynamic and uncertain environments will inevitably encounter unanticipated situations during their operation. Examples range from naturally occurring faults in both the cyber and physical components to attacks launched by malicious entities with the purpose of disrupting normal operations. As infrastructures, e.g. energy, transportation, industrial systems and built environments, are getting smarter, the chance of a fault or attack increases.

Deep Neural Networks (DNN) enabled Cyber-Physical Systems (CPS) hold great promise for revolutionizing many industries, such as drones and self-driving cars. However, the current generation of DNN cannot provide analyzable behaviors and verifiable properties that are necessary for safety assurance. This critical flaw in purely data-driven DNN sometimes leads to catastrophic consequences, such as vehicle crashes linked to self-driving and driver-assistance technologies.

Many emerging cyber-physical systems (CPS) are composed of a network of autonomous agents that make high-impact decisions, for example, a network of robots or drones that are on a search and rescue (SAR) mission. Such systems are referred to as high-impact decision making cyber-physical systems (HI-CPS). This research aims at building a unified theoretical framework for HI-CPS and validating this framework by fully implementing and testing a concrete example of such systems, namely, a network of autonomous agents that search for a lost person in dire conditions.