G. Mecacci
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11 records found
1
Human behaviour with automated driving systems
A quantitative framework for meaningful human control
Automated driving systems (ADS) with partial automation are currently available for the consumer. They are potentially beneficial to traffic flow, fuel consumption, and safety, but human behaviour whilst driving with ADS is poorly understood. Human behaviour is currently expected to lead to dangerous circumstances as ADS could place human drivers ‘out-of-the-loop’ or cause other types of adverse behavioural adaptation. This article introduces the concept of ‘meaningful human control’ to better address the challenges raised by ADS, and presents a new framework of human control over ADS by means of literature-based categorisation. Using standards set by European authorities for driver skills and road rules, this framework offers a unique, quantified perspective into the effects of ADS on human behaviour. One main result is a rapid and inconsistent decrease in required skill- and rule-based behaviour mismatching with the increasing amount of required knowledge-based behaviour. Furthermore, the development of higher levels of automation currently requires different human behaviour than feasible, as a mismatch between supply and demand in terms of behaviour arises. Implications, discrepancies and emerging mismatches this framework elicits are discussed, and recommendations towards future design strategies and research opportunities are made to provide a meaningful transition of human control over ADS.
Meaningful human control as reason-responsiveness
The case of dual-mode vehicles
In this paper, in line with the general framework of value-sensitive design, we aim to operationalize the general concept of “Meaningful Human Control” (MHC) in order to pave the way for its translation into more specific design requirements. In particular, we focus on the operationalization of the first of the two conditions (Santoni de Sio and Van den Hoven 2018) investigated: the so-called ‘tracking’ condition. Our investigation is led in relation to one specific subcase of automated system: dual-mode driving systems (e.g. Tesla ‘autopilot’). First, we connect and compare meaningful human control with a concept of control very popular in engineering and traffic psychology (Michon 1985), and we explain to what extent tracking resembles and differs from it. This will help clarifying the extent to which the idea of meaningful human control is connected to, but also goes beyond, current notions of control in engineering and psychology. Second, we take the systematic analysis of practical reasoning as traditionally presented in the philosophy of human action (Anscombe, Bratman, Mele) and we adapt it to offer a general framework where different types of reasons and agents are identified according to their relation to an automated system’s behaviour. This framework is meant to help explaining what reasons and what agents (should) play a role in controlling a given system, thereby enabling policy makers to produce usable guidelines and engineers to design systems that properly respond to selected human reasons. In the final part, we discuss a practical example of how our framework could be employed in designing automated driving systems.
Full platoon control in Truck Platooning
A Meaningful Human Control perspective
Truck platooning is a form of vehicle automation and cooperation that is leading the way for cooperative and automated vehicle implementation. However, much is still unknown about the effects and potential dangers of many situations in regard to cooperative control of these platoons. In this contribution, we discuss many of the challenges in regard to full platoon control, we give concepts that can answer some of the questions and make recommendations on how full platoon control should be considered by truck manufactures, ADS software developers and policy makers. A main concept that is applied is that of Meaningful Human Control (MHC). We furthermore consider driver 'reasons', both distal and proximal, to identify correct chains of MHC. We conclude that each part of a system should be responsive to the maximum amount of relevant reasons available and the availability of relevant reasons should be maximized to obtain sufficient MHC.