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Objective: This paper considers the influence of "Humans and Automation: Use, Misuse, Disuse, Abuse" and examines how it relates to the evolving issue of human-automation interaction. Background: Automation presents important practical challenges that can dramatically affect satisfaction, performance, and safety; philosophical challenges also arise as automation changes the nature of work and human cognition. Method: Papers cited by and citing "Humans and Automation" were reviewed to identify enduring and emerging themes in human-automation research. Results: "Humans and Automation" emerges as an important node in the network of automation-related papers, citing many and being cited by many recent influential automation-related papers. In their article, Parasuraman and Riley (1997) integrated previous research and identified differing expectations across designers, managers, and operators regarding the need to support operators as a source of automation problems. They also foresaw and inspired research that addresses problems of overreliance and underreliance on automation. Conclusion: This pivotal article and associated research show that even though automation seems to relieve people of tasks, automation requires more, not less, attention to training, interface design, and interaction design. The original article also alludes to the emergence of vicious cycles and dysfunctional meta-control. These problems reflect the coevolution of automation and humans, in which both adapt to the responses of the other. Application: Understanding this coevolution has important philosophical implications for the nature of human cognition and practical implications for satisfaction, performance, and safety.

Objective: This brief review covers the 50 years of driving-related research published in Human Factors, its contribution to driving safety, and emerging challenges. Background: Many factors affect driving safety, making it difficult to assess the impact of specific factors such as driver age, cell phone distractions, or collision warnings. Method: The author considers the research themes associated with the approximately 270 articles on driving published in Human Factors in the past 50 years. Results: To a large extent, current and past research has explored similar themes and concepts. Many articles published in the first 25 years focused on issues such as driver impairment, individual differences, and perceptual limits. Articles published in the past 25 years address similar issues but also point toward vehicle technology that can exacerbate or mitigate the negative effect of these issues. Conceptual and computational models have played an important role in this research. Conclusion: Improved crashworthiness has contributed to substantial improvements in driving safety over the past 50 years, but future improvements will depend on enhancing driver performance and perhaps, more important, improving driver behavior. Developing models to guide this research will become more challenging as new technology enters the vehicle and shifts the focus from driver performance to driver behavior. Application: Over the past 50 years, Human Factors has accumulated a large base of driving-related research that remains relevant for many of today's design and policy concerns.

ObjectiveThis study examined attitudes toward self-driving vehicles and the factors motivating those attitudes.BackgroundSelf-driving vehicles represent potentially transformative technology, but achieving this potential depends on consumers' attitudes. Ratings from surveys estimate these attitudes, and open-ended comments provide an opportunity to understand their basis.MethodA nationally representative sample of 7,947 drivers in 2016 and 8,517 drivers in 2017 completed the J.D. Power U.S. Tech Choice StudySM, which included a rating for level of trust with self-driving vehicles and associated open-ended comments. These open-ended comments are qualitative data that can be analyzed quantitatively using structural topic modeling. Structural topic modeling identifies common themes, extracts prototypical comments for each theme, and assesses how the survey year and rating affect the prevalence of these themes.ResultsStructural topic modeling identified 13 topics, such as "Tested for a long time," which was strongly associated with positive ratings, and "Hacking & glitches," which was strongly associated with negative ratings. The topics of "Self-driving accidents" and "Trust when mature" were more prominent in 2017 compared with 2016.ConclusionStructural topic modeling reveals reasons underlying consumer attitudes toward vehicle automation. These reasons align with elements typically associated with trust in automation, as well as elements that mediate perceived risk, such as the desire for control as well as societal, relational, and experiential bases of trust.ApplicationThe analysis informs the debate concerning how safe is safe enough for automated vehicles and provides initial indicators of what makes such vehicles feel safe and trusted.

Objective: This special section brings together diverse research regarding driver interaction with advanced automotive technology to guide design of increasingly automated vehicles. Background: Rapidly evolving vehicle automation will likely change cars and trucks more in the next 5 years than the preceding 50, radically redefining what it means to drive. Method: This special section includes 10 articles from European and North American researchers reporting simulator and naturalistic driving studies. Results: Little research has considered the consequences of fully automated driving, with most focusing on lane-keeping and speed control systems individually. The studies reveal two underlying design philosophies: automate driving versus support driving. Results of several studies, consistent with previous research in other domains, suggest that the automate philosophy can delay driver responses to incidents in which the driver has to intervene and take control from the automation. Understanding how to orchestrate the transfer or sharing of control between the system and the driver, particularly in critical incidents, emerges as a central challenge. Conclusion: Designers should not assume that automation can substitute seamlessly for a human driver, nor can they assume that the driver can safely accommodate the limitations of automation. Designers, policy makers, and researchers must give careful consideration to what role the person should have in highly automated vehicles and how to support the driver if the driver is to be responsible for vehicle control. As in other domains, driving safety increasingly depends on the combined performance of the human and automation, and successful designs will depend on recognizing and supporting the new roles of the driver.

Objective This paper reviews recent articles related to human trust in automation to guide research and design for increasingly capable automation in complex work environments. Background Two recent trends—the development of increasingly capable automation and the flattening of organizational hierarchies—suggest a reframing of trust in automation is needed. Method Many publications related to human trust and human–automation interaction were integrated in this narrative literature review. Results Much research has focused on calibrating human trust to promote appropriate reliance on automation. This approach neglects relational aspects of increasingly capable automation and system-level outcomes, such as cooperation and resilience. To address these limitations, we adopt a relational framing of trust based on the decision situation, semiotics, interaction sequence, and strategy. This relational framework stresses that the goal is not to maximize trust, or to even calibrate trust, but to support a process of trusting through automation responsivity. Conclusion This framing clarifies why future work on trust in automation should consider not just individual characteristics and how automation influences people, but also how people can influence automation and how interdependent interactions affect trusting automation. In these new technological and organizational contexts that shift human operators to co-operators of automation, automation responsivity and the ability to resolve conflicting goals may be more relevant than reliability and reliance for advancing system design. Application A conceptual model comprising four concepts—situation, semiotics, strategy, and sequence—can guide future trust research and design for automation responsivity and more resilient human–automation systems.

ObjectiveThe aim of this special issue is to bring together the latest research related to driver interaction with various types of vehicle automation.BackgroundVehicle 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.MethodThis 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.ResultsThe 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.ConclusionsCollectively, 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.

Objective: This study uses a dyadic approach to understand human-agent cooperation and system resilience. Background: Increasingly capable technology fundamentally changes human-machine relationships. Rather than reliance on or compliance with more or less reliable automation, we investigate interaction strategies with more or less cooperative agents. Method: A joint-task microworld scenario was developed to explore the effects of agent cooperation on participant cooperation and system resilience. To assess the effects of agent cooperation on participant cooperation, 36 people coordinated with a more or less cooperative agent by requesting resources and responding to requests for resources in a dynamic task environment. Another 36 people were recruited to assess effects following a perturbation in their own hospital. Results: Experiment 1 shows people reciprocated the cooperative behaviors of the agents; a low-cooperation agent led to less effective interactions and less resource sharing, whereas a high-cooperation agent led to more effective interactions and greater resource sharing. Experiment 2 shows that an initial fast-tempo perturbation undermined proactive cooperation—people tended to not request resources. However, the initial fast tempo had little effect on reactive cooperation—people tended to accept resource requests according to cooperation level. Conclusion: This study complements the supervisory control perspective of human-automation interaction by considering interdependence and cooperation rather than the more common focus on reliability and reliance. Application: The cooperativeness of automated agents can influence the cooperativeness of human agents. Design and evaluation for resilience in teams involving increasingly autonomous agents should consider the cooperative behaviors of these agents.

Cognitive distraction represents an important and growing traffic safety issue, particularly with the increasing computerization of cars. The target paper in this special section describes a protocol for assessing the distraction potential of information and entertainment systems. Cognitive distraction has specific relevance to the challenges facing driving safety but also reflects the more pervasive challenge of generalizing findings in the face of complex contextual and compensatory influences. Peer commentaries from five driving safety experts sketch paths forward in assessing the distraction potential of in-vehicle information technology. A simple, definitive statement regarding the risk of talking to your car is appealing, but the complexity of driver behavior may make such a statement unachievable.

Objective This paper investigates driver engagement with vehicle automation and the transition to manual control in the context of a phenomenon that we have termed vicarious steering—drivers steering when the vehicle is under automated control. Background Automated vehicles introduce many challenges, including disengagement from the driving task and out-of-the-loop performance decrement. We examine drivers' steering behavior when the automation is engaged, and steering input has no effect on the vehicle state. Such vicarious steering is a potential indicator of engagement for evaluating automated vehicles. Method A total of 32 female and 32 male drivers between 25 and 55 years of age participated in this experiment. A 2 × 2 between-subject design combined control algorithms and instructed responsibility. The control algorithms (lane centering and adaptive) were intended to convey the capability of the automation. The adaptive algorithm drifted across the lane center when latent hazards were present. The instructed levels of responsibility (driver primarily responsible and automation primarily responsible) were intended to replicate the admonitions of owners' manuals. Results The adaptive algorithm increased vicarious steering ( p < .001), but instructed responsibility did not ( p = .67), and there was no interaction between the algorithm and the responsibility ( p = .75). Vicarious steering was associated with an increase in transitions to manual control and glances to the road but was negatively associated with driving performance immediately after the transition to manual control. Conclusion Vicarious steering is a promising indicator of driver engagement when the vehicle is under automated control and automation algorithms can promote engagement.

Objective: In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. Background: Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. Method: The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction—glance duration, glance history, and glance location—on how well the algorithms predicted crash risk. Results: Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history. Augmenting glance duration with other elements of glance behavior—1.5th power of duration and duration weighted by glance location—produced similar prediction performance as glance duration alone. Conclusions: The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. Application: The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.

In the United States, around 20 percent of all Police-reported road crashes involve driver distraction as a contributing factor. The situation is similar in other countries. This book adds to the accumulating evidence-base on driver distraction and inattention. It provides important new perspectives on the definition and meaning of driver distraction and inattention, the mechanisms that characterize them, the measurement of their effects, strategies for mitigating their effects, and recommendations for further research

Defines driver distraction, discusses causes, and explains how to measure acceptable and unacceptable levels of distraction. This book explores ways to mitigate driver distraction, as well as countermeasures, including vehicle design and effective legislation