Collaborating Authors

Towards a Framework for Certification of Reliable Autonomous Systems Artificial Intelligence

The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.

Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods Machine Learning

The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecasting of forthcoming positions). This paper focuses on predictive analytics for moving objects (could be pedestrians, cars, vessels, planes, animals, etc.) and surveys the state-of-the-art in the context of future location and trajectory prediction. We provide an extensive review of over 50 works, also proposing a novel taxonomy of predictive algorithms over moving objects. We also list the properties of several real datasets used in the past for validation purposes of those works and, motivated by this, we discuss challenges that arise in the transition from conventional to Big Data applications. CCS Concepts: Information systems > Spatial-temporal systems; Information systems > Data analytics; Information systems > Data mining; Computing methodologies > Machine learning Additional Key Words and Phrases: mobility data, moving object trajectories, trajectory prediction, future location prediction.

A Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration Concepts Machine Learning

Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the National Aerospace (NAS) is a complex problem which is required to be addressed by simulation studies before allowing the routine access of UAS into the NAS. This thesis focuses on providing 2D and 3D simulation frameworks using a game theoretical methodology to evaluate integration concepts in scenarios where manned and unmanned air vehicles co-exist. The fundamental gap in the literature is that the models of interaction between manned and unmanned vehicles are insufficient: a) they assume that pilot behavior is known a priori and b) they disregard decision making processes. The contribution of this work is to propose a modeling framework, in which, human pilot reactions are modeled using reinforcement learning and a game theoretical concept called level-k reasoning to fill this gap. The level-k reasoning concept is based on the assumption that humans have various levels of decision making. Reinforcement learning is a mathematical learning method that is rooted in human learning. In this work, a classical and an approximate reinforcement learning (Neural Fitted Q Iteration) methods are used to model time-extended decisions of pilots with 2D and 3D maneuvers. An analysis of UAS integration is conducted using example scenarios in the presence of manned aircraft and fully autonomous UAS equipped with sense and avoid algorithms.

Artificial intelligence needs guardrails


With the recent launch of the website as "Artificial Intelligence for the American People," AI will clearly be an integral part of our future. While some may still wonder, "what can AI do for us?," many more may be asking, "what can AI do to us?" given some recent tragic events. The crashes of the Boeing 737 MAXs and Uber and Tesla's self-driving car fatalities point to AI's unintended consequences and highlight how technologists as well as users of AI have both fallen short at making proper guardrails in deploying AI technology. People often think of AI as the panacea that will enable technology to solve our most pressing problems. In that way, AI brings to mind a seeming panacea of an earlier age: aspirin.

Uber says it will bring its flying taxis to Los Angeles in 2020

Los Angeles Times

In just over two years, Uber says it will let commuters soar over Los Angeles' snarled traffic in flying taxis. The ride-hailing firm announced Wednesday that L.A. will be one of the first cities served by UberAir, which it says will begin ferrying passengers across the region in electric aircraft in 2020. Aviation manufacturers such as Embraer, Bell Helicopter, Pipistrel, Aurora Flight Sciences, and Mooney Aviation will supply and pilot the aircraft. Uber will operate the software that passengers use to book a trip and take a commission, much like with Uber rides on the ground. "We're trying to work with cities in the early days who are interested in partnering to make it happen, while knowing that there will be pitfalls along the way," said Jeff Holden, Uber's chief product officer, explaining why the company chose Los Angeles and Dallas as the first cities to test the service.