Comparing driver reaction and mental workload of visual and auditory take-over request from perspective of driver characteristics and eye-tracking metrics

[1]  T. Sawaragi,et al.  Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving , 2022, Transportation Research Part F: Traffic Psychology and Behaviour.

[2]  Tetsuo Sawaragi,et al.  Adaptive multi-modal interface model concerning mental workload in take-over request during semi-autonomous driving , 2021 .

[3]  Tatsuya Suzuki,et al.  Drivers’ driving style and their take-over-control judgment , 2020 .

[4]  P. Paubel,et al.  Toward the Use of Pupillary Responses for Pilot Selection , 2020, Hum. Factors.

[5]  S. Brandenburg,et al.  Behavioral changes to repeated takeovers in automated driving: The drivers’ ability to transfer knowledge and the effects of takeover request process , 2020 .

[6]  Michael G. Lenné,et al.  Effects of Distraction in On-Road Level 2 Automated Driving: Impacts on Glance Behavior and Takeover Performance , 2020, Hum. Factors.

[7]  Hanna Yun,et al.  Multimodal warning design for take-over request in conditionally automated driving , 2020, European Transport Research Review.

[8]  Motoyuki Akamatsu,et al.  Effects of cognitive and visual loads on driving performance after take-over request (TOR) in automated driving. , 2020, Applied ergonomics.

[9]  Michael G. Lenné,et al.  Effects of different non-driving-related-task display modes on drivers’ eye-movement patterns during take-over in an automated vehicle , 2020 .

[10]  Qiuyang Tang,et al.  Olfactory Facilitation of Takeover Performance in Highly Automated Driving , 2020, Hum. Factors.

[11]  Gaojian Huang,et al.  Multimodal Cue Combinations: A Possible Approach to Designing In-Vehicle Takeover Requests for Semi-autonomous Driving , 2019, Proceedings of the Human Factors and Ergonomics Society Annual Meeting.

[12]  Lee Skrypchuk,et al.  The comparison of auditory, tactile, and multimodal warnings for the effective communication of unexpected events during an automated driving scenario , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[13]  Sky Eurich,et al.  Quality of control takeover following disengagements in semi-automated vehicles , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[14]  Lihua Huang,et al.  Quantitatively exploring the relationship between eye movement and driving behavior under the effect of different complex diagrammatic guide signs , 2019, Cognition, Technology & Work.

[15]  Jacob Haspiel,et al.  Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload , 2019, Transportation Research Part C: Emerging Technologies.

[16]  So Young Kim,et al.  Effects of Touch, Voice, and Multimodal Input, and Task Load on Multiple-UAV Monitoring Performance During Simulated Manned-Unmanned Teaming in a Military Helicopter , 2018, Hum. Factors.

[17]  Sebastiaan M. Petermeijer,et al.  Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays , 2018, Transportation Research Part F: Traffic Psychology and Behaviour.

[18]  Florian Alt,et al.  Your Eyes Tell: Leveraging Smooth Pursuit for Assessing Cognitive Workload , 2018, CHI.

[19]  Natasha Merat,et al.  Coming back into the loop: Drivers' perceptual-motor performance in critical events after automated driving. , 2017, Accident; analysis and prevention.

[20]  Natasha Merat,et al.  What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems , 2017 .

[21]  Marco Dozza,et al.  Drivers anticipate lead-vehicle conflicts during automated longitudinal control: Sensory cues capture driver attention and promote appropriate and timely responses. , 2016, Accident; analysis and prevention.

[22]  Régis Lobjois,et al.  The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior , 2016 .

[23]  Linda Ng Boyle,et al.  Drivers’ Engagement Level in Adaptive Cruise Control while Distracted or Impaired , 2015 .

[24]  M. Niezgoda,et al.  Towards testing auditory–vocal interfaces and detecting distraction while driving: A comparison of eye-movement measures in the assessment of cognitive workload , 2015 .

[25]  William Payre,et al.  Intention to use a fully automated car: attitudes and a priori acceptability , 2014 .

[26]  Ying Wang,et al.  The sensitivity of different methodologies for characterizing drivers’ gaze concentration under increased cognitive demand , 2014, Transportation Research Part F: Traffic Psychology and Behaviour.

[27]  Simone Benedetto,et al.  Driver workload and eye blink duration , 2011 .

[28]  Torbjørn Rundmo,et al.  An investigation of driver attitudes and behaviour in rural and urban areas in Norway. , 2010 .

[29]  P. Ulleberg,et al.  Personality, attitudes and risk perception as predictors of risky driving behaviour among young drivers , 2003 .

[30]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[31]  Jiro Sakamoto,et al.  Estimation of mental workload during automobile driving based on eye-movement measurement with a visible light camera , 2020, Transactions of the JSME (in Japanese).

[32]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .