Co-founder Dr. Sam Anthony and colleagues publish paper outlining common-sense rules for safe and ethical autonomous vehicles
Perceptive Automata CTO and Co-founder publishes scientific paper in Proceedings of the National Academy of Sciences
BOSTON, March 8, 2021 /PRNewswire-PRWeb/ -- In March 2021, Perceptive Automata Chief Technology Officer and Co-Founder, Dr. Sam Anthony, co-authored a scientific paper in the Proceedings of the National Academy of Sciences establishing a groundbreaking new framework for thinking about ethics and safety for self-driving cars. Titled "From driverless dilemmas to more practical commonsense tests for automated vehicles", Sam and his co-authors discuss a set of common-sense rules for automated vehicles (AVs) and how it's possible "to test the common sense of an AV". As defined in the paper, Dr. Anthony and his colleagues "start by reviewing discussions of "driverless dilemmas"...Then, [they] explain how to substantially change the premises and features of these dilemmas in order to lay the foundations for a more practical and relevant framework that tests driving common sense as an integral part of road rules testing."
As one of the world's most-cited and comprehensive multidisciplinary scientific journals, Proceedings of the National Academy of Sciences (PNAS), Sam is honored to be recognized by such an esteemed scientific journal. Dr. Anthony's co-contributors on this article include nuTonomy co-founder Emilio Frazzoli, along with corresponding author Julian De Freitas of Harvard, Andrea Censi, Bryant Walker Smith, and Luigi Di Lillo.
To read the full paper, "From driverless dilemmas to more practical commonsense tests for automated vehicles", by Dr. Anthony and his colleagues, please visit https://www.pnas.org/content/118/11/e2010202118
About Perceptive Automata
Perceptive Automata is creating a best-in-class artificial intelligence that gives machines, such as self-driving cars, the ability to understand the human state of mind. The company combines behavioral science techniques with machine learning to give autonomous systems the capability to anticipate and react to human behavior, enabling autonomous vehicles to navigate safely and smoothly around pedestrians, cyclists, and other drivers. This is essential for autonomous systems to seamlessly roll out in human-dominated road environments and to deliver a smooth ride experience for passengers of autonomous mobility services. For more information about Perceptive Automata, visit http://www.perceptiveautomata.com.
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