Varying Levels of Reality in Human Factors Testing: Parallel Experiments at Mcity and in a Driving Simulator

2017 ◽  
Author(s):  
Michael J. Flannagan ◽  
Shan Bao ◽  
Anuj Pradhan ◽  
John Sullivan ◽  
Yu Zhang
Keyword(s):  
Human Factors ◽  
Driving Simulator ◽  
Levels Of Reality

RETRACTED: Impact of Information Bandwidth of In-Vehicle Technologies on Young Drivers’ Attention Maintenance Performance: A Driving Simulator Study

2017 ◽  
Vol 61 (1) ◽  
pp. 1639-1643
Keyword(s):  
Human Factors ◽  
Driving Simulator ◽  
Young Drivers ◽  
Simulator Study ◽  
Vehicle Technologies ◽  
Maintenance Performance

This proceedings paper was inadvertently published after the authors notified the journal of their desire to withdraw the paper from the conference. The paper was not actually presented at the conference. This retraction is being issued at the authors’ request. The Journal, Human Factors, and SAGE apologize to the authors and readers for the inadvertent publication.

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 Validation of a driving simulator for research into human factors issues of automated vehicles

2019 ◽  
Vol 30 (2) ◽  
pp. 37-44
Author(s):  
Nebojsa Tomasevic ◽  
Tim Horberry ◽  
Brian Fildes
Keyword(s):  
Statistical Analysis ◽  
Human Factors ◽  
Driving Simulator ◽  
Research Centre ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Simulated Driving ◽  
Automated Vehicle ◽  
Perception Of Safety ◽  
Accident Research

This study evaluated the behavioural validity of the Monash University Accident Research Centre automation driving simulator for research into the human factors issues associated with automated driving. The study involved both on-road and simulated driving. Twenty participants gave ratings of their willingness to resume control of an automated vehicle and perception of safety for a variety of situations along the drives. Each situation was individually categorised and ratings were processed. Statistical analysis of the ratings confirmed the behavioural validity of the simulator, in terms of the similarity of the on-road and simulator data.

A Human Factors Investigation Into the Unavailability of Active Warnings at Railway Level Crossings

2012 ◽  
Author(s):  
Matthew Gildersleeve ◽  
Christian Wullems
Keyword(s):  
Human Factors ◽  
Task Analysis ◽  
Human Error ◽  
Driving Simulator ◽  
Human Behaviour ◽  
Human Reliability ◽  
Driver Behaviour ◽  
Theoretical Understanding ◽  
Level Crossings ◽  
The Impact

This paper discusses human factors issues of low cost railway level crossings in Australia. Several issues are discussed in this paper including safety at level railway crossings, human factors considerations associated with the unavailability of a warning device, and a conceptual model for how safety could be compromised at railway level crossings following prolonged or frequent unavailability. The current paper summarises and extends pertinent literature that must be considered for effective interventions to improve safety and to advance our theoretical understanding of human behaviour at level crossings. Although the results of our research are not presented, we describe our experimental approach to progress the current lack of knowledge in this area. In particular we highlight where we can improve previous research methodology (independent & dependent variables) when investigating right-side failure at level crossings, which can produce results with greater validity and meaning. Our research aims to quantify risk to motorists at level crossings following right-side failure using a Human Reliability Assessment (HRA) method, supported by data collected using an advanced driving simulator. This method aims to identify human error within tasks and task units identified as part of the task analysis process. It is anticipated that by modelling driver behaviour the current study will be able to quantify human reliability. Such a risk assessment for the impact of right-side failure at level crossings is currently absent in the literature. Therefore it is crucial to offer quantification of success and failure of this intricate system. The task analysis allows human error identification for the precursors to risky driving to be achieved. If task analysis is not employed the error reduction method may be unsuitable and eventually unsuccessful. Our aim is also to determine those contexts that allow the system to operate successfully with the smallest probability of human error. Human behaviour during complex tasks such as driving through a level crossing is fundamentally context bound. Therefore this study also aims to quantify those performance-shaping factors that may contribute to vehicle train collisions by highlighting changes in the task units and driver physiology. Finally we consider a number of variables germane to ensuring external validity of our results. Without this inclusion, such an analysis could seriously underestimate risk.

Driving Simulator Study of J-Turn Acceleration–Deceleration Lane and U-Turn Spacing Configurations

2017 ◽  
Vol 2638 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Carlos Sun ◽  
Zhu Qing ◽  
Praveen Edara ◽  
Bimal Balakrishnan ◽  
James Hopfenblatt
Keyword(s):  
Human Factors ◽  
Driving Simulator ◽  
Design Parameters ◽  
Crash Analysis ◽  
Significant Interaction Effect ◽  
Intersection Safety ◽  
Vehicle Trajectories ◽  
Acceleration And Deceleration ◽  
Simulator Study ◽  
Traditional Approaches

The J-turn, also known as the restricted crossing U-turn and Superstreet, is an innovative geometric design that can improve intersection safety. Although this design has been in use in several states for many years, there is very little research-based guidance for several design parameters. A driving simulator study was conducted to analyze the parameters of lane configuration, U-turn spacing, and signage. Two lane configurations were examined: ( a) an acceleration–deceleration configuration, in which acceleration and deceleration lanes are provided, and ( b) a deceleration-only configuration, in which only deceleration lanes are provided. Lane configuration was found to be the most important parameter affecting J-turn safety according to speed differentials. The only significant interaction effect among parameters was between lane configuration and U-turn spacing. The acceleration–deceleration configuration performed better than the deceleration-only configuration, having 66.3% fewer safety critical events. Vehicle trajectories and average lane change locations showed that U-turn spacing significantly affected the acceleration–deceleration configuration (e.g., average merge locations changed by 96% to .0101%) but not the deceleration-only configuration. No strong preference was demonstrated for either the directional or the diagrammatic signage style. This paper presents one of the first human-factors studies of the J-turn to focus on developing design guidance. This human-factors approach complements traditional approaches such as crash analysis and microsimulation.

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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