scholarly journals Adaptive automation: automatically (dis)engaging automation during visually distracted driving

2018 ◽  
Vol 4 ◽  
pp. e166 ◽  
Author(s):  
Christopher D.D. Cabrall ◽  
Nico M. Janssen ◽  
Joost C.F. de Winter
Keyword(s):  
Future Research ◽  
Eye Tracker ◽  
Steering Control ◽  
Automated Driving ◽  
Human Errors ◽  
Adaptive Automation ◽  
Visual Distraction ◽  
Improved Performance ◽  
Lateral Error ◽  
Acceptance Rating

Background Automated driving is often proposed as a solution to human errors. However, fully automated driving has not yet reached the point where it can be implemented in real traffic. This study focused on adaptively allocating steering control either to the driver or to an automated pilot based on momentary driver distraction measured from an eye tracker. Methods Participants (N = 31) steered a simulated vehicle with a fixed speed, and at specific moments were required to perform a visual secondary task (i.e., changing a CD). Three conditions were tested: (1) Manual driving (Manual), in which participants steered themselves. (2) An automated backup (Backup) condition, consisting of manual steering except during periods of visual distraction, where the driver was backed up by automated steering. (3) A forced manual drive (Forced) condition, consisting of automated steering except during periods of visual distraction, where the driver was forced into manual steering. In all three conditions, the speed of the vehicle was automatically kept at 70 km/h throughout the drive. Results The Backup condition showed a decrease in mean and maximum absolute lateral error compared to the Manual condition. The Backup condition also showed the lowest self-reported workload ratings and yielded a higher acceptance rating than the Forced condition. The Forced condition showed a higher maximum absolute lateral error than the Backup condition. Discussion In conclusion, the Backup condition was well accepted, and significantly improved performance when compared to the Manual and Forced conditions. Future research could use a higher level of simulator fidelity and a higher-quality eye-tracker.

scholarly journals Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

2019 ◽  
Vol 61 (7) ◽  
pp. 1037-1065 ◽  
Author(s):  
Callum D. Mole ◽  
Otto Lappi ◽  
Oscar Giles ◽  
Gustav Markkula ◽  
Franck Mars ◽  
...  
Keyword(s):  
Motor Coordination ◽  
Reaction Times ◽  
Manual Control ◽  
Future Research ◽  
Steering Control ◽  
Automated Driving ◽  
Motor Behaviors ◽  
Human Driver ◽  
Active Feedback ◽  
The Impact

Objective: To present a structured, narrative review highlighting research into human perceptual-motor coordination that can be applied to automated vehicle (AV)–human transitions. Background: Manual control of vehicles is made possible by the coordination of perceptual-motor behaviors (gaze and steering actions), where active feedback loops enable drivers to respond rapidly to ever-changing environments. AVs will change the nature of driving to periods of monitoring followed by the human driver taking over manual control. The impact of this change is currently poorly understood. Method: We outline an explanatory framework for understanding control transitions based on models of human steering control. This framework can be summarized as a perceptual-motor loop that requires (a) calibration and (b) gaze and steering coordination. A review of the current experimental literature on transitions is presented in the light of this framework. Results: The success of transitions are often measured using reaction times, however, the perceptual-motor mechanisms underpinning steering quality remain relatively unexplored. Conclusion: Modeling the coordination of gaze and steering and the calibration of perceptual-motor control will be crucial to ensure safe and successful transitions out of automated driving. Application: This conclusion poses a challenge for future research on AV-human transitions. Future studies need to provide an understanding of human behavior that will be sufficient to capture the essential characteristics of drivers reengaging control of their vehicle. The proposed framework can provide a guide for investigating specific components of human control of steering and potential routes to improving manual control recovery.

Comparison of Visual Sampling Patterns Under Simulated L2 and L0 Systems

2018 ◽  
Vol 62 (1) ◽  
pp. 1826-1826
Author(s):  
Nathan Hatfield ◽  
Yusuke Yamani ◽  
Dakota B. Palmer ◽  
Nicole D. Karpinsky ◽  
William J. Horrey ◽  
...  
Keyword(s):  
Eye Movements ◽  
Driving Simulator ◽  
Human Information Processing ◽  
Steering Wheel ◽  
Future Research ◽  
Eye Tracker ◽  
Net Benefit ◽  
Automated Driving ◽  
Information Processing Model ◽  
Young Drivers

Automated driving systems (ADS) partially or fully perform or assist with primary driving functions. According to SAE J3016 (SAE, 2016), ADS can subsume driving tasks traditionally reserved for humans, ranging from L0 (no automation) to L5 (full automation), creating varying degrees of driver interaction and responsibility. However, the literature on human-automation interaction indicates that human operators may perform at a suboptimal level when interacting with automated support systems (Parasuraman & Riley, 1997), reducing the net benefit that automation can bring while also simultaneously increasing the potential for unforeseen human errors. Yamani and Horrey (in press) proposed a theoretical framework of human-automation interaction building upon a human information-processing model (Wickens, Hollands, Banbury, & Parasuraman, 2013) that accounts for human performance when interacting with varying types and levels of automation (Parasuraman, Sheridan, & Wickens, 2000). Following the model by Yamani and Horrey (in press), we hypothesized that when the ADS is perceived to be reliable, drivers engaging with such systems (e.g. L2) would exhibit eye movements no better or worse than the drivers engaged with manual or L0 driving since the drivers allocate their reserved or spare resources to other driving-irrelevant activities such as mind wandering or task irrelevant thoughts (Yanko & Spalek, 2014). The current driving simulator study compared young drivers’ eye movements across four unique scenarios in either L0 or L2 driving systems. We asked participants to complete a three-phased skill-based training program (RAPT-3; see Unverricht, Samuel, & Yamani for review) proven effective to improve young drivers’ ability to anticipate latent hazards, immediately followed by the evaluation of their eye movements in either L0 or L2 systems using a head-mounted eye tracker and a driving simulator. Participants in the L2 condition were instructed that the system detects and mitigates existing and latent threats on the forward roadway while maintaining appropriate speed and lateral positioning for the duration of the drive. To ensure similarity between both systems, L2 participants were required to position their hands on the steering wheel and feet above the pedal. No hazards materialized in any of the four driving scenarios. Data showed similar breadths of eye movements for the drivers of the L2 and L0 systems both horizontally [M = 36.5 vs. 36.3 pixels; L2 and L0, respectively] and vertically [M = 26.9 vs. 34.5 pixels] and no difference in mean fixation durations [M = 367 vs. 333 ms for L2 and L0 conditions]. However, data indicated substantial differences between L0 and L2 conditions for number of fixations, with L2 drivers fixating less frequently than L0 drivers, [M = 687 vs. 796 fixations, t (22) = 2.53, B10 = 3.23]. The results imply that L2 drivers may sample information from the forward roadway less often than L0 drivers, suggesting the mobilization of spare resources for non-driving related tasks. Future research should examine the relationship between conveyed system reliability and attention allocation for drivers of ADS with different automation levels. In summary, the current results support Yamani and Horrey’s model and offer potential implications for the design of autonomous systems and the NHTSA automation guidelines to consider the perceived reliability of lower level ADS towards ascribing the role of the driver when the driving task is either partially or fully automated.

scholarly journals Redesigning Today’s Driving Automation Toward Adaptive Backup Control With Context-Based and Invisible Interfaces

2020 ◽  
Vol 62 (2) ◽  
pp. 211-228 ◽  
Author(s):  
Christopher D. D. Cabrall ◽  
Jork C. J. Stapel ◽  
Riender Happee ◽  
Joost C. F. de Winter
Keyword(s):  
Driving Simulator ◽  
False Alarms ◽  
Eye Tracker ◽  
Negative Consequences ◽  
Automated Driving ◽  
Automated Control ◽  
Visual Distraction ◽  
The Status ◽  
Primary Driver ◽  
Back Task

Objective We investigated a driver monitoring system (DMS) designed to adaptively back up distracted drivers with automated driving. Background Humans are likely inadequate for supervising today’s on-road driving automation. Conversely, backup concepts can use eye-tracker DMS to retain the human as the primary driver and use computerized control only if needed. A distraction DMS where perceived false alarms are minimized and the status of the backup is unannounced might reduce problems of distrust and overreliance, respectively. Experimental research is needed to assess the viability of such designs. Methods In a driving simulator, 91 participants either supervised driving automation ( auto-hand-on-wheel vs. auto-hands-off-wheel), drove with different forms of DMS-induced backup control ( eyes-only-backup vs. eyes-plus-context-backup; visible-backup vs. invisible-backup), or drove without any automation. All participants performed a visual N-back task throughout. Results Supervised driving automation increased visual distraction and hazard non-responses compared to backup and conventional driving. Auto-hand-on-wheel improved response generation compared to auto-hands-off-wheel. Across entire driving trials, the backup improved lateral performance compared to conventional driving. Without negatively impacting safety, the eyes-plus-context-backup DMS reduced unnecessary automated control compared to the eyes-only-backup DMS conditions. Eyes-only-backup produced low satisfaction ratings, whereas eyes-plus-context-backup satisfaction was on par with automated driving. There were no appreciable negative consequences attributable to the invisible-backup driving automation. Conclusions We have demonstrated preliminary feasibility of DMS designs that incorporate driving context information for distraction assessment and suppress their status indication. Application An appropriately designed DMS can enable benefits for automated driving as a backup.

Locus of Control, Safety Attitudes and Involvement in Hazardous Events in Indian Army Aviators

2013 ◽  
Vol 3 (1) ◽  
pp. 9-18 ◽  
Author(s):  
Catherine Joseph ◽  
Suhasini Reddy ◽  
Kanwal Kashore Sharma
Keyword(s):  
Locus Of Control ◽  
Future Research ◽  
Research Needs ◽  
Negative Attitudes ◽  
Human Errors ◽  
Safety Attitudes ◽  
Positive Correlation ◽  
Self Confidence

Locus of control (LOC), safety attitudes, and involvement in hazardous events were studied in 205 Indian Army aviators using a questionnaire-based method. A positive correlation was found between external LOC and involvement in hazardous events. Higher impulsivity and anxiety, and decreased self-confidence, safety orientation, and denial were associated with a greater number of hazardous events. Higher external LOC was associated with higher impulsivity, anxiety, and weather anxiety and with lower self-confidence, safety orientation, and denial. Internal LOC was associated with increased self-confidence, safety orientation, and denial. Hazardous events and self-confidence were higher in those involved in accidents than those not involved in accidents. Future research needs to address whether training can effectively modify LOC and negative attitudes, and whether this would cause a reduction in, and better management of, human errors.

Independent wheel control system design for highly automated driving

2021 ◽  
pp. 095440702110065
Author(s):  
Shihuan Li ◽  
Lei Wang
Keyword(s):  
Control System ◽  
Control Method ◽  
Autonomous Driving ◽  
Control System Design ◽  
Steering Control ◽  
Automated Driving ◽  
Accuracy And Precision ◽  
Actual Observation ◽  
Control Scheme ◽  
Interference Measurement

For L4 and above autonomous driving levels, the automatic control system has been redundantly designed, and a new steering control method based on brake has been proposed; a new dual-track model has been established through multiple driving tests. The axle part of the model was improved, the accuracy of the transfer function of the model was verified again through acceleration-slide tests; a controller based on interference measurement was designed on the basis of the model, and the relationships between the controller parameters was discussed. Through the linearization of the controller, the robustness of uncertain automobile parameters is discussed; the control scheme is tested and verified through group driving test, and the results prove that the accuracy and precision of the controller meet the requirements, the robustness stability is good. Moreover, the predicted value of the model fits well with the actual observation value, the proposal of this method provides a new idea for avoiding car out of control.

First encounter effects in testing of highly automated vehicles during two experimental occasions – The need for recurrent testing

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jonas Andersson ◽  
Azra Habibovic ◽  
Daban Rizgary
Keyword(s):  
Driver Behavior ◽  
Added Value ◽  
Future Research ◽  
Gaze Behavior ◽  
Repeated Testing ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Perceived Safety ◽  
Repeated Interaction ◽  
Over Time

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver ( n = 8 n=8 ) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

scholarly journals A Brief Taxonomy of Hybrid Intelligence

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 633-643
Author(s):  
Niccolo Pescetelli
Keyword(s):  
Prediction Models ◽  
Collective Intelligence ◽  
Machine Intelligence ◽  
Future Research ◽  
Human Errors ◽  
Wide Range ◽  
Hybrid Intelligence ◽  
Forecasting System ◽  
Problem Solvers

As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field.

The Cybersecurity Awareness Training Model (CATRAM)

2021 ◽  
pp. 233-257
Keyword(s):  
Education Institution ◽  
Training Model ◽  
Future Research ◽  
Awareness Training ◽  
Human Errors ◽  
Canadian Higher Education ◽  
Awareness Programs ◽  
Multiple Case ◽  
People’S Attitudes

This chapter presents the outcome of one empirical research study that assess the implementation and validation of the cybersecurity awareness training model (CATRAM), designed as a multiple-case study in a Canadian higher education institution. Information security awareness programs have become unsuccessful to change people's attitudes in recognizing, stopping, or reporting cyberthreats within their corporate environment. Therefore, human errors and actions continue to demonstrate that we as humans are the weakest links in cybersecurity. The chapter studies the most recent cybersecurity awareness programs and its attributes. Furthermore, the authors compiled recent awareness methodologies, frameworks, and approaches. The cybersecurity awareness training model (CATRAM) has been created to deliver training to different corporate audiences, each of these organizational units with peculiar content and detached objectives. They concluded their study by addressing the necessity of future research to target new approaches to keep cybersecurity awareness focused on the everchanging cyberthreat landscape.

The Cybersecurity Awareness Training Model (CATRAM)

2022 ◽  
pp. 501-520
Author(s):  
Regner Sabillon
Keyword(s):  
Education Institution ◽  
Training Model ◽  
Future Research ◽  
Awareness Training ◽  
Human Errors ◽  
Canadian Higher Education ◽  
Awareness Programs ◽  
Multiple Case ◽  
People’S Attitudes

This chapter presents the outcome of one empirical research study that assess the implementation and validation of the cybersecurity awareness training model (CATRAM), designed as a multiple-case study in a Canadian higher education institution. Information security awareness programs have become unsuccessful to change people's attitudes in recognizing, stopping, or reporting cyberthreats within their corporate environment. Therefore, human errors and actions continue to demonstrate that we as humans are the weakest links in cybersecurity. The chapter studies the most recent cybersecurity awareness programs and its attributes. Furthermore, the author compiled recent awareness methodologies, frameworks, and approaches. The cybersecurity awareness training model (CATRAM) has been created to deliver training to different corporate audiences, each of these organizational units with peculiar content and detached objectives. They concluded their study by addressing the necessity of future research to target new approaches to keep cybersecurity awareness focused on the everchanging cyberthreat landscape.

Linking Business Strategy and Knowledge Management Capabilities for Organizational Effectiveness

2012 ◽  
pp. 186-207
Author(s):  
Trevor A. Smith ◽  
Annette M. Mills ◽  
Paul Dion
Keyword(s):  
Knowledge Management ◽  
Organizational Effectiveness ◽  
Business Strategy ◽  
Future Research ◽  
Knowledge Process ◽  
Knowledge Resources ◽  
Improved Performance ◽  
Using Data ◽  
Knowledge Management Capabilities ◽  
Management Capabilities

The effective management of knowledge resources is a key imperative for firms that want to leverage their knowledge assets for competitive advantage and improved performance. However, most firms do not attain the required performance levels even when programs are in place for managing knowledge resources. Research suggests this shortcoming can be addressed by linking knowledge management to business strategy. This study examines a model that links business strategy to knowledge management capabilities and organizational effectiveness. Using data collected from 189 managers, the results suggest that business strategy is a key driver of knowledge capabilities, and that both business strategy and knowledge capabilities impact organizational effectiveness. Additionally, the authors’ findings indicate that knowledge infrastructure capability is a key imperative for effective knowledge process capability. Managerial implications, limitations and opportunities for future research are also discussed.

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