Human Factors of Vehicle Automation

2021 ◽  
pp. 335-358
Author(s):  
Sunil Kr. Sharma ◽  
Sunil Kr. Singh ◽  
Subhash C. Panja
Keyword(s):  
Human Factors ◽  
Vehicle Automation

Preface to the Special Issue on Human Factors and Advanced Vehicle Automation: Of Benefits, Barriers, and Bridges to Safe and Effective Implementation

2020 ◽  
Vol 62 (2) ◽  
pp. 189-193
Author(s):  
William J. Horrey ◽  
John D. Lee
Keyword(s):  
Human Factors ◽  
Experimental Studies ◽  
Special Issue ◽  
Automated Driving ◽  
Effective Implementation ◽  
Different Types ◽  
Vehicle Technology ◽  
Vehicle Automation ◽  
Vehicle Technologies ◽  
Near Future

Objective The aim of this special issue is to bring together the latest research related to driver interaction with various types of vehicle automation. Background Vehicle technology has undergone significant progress over the past decade, bringing new support features that can assist the driver and take on more and more of the driving responsibilities. Method This issue is comprised of eight articles from international research teams, focusing on different types of automation and different user populations, including driver support features through to highly automated driving systems. Results The papers comprising this special issue are clustered into three categories: (a) experimental studies of driver interactions with advanced vehicle technologies; (b) analysis of existing data sources; and (c) emerging human factors issues. Studies of currently available and pending systems highlight some of the human factors challenges associated with the driver–system interaction that are likely to become more prominent in the near future. Moreover, studies of more nascent concepts (i.e., those that are still a long way from production vehicles) underscore many attitudes, perceptions, and concerns that will need to be considered as these technologies progress. Conclusions Collectively, the papers comprising this special issue help fill some gaps in our knowledge. More importantly, they continue to help us identify and articulate some of the important and potential human factors barriers, design considerations, and research needs as these technologies become more ubiquitous.

A Human Factors Perspective on Ethical Concerns of Vehicle Automation

2016 ◽  
Vol 60 (1) ◽  
pp. 1844-1848 ◽  
Author(s):  
Wesley J. Kumfer ◽  
Samuel J. Levulis ◽  
Megan D. Olson ◽  
Richard A. Burgess
Keyword(s):  
Human Factors ◽  
Knowledge Synthesis ◽  
Respect For Persons ◽  
Automated Vehicle ◽  
Ethical Concerns ◽  
Starting Point ◽  
Levels Of Automation ◽  
Low Levels ◽  
Vehicle Automation ◽  
Different Levels

This paper presents a knowledge synthesis of ethical questions for the application of rational ethics theories to human factors in vehicle automation. First, a brief summary of ethical concerns related to transportation automation and human factors is presented. A series of theoretical questions are then posed for different levels of vehicle automation. Particular concerns relating to the Principle of Utility and the Principle of Respect for Persons are highlighted for low levels of automation, high levels of automation, and full automation through the use of theoretical scenarios. Although some recommendations are drawn from these scenarios, the primary purpose of this paper is to serve as a starting point to encourage discussion and collaboration between human factors professionals, engineers, policymakers, transportation officials, software programmers, manufacturers, and the driving public regarding realistic goals for automated vehicle implementation.

scholarly journals Modelling Automation–Human Driver Handovers Using Operator Event Sequence Diagrams

2021 ◽  
Vol 1 (2) ◽  
pp. 351-369
Author(s):  
Neville A. Stanton ◽  
James W. Brown ◽  
Kirsten M. A. Revell ◽  
Jed Clark ◽  
Joy Richardson ◽  
...  
Keyword(s):  
Human Factors ◽  
Predictive Validity ◽  
Driving Simulator ◽  
Event Sequence ◽  
Sequence Diagrams ◽  
Human Driver ◽  
Wide Range ◽  
Human Operators ◽  
Vehicle Automation ◽  
First Time

This research aims to show the effectiveness of Operator Event Sequence Diagrams (OESDs) in the normative modelling of vehicle automation to human drivers’ handovers and validate the models with observations from a study in a driving simulator. The handover of control from automation to human operators has proved problematic, and in the most extreme circumstances catastrophic. This is currently a topic of much concern in the design of automated vehicles. OESDs were used to inform the design of the interaction, which was then tested in a driving simulator. This test provided, for the first time, the opportunity to validate OESDs with data gathered from videoing the handover processes. The findings show that the normative predictions of driver activity determined during the handover from vehicle automation in a driving simulator performed well, and similar to other Human Factors methods. It is concluded that OESDs provided a useful method for the human-centred automation design and, as the predictive validity shows, can continue to be used with some confidence. The research in this paper has shown that OESDs can be used to anticipate normative behaviour of drivers engaged in handover activities with vehicle automation in a driving simulator. Therefore, OESDs offer a useful modelling tool for the Human Factors profession and could be applied to a wide range of applications and domains.

scholarly journals Heavy Vehicle Automation: Human Factors Lessons Learned

2015 ◽  
Vol 3 ◽  
pp. 2945-2952 ◽  
Author(s):  
C. Nowakowski ◽  
Steven E. Shladover ◽  
H.-S. Tan
Keyword(s):  
Human Factors ◽  
Lessons Learned ◽  
Heavy Vehicle ◽  
Vehicle Automation

scholarly journals Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

Machines ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 36
Author(s):  
Simon Enjalbert ◽  
Livia Maria Gandini ◽  
Alexandre Pereda Baños ◽  
Stefano Ricci ◽  
Frederic Vanderhaegen
Keyword(s):  
Human Factors ◽  
New Technologies ◽  
Human Machine Interface ◽  
Transport Systems ◽  
Special Focus ◽  
Driving Assistance ◽  
Automation Equipment ◽  
Machine Interface ◽  
Transport Modes ◽  
Vehicle Automation

This paper provides an overview of Human Machine Interface (HMI) design and command systems in commercial or experimental operation across transport modes. It presents and comments on different HMIs from the perspective of vehicle automation equipment and simulators of different application domains. Considering the fields of cognition and automation, this investigation highlights human factors and the experiences of different industries according to industrial and literature reviews. Moreover, to better focus the objectives and extend the investigated industrial panorama, the analysis covers the most effective simulators in operation across various transport modes for the training of operators as well as research in the fields of safety and ergonomics. Special focus is given to new technologies that are potentially applicable in future train cabins, e.g., visual displays and haptic-shared controls. Finally, a synthesis of human factors and their limits regarding support for monitoring or driving assistance is proposed.

An Application of the HFACS Method to Aviation Accidents in Africa

2016 ◽  
Vol 6 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Isaac Munene
Keyword(s):  
Human Factors ◽  
Physical Environment ◽  
Human Factor ◽  
Flight Training ◽  
African Countries ◽  
Causal Factors ◽  
Aviation Accidents ◽  
Factors Analysis ◽  
Adverse Weather ◽  
Decision Errors

Abstract. The Human Factors Analysis and Classification System (HFACS) methodology was applied to accident reports from three African countries: Kenya, Nigeria, and South Africa. In all, 55 of 72 finalized reports for accidents occurring between 2000 and 2014 were analyzed. In most of the accidents, one or more human factors contributed to the accident. Skill-based errors (56.4%), the physical environment (36.4%), and violations (20%) were the most common causal factors in the accidents. Decision errors comprised 18.2%, while perceptual errors and crew resource management accounted for 10.9%. The results were consistent with previous industry observations: Over 70% of aviation accidents have human factor causes. Adverse weather was seen to be a common secondary casual factor. Changes in flight training and risk management methods may alleviate the high number of accidents in Africa.

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