An Ontological Approach to Inform HMI Designs for Minimizing Driver Distractions with ADAS

ADAS (Advanced Driver Assistance Systems) are in-vehicle systems designed to enhance driving safety and efficiency as well as comfort for drivers in the driving process. Recent studies have noticed that when Human Machine Interface (HMI) is not designed properly, an ADAS can cause distraction which would affect its usage and even lead to safety issues. Current understanding of these issues is limited to the context-dependent nature of such systems. This paper reports the development of a holistic conceptualisation of how drivers interact with ADAS and how such interaction could lead to potential distraction. This is done taking an ontological approach to contextualise the potential distraction, driving tasks and user interactions centred on the use of ADAS. Example scenarios are also given to demonstrate how the developed ontology can be used to deduce rules for identifying distraction from ADAS and informing future designs

From Human Automation Interactions to Social Human Autonomy Machine Teaming in Maritime Transportation

Part 1: Information Technology and Disaster ManagementInternational audienceRecent technological advances in the field of Artificial intelligence (AI) and machine learning led to the creation of smart AI-enabled automation systems that are drastically changing maritime transportation. We developed a systematic literature review to understand how automation, based on Information Technologies (IT), has tackled the challenges related to human and machine interactions. We notably discuss the conceptual evolution from Human-Automation Interaction (HAI) to Human Autonomy Teaming (HAT) and present the risks of high levels of automation and the importance of teamwork in safety critical systems. Our results lie on a map of five clusters that highlight the importance of trust in the interactions between humans and machines, the risks related to automation, the human errors that are arising from these interactions, the effects of automation on situational awareness and the social norms in human-computer interactions. This literature show that human-machines interactions have mainly been studied from the computer/information systems’ (IS) point of view, hence neglecting the social dimensions of humans. Building on the difference between the concepts of automation and autonomy, we suggest the development of the concept of Social Human Autonomy Machine Teaming (SHAMT) to better consider the social dimensions of humans in these new interactions. Future research should focus on the right equilibrium between social needs, social interactions among humans and with autonomous machines with AI to optimize the global autonomy of the human-machine teammates in a whole ecosystem

The “Out-of-the-Loop” concept in automated driving: proposed definition, measures and implications

Despite an abundant use of the term “Out of the loop” (OOTL) in the context of automated driving and human factors research, there is currently a lack of consensus on its precise definition, how it can be measured, and the practical implications of being in or out of the loop during automated driving. The main objective of this paper is to consider the above issues, with the goal of achieving a shared understanding of the OOTL concept between academics and practitioners. To this end, the paper reviews existing definitions of OOTL and outlines a set of concepts, which, based on the human factors and driver behaviour literature, could serve as the basis for a commonly-agreed definition. Following a series of working group meetings between representatives from academia, research institutions and industrial partners across Europe, North America, and Japan, we suggest a precise definition of being in, out, and on the loop in the driving context. These definitions are linked directly to whether or not the driver is in physical control of the vehicle, and also the degree of situation monitoring required and afforded by the driver. A consideration of how this definition can be operationalized and measured in empirical studies is then provided, and the paper concludes with a short overview of the implications of this definition for the development of automated driving functions