Data Mining Practical Machine Learning Tools and Techniques

Mother Nature knows best--How engineered organizations of the future will resemble natural-born systems.

Persuasive Technology: Using Computers to Change What We Think and Do

We outline a model for types and levels of automation that provides a framework and an objective basis for deciding which system functions should be automated and to what extent. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.

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A model for types and levels of human interaction with automation

Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.

/pdf/trust-in-automation-designing-for-appropriate-reliance-2uiy4o89ga.pdf

Trust in Automation: Designing for Appropriate Reliance

This paper addresses theoretical, empirical, and analytical studies pertaining to human use, misuse, disuse, and abuse of automation technology. Use refers to the voluntary activation or disengagement of automation by human operators. Trust, mental workload, and risk can influence automation use, but interactions between factors and large individual differences make prediction of automation use difficult. Misuse refers to over reliance on automation, which can result in failures of monitoring or decision biases. Factors affecting the monitoring of automation include workload, automation reliability and consistency, and the saliency of automation state indicators. Disuse, or the neglect or underutilization of automation, is commonly caused by alarms that activate falsely. This often occurs because the base rate of the condition to be detected is not considered in setting the trade-off between false alarms and omissions. Automation abuse, or the automation of functions by designers and implementation by man...

Humans and Automation: Use, Misuse, Disuse, Abuse

As automated controllers supplant human intervention in controlling complex systems, the operators' role often changes from that of an active controller to that of a supervisory controller. Acting as supervisors, operators can choose between automatic and manual control. Improperly allocating function between automatic and manual control can have negative consequences for the performance of a system. Previous research suggests that the decision to perform the job manually or automatically depends, in part, upon the trust the operators invest in the automatic controllers. This paper reports an experiment to characterize the changes in operators' trust during an interaction with a semi-automatic pasteurization plant, and investigates the relationship between changes in operators' control strategies and trust. A regression model identifies the causes of changes in trust, and a ‘trust transfer function’ is developed using lime series analysis to describe the dynamics of trust. Based on a detailed analysis of ...

Trust, control strategies and allocation of function in human-machine systems.

The increasing use of automation to supplant human intervention in controlling complex systems changes the operators' role from active controllers (directly involved with the system) to supervisory controllers (managing the use of different degrees of automatic and manual control). This paper examines the relationship between trust in automatic controllers, self-confidence in manual control abilities, and the use of automatic controllers in operating a simulated semi-automatic pasteurization plant. Trust, combined with self-confidence, predicted the operators' allocation strategy. A Multitrait-multimethod matrix and logit functions showed how trust and self-confidence relate to the use of automation. An ARMAV time series model of the dynamic interaction of trust and self-confidence, combined with individual biases, accounted for 60.9-86.5% of the variance in the use of the three automatic controllers. In general, automation is used when trust exceeds self-confidence, and manual control when the opposite is true. Since trust and self-confidence are two factors that guide operators' interactions with automation, the design of supervisory control systems should include provisions to ensure that operators' trust reflects the capabilities of the automation and operators' self-confidence reflects their abilities to control the system manually.

Trust, self-confidence, and operators' adaptation to automation

Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that early warnings helped distracted drivers react more quickly--and thereby avoid more collisions--than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0% and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input.

https://journals.sagepub.com/doi/pdf/10.1518/0018720024497844

Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator.

INTRODUCTION Introduction, M.A. Regan, K.L. Young, and J.D. Lee DEFINITIONS, THEORIES, AND MODELS OF DRIVER DISTRACTION On the Philosophical Foundations of the Distracted Driver and Driving Distraction, P.A. Hancock, M. Mouloua, and J.W. Senders Defining Driver Distraction, J.D. Lee, K.L. Young, and M.A. Regan What Drives Distraction? Distraction as a Breakdown of Multilevel Control, J. D. Lee, M.A. Regan, and K.L. Young Models of Attention, Distraction, and Highway Hazard Avoidance, C.D. Wickens and W.J. Horrey MEASUREMENT OF DRIVER DISTRACTION Measuring Exposure to Driver Distraction, S.P. McEvoy and M.R. Stevenson Measuring the Effects of Driver Distraction: Direct Driving Performance Methods and Measures, K.L. Young, M.A. Regan, and J.D. Lee Surrogate Distraction Measurement Techniques: The Lane Change Test, S. Mattes and A. Hallen Now You See It, Now You Don't: Visual Occlusion as a Surrogate Distraction Measurement Technique, J.P. Foley Distraction Assessment Methods Based on Visual Behavior and Event Detection, T.W. Victor, J. Engstrom, and J.L. Harbluk EFFECTS OF DISTRACTION ON DRIVING PERFORMANCE Cellular Phones and Driver Distraction, F.A. Drews and D.L. Strayer Sources of Distraction inside the Vehicle and Their Effects on Driving Performance, M. Bayly, K.L. Young, and M.A. Regan Distractions outside the Vehicle, T. Horberry and J. Edquist Distraction and Public Transport: Case Study of Bus Driver Distraction, P.M. Salmon, K.L. Young, and M.A. Regan DISTRACTION, CRASHES, AND CRASH RISK Sources of Driver Distraction, M.A. Regan, K.L. Young, J.D. Lee, and C.P. Gordon Crash Studies of Driver Distraction, C.P. Gordon Epidemiological Research on Driver Distraction, S.P. McEvoy and M.R. Stevenson Driver Distraction Exposure Research: A Summary of Findings, K.L. Young and M.A. Regan FACTORS MEDIATING THE EFFECTS OF DISTRACTION Factors Moderating the Impact of Distraction on Driving Performance and Safety, K.L. Young, M.A. Regan, and J.D. Lee Distraction and the Older Driver, S. Koppel, J.L. Charlton, and B. Fildes The Relationship between Driver Fatigue and Driver Distraction, A. Williamson DESIGN AND STANDARDIZATION European Approaches to Principles, Codes, Guidelines, and Checklists for In-Vehicle HMI, A. Stevens North American Approaches to Principles, Codes, Guidelines, and Checklists for In-Vehicle HMI, P.C. Burns Japanese Approaches to Principles, Codes, Guidelines, and Checklists for In-Vehicle HMI, M. Akamatsu Driver Interface Safety and Usability Standards: An Overview, P. Green PREVENTION AND MITIGATION STRATEGIES Real-Time Distraction Countermeasures, J. Engstrom and T.W. Victor Driving Task Demand-Based Distraction Mitigation, H. Zhang, M.R.H. Smith, and G.J. Witt Estimates of Driver Distraction, M.R.H. Smith, G.J. Witt, D.L. Bakowski, D. Leblanc, and J.D. Lee Designing Feedback to Mitigate Distraction, B. Donmez, L. Boyle, and J.D. Lee Driver Distraction Injury Prevention Countermeasures-Part 1: Data Collection, Legislation and Enforcement, Vehicle Fleet, Management, and Driver Licensing, M.A. Regan, K.L. Young, and J.D. Lee Driver Distraction Injury Prevention Countermeasures-Part 2: Education and Training, M. A. Regan, J.D. Lee, and K.L. Young Driver Distraction Injury Prevention Countermeasures-Part 3: Vehicle, Technology, and Road Design, T.W. Victor, M.A. Regan, J.D. Lee, and K.L. Young Government and Industry Perspectives on Driver Distraction, C. Tingvall, L. Eckstein, and M. Hammer CONCLUSIONS Some Concluding Remarks, M.A. Regan, K.L. Young, and J.D. Lee Index

John D. Lee

Papers

Trust in Automation: Designing for Appropriate Reliance

Trust, control strategies and allocation of function in human-machine systems.

Trust, self-confidence, and operators' adaptation to automation

Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator.

Driver Distraction : Theory, Effects, and Mitigation