Multiple studies have illustrated the potential for dramatic societal, environmental and economic benefits from significant penetration of autonomous driving. However, all the current approaches to autonomous driving require the automotive manufacturers to shoulder the primary responsibility and liability associated with replacing human perception and decision making with automation, potentially slowing the penetration of autonomous vehicles, and consequently slowing the realization of the societal benefits of autonomous vehicles. We propose here a new approach to autonomous driving that will re-balance the responsibility and liabilities associated with autonomous driving between traditional automotive manufacturers, infrastructure players, and third-party players. Our proposed distributed intelligence architecture leverages the significant advancements in connectivity and edge computing in the recent decades to partition the driving functions between the vehicle, edge computers on the road side, and specialized third-party computers that reside in the vehicle. Infrastructure becomes a critical enabler for autonomy. With this Infrastructure Enabled Autonomy (IEA) concept, the traditional automotive manufacturers will only need to shoulder responsibility and liability comparable to what they already do today, and the infrastructure and third-party players will share the added responsibility and liabilities associated with autonomous functionalities. We propose a Bayesian Network Model based framework for assessing the risk benefits of such a distributed intelligence architecture. An additional benefit of the proposed architecture is that it enables "autonomy as a service" while still allowing for private ownership of automobiles.
Infrastructure-based Autonomous Mobile Robots for Internal Logistics -- Challenges and Future Perspectives
Dept. of Electrical Engineering, Chalmers University of Technology（查尔姆斯理工大学电子工程系）；Global Trucks Operations, Volvo Group（沃尔沃集团全球卡车运营）；Chalmers Industriteknik（查尔姆斯工业技术）；School of Informatics, University of Skövde（斯德哥尔摩大学信息学院）；Dept. of Information Technology, Uppsala University（乌普萨拉大学信息科技系）；RISE Research Institutes of Sweden（瑞典研究机构）
The Algorithmic State Architecture (ASA): An Integrated Framework for AI-Enabled Government
Data for Policy CIC（数据政策社区利益公司）；University College London (UCL)（伦敦大学学院）；University of Cambridge（剑桥大学）；The Alan Turing Institute（艾伦·图灵研究所）；Imperial College London（帝国理工学院）
Berta: an open-source, modular tool for AI-enabled clinical documentation
Department of Medicine, University of Alberta, Edmonton, Alberta, Canada（阿尔伯塔大学医学系，埃德蒙顿，阿尔伯塔，加拿大）；Department of Emergency Medicine, University of Alberta, Edmonton, Alberta, Canada（阿尔伯塔大学急诊医学系，埃德蒙顿，阿尔伯塔，加拿大）；Alberta Health Services, Edmonton, Alberta, Canada（阿尔伯塔省健康服务，埃德蒙顿，阿尔伯塔，加拿大）；Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada（阿尔伯塔人工智能研究所，埃德蒙顿，阿尔伯塔，加拿大）；Department of Computer Science, University of Alberta, Edmonton, Alberta, Canada（阿尔伯塔大学计算机科学系，埃德蒙顿，阿尔伯塔，加拿大）
Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions
