Task 4: Distraction Mitigation John D. Lee University of Iowa January 3, 2020

1. SAfety VEhicles using adaptive Interface TechnologyPhase 1 Research ProgramQuarterly Program Review Task 4: Distraction Mitigation John D. Lee University of Iowa January 3, 2020

2. Task Description • Team Members • John Lee (lead) • Dan McGehee • Linda Boyle • Birsen Donmez • Objectives • Identify and validate mitigation strategies and their appropriate applications • Deliverables • The specification of the most effective combination of telematics management interventions • includes the design, data analysis, and recommendations to be integrated into Task 4B • Final report based on the literature review, the identification of general distraction mitigation strategies, and an updated task definition document (Task 4A)

3. Task Description • Schedule (Task 4A: Literature Review) • 4.A.1 Identify general domains and mitigation approaches to focus literature review (complete) • 4.A.2 Define and describe potential distractions (in collaboration with task 5) • 4.A.3 Define and describe potential mitigation strategies (e.g., lockout, interruption) (complete) • 4.A.4 Identify costs and limits and benefits, such as annoyance, of each mitigation strategy (complete)

4. Task Description • Schedule (Task 4B: Identify Countermeasures) • 4.B.1 Extract data from literature review to link mitigation strategies to potential distracting interactions (in collaboration with task 5) • 4.B.2 Conduct focus groups to evaluate preliminary strategies and identify additional mitigation strategies (complete) • 4.B.3 Conduct a cognitive task analysis (OFM-COG) to link system state, driver state, and the mitigation strategies (in collaboration with task 5) • 4.B.4 Conduct simulator studies to evaluate mitigation strategies driver acceptance

5. Literature Review: Task 4A • Research Areas of Interest • Distraction issues • Current and past mitigation strategies • Issues with mitigation strategies • Potential research areas • Key Source Material • Lee, J. D., & See, K. A. (in press). Trust in Automation: Designing for Appropriate Reliance. Human Factors. • Parasuraman, R., & Hancock, P. A. (2001). Adaptive control of mental workload. In P. A. Hancock & P. A. Desmond (Eds.), Stress, Workload, and Fatigue (pp. 305-320). Mahwah, New Jersey: Lawrence Erlbaum Associates, Publishers. • Parasuraman, R., & Riley, V. (1997). Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors, 39(2), 230-253. • Sheridan, T. (2002). Humans and Automation: System Design and Research Issues: Wiley.

6. Literature review: Task 4A • Major Findings • Defined 12 mitigation strategies • By level of automation • By driving/non-driving task • By system/driver induced • Identified potential research areas • Identified pros/cons of mitigation strategies

7. Literature review: Task 4A

8. Literature review: Task 4A • Summary/Hypotheses • Non-driving related strategies need to be further explored • Driver initiation of a strategy is likely to be more acceptable than system initiation • The level of automation should change based on driver state and driving condition • Need to explore… • whether driver initiated strategies are more helpful in distracted situations • whether the driver perceives driver initiated strategies to be more helpful • the effects of the system falsely adapting the level of automation to driver state and traffic condition

9. Research: Task 4B FOCUS GROUPS • Research Objectives • To determine which activities are distracting to drivers and what mitigation strategies are preferred • Outcomes: help guide development of parameters for distraction mitigation strategies • Methods • Four different focus groups • Facilities/Apparatus/Subjects • 24 participants total • Two age groups: younger (18-35, mean 29), and older (36+, mean 49) • All participants were recruited by temporary agencies by age and balanced by gender, and education level • Two locations—Iowa City (rural drivers) and Seattle (urban drivers) • Issues/Concerns • None

10. Taxonomy of mitigation strategies High Interrupt interaction Increase alert intensity Lockout functions Change alert intensity Call screening DEGREE OF INTERVENTION Vehicle System Driver LOCUS OF CONTROL Prioritize messages Voice input vs. manual input Low

11. Research: Task 4B • Results/Insights: • Technology should be viewed as a helpful ‘co-pilot’, not as an annoying ‘back seat driver’ • ‘Filtering’ (e.g. caller ID) or context specific display of information is beneficial • Information should be very accessible (e.g., not on the phone display) • Vehicle subsystems should be adjustable based on driver state (e.g., is driver on the phone) • Intelligent systems are good if they can be tailored to individual drivers • Simple and intuitive interface design a key need —specifically related to information input • Drivers appear more susceptible to in-vehicle distractions than out-of-vehicle distractions • Drivers believe mitigation strategies will work only if reliable and cost-effective

12. Research: Task 4B • Objectives • Assess driver perception of “false system adaptation” • Assess age-related differences in the acceptance of adaptive systems • Assess how high and low levels of adaptation interact with high and low levels of distraction • Independent variables: • Age • Level of automation • Level of distraction • Appropriate and inappropriate adaptation • Dependent variables • Reaction time to safety-relevant driving events • Trust in the system • IVIS Usability questionnaire (utility and acceptance) • Apparatus/Subjects • 40 participants between 35-55 and 65-75 years of age • Design • Mixed between and within subject design (Adaptation (b) X Age (b) X Automation (w) X Distraction (w)