Reliable but multi-dimensional cognitive demand in operating partially automated vehicles: implications for real-world automation research.

The reliability of cognitive demand measures in controlled laboratory settings is well-documented; however, limited research has directly established their stability under real-life and high-stakes conditions, such as operating automated technology on actual highways. Partially automated vehicles have advanced to become an everyday mode of transportation, and research on driving these advanced vehicles requires reliable tools for evaluating the cognitive demand on motorists to sustain optimal engagement in the driving process. This study examined the reliability of five cognitive demand measures, while participants operated partially automated vehicles on real roads across four occasions.

Automated driving experiences, attention, and intentions following extensive on-road usage of a level 2 automation vehicle.

Introduction: An on-road study was conducted to examine the effects of level 2 automation on the stressfulness and enjoyment of driving and driving attention following prolonged usage. The study also examined the changes in the automated driving experience and attention over time as well as important predictors such as pre-driving trust in technology and attitudes toward automated systems.

Method: Motorists who had never used automated systems drove a level 2 automation vehicle for a 6-8 week period.

Multitasking Induced Contextual Blindness.

Objective: To examine the impact of secondary task performance on contextual blindness arising from the suppression and masking of temporal and spatial sequence learning.

Background: Dual-task scenarios can lead to a diminished ability to use environmental cues to guide attention, a phenomenon that is related to multitasking-induced inattentional blindness. This research aims to extend the theoretical understanding of how secondary tasks can impair attention and memory processes in sequence learning and access.

Driver behavior while using Level 2 vehicle automation: a hybrid naturalistic study.

Vehicle automation is becoming more prevalent. Understanding how drivers use this technology and its safety implications is crucial. In a 6-8 week naturalistic study, we leveraged a hybrid naturalistic driving research design to evaluate driver behavior with Level 2 vehicle automation, incorporating unique naturalistic and experimental control conditions.

This Is Your Brain on Autopilot 2.0: The Influence of Practice on Driver Workload and Engagement During On-Road, Partially Automated Driving.

Objective: This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement.

Background: Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect "edge cases" that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively.

Dynamic workload measurement and modeling: Driving and conversing.

Tillman et al. (2017) used evidence-accumulation modeling to ascertain the effects of a conversation (either with a passenger or on a hands-free cell phone) on a drivers' mental workload. They found that a concurrent conversation increased the response threshold but did not alter the rate of evidence accumulation.

The persistence of distraction: The hidden costs of intermittent multitasking.

We examined the hidden costs of intermittent multitasking. Participants performed a pursuit-tracking task (Experiment 1) or drove in a high-fidelity driving simulator (Experiment 2) by itself or while concurrently performing an easy or difficult backwards counting task that periodically started and stopped, creating on-task and off-task multitasking epochs. A novel application of the Detection Response Task (DRT), a standardized protocol for measuring cognitive workload (ISO 17488, 2016), was used to measure performance in the on-task and off-task intervals.

This Is Your Brain on Autopilot: Neural Indices of Driver Workload and Engagement During Partial Vehicle Automation.

Objective: This research explores the effect of partial vehicle automation on neural indices of mental workload and visual engagement during on-road driving.

Background: There is concern that the introduction of automated technology in vehicles may lead to low driver stimulation and subsequent disengagement from the driving environment. Simulator-based studies have examined the effect of automation on a driver's cognitive state, but it is unknown how the conclusions translate to on-road driving.

No Difference in Arousal or Cognitive Demands Between Manual and Partially Automated Driving: A Multi-Method On-Road Study.

Introduction: Partial driving automation is not always reliable and requires that drivers maintain readiness to take over control and manually operate the vehicle. Little is known about differences in drivers' arousal and cognitive demands under partial automation and how it may make it difficult for drivers to transition from automated to manual modes. This research examined whether there are differences in drivers' arousal and cognitive demands during manual versus partial automation driving.

Age-Related Differences in the Cognitive, Visual, and Temporal Demands of In-Vehicle Information Systems.

In-vehicle information systems (IVIS) refer to a collection of features in vehicles that allow motorists to complete tasks (often unrelated to driving) while operating the vehicle. These systems may interfere, to a greater extent, with older drivers' ability to attend to the visual and cognitive demands of the driving environment. The current study sought to examine age-related differences in the visual, cognitive and temporal demands associated with IVIS interactions.

Assessing the visual and cognitive demands of in-vehicle information systems.

Background: New automobiles provide a variety of features that allow motorists to perform a plethora of secondary tasks unrelated to the primary task of driving. Despite their ubiquity, surprisingly little is known about how these complex multimodal in-vehicle information systems (IVIS) interactions impact a driver's workload.

Results: The current research sought to address three interrelated questions concerning this knowledge gap: (1) Are some task types more impairing than others? (2) Are some modes of interaction more distracting than others? (3) Are IVIS interactions easier to perform in some vehicles than others? Depending on the availability of the IVIS features in each vehicle, our testing involved an assessment of up to four task types (audio entertainment, calling and dialing, text messaging, and navigation) and up to three modes of interaction (e.

Visual and Cognitive Demands of CarPlay, Android Auto, and Five Native Infotainment Systems.

Objective: The present research compared and contrasted the workload associated with using in-vehicle information systems commonly available in five different automotive original equipment manufacturers (OEMs) with that of CarPlay and Android Auto when used in the same vehicles.

Background: A growing trend is to provide access to portable smartphone-based systems (e.g.

The smartphone and the driver's cognitive workload: A comparison of Apple, Google, and Microsoft's intelligent personal assistants.

The goal of this research was to examine the impact of voice-based interactions using 3 different intelligent personal assistants (Apple's , Google's for Android phones, and Microsoft's ) on the cognitive workload of the driver. In 2 experiments using an instrumented vehicle on suburban roadways, we measured the cognitive workload of drivers when they used the voice-based features of each smartphone to place a call, select music, or send text messages. Cognitive workload was derived from primary task performance through video analysis, secondary-task performance using the Detection Response Task (DRT), and subjective mental workload.

Talking to your car can drive you to distraction.

This research examined the impact of in-vehicle information system (IVIS) interactions on the driver's cognitive workload; 257 subjects participated in a weeklong evaluation of the IVIS interaction in one of ten different model-year 2015 automobiles. After an initial assessment of the cognitive workload associated with using the IVIS, participants took the vehicle home for 5 days and practiced using the system. At the end of the 5 days of practice, participants returned and the workload of these IVIS interactions was reassessed.

Driven to Distraction.

We address several themes that emerged in the commentaries related to our target article. First, we consider the relationship between cognitive distraction and crash risk. Second, we discuss the development of our cognitive distraction scale.

Assessing Cognitive Distraction in the Automobile.

Objective: The objective was to establish a systematic framework for measuring and understanding cognitive distraction in the automobile.

Background: Driver distraction from secondary in-vehicle activities is increasingly recognized as a significant source of injuries and fatalities on the roadway.

Method: Across three studies, participants completed eight in-vehicle tasks commonly performed by the driver of an automobile.

Hierarchical control and driving.

We manipulated primary task predictability and secondary task workload in the context of driving an automobile. As the driving task became less predictable (by adding wind gusts), more attention was required to maintain lane position. When drivers concurrently engaged in a secondary cognitive task in the windy driving condition, attention was diverted from driving and the ability to maintain lane position was degraded.

The impact of eye movements and cognitive workload on lateral position variability in driving.

Objective: The objective of this work was to understand the relationship between eye movements and cognitive workload in maintaining lane position while driving.

Background: Recent findings in driving research have found that, paradoxically, increases in cognitive workload decrease lateral position variability. If people drive where they look and drivers look more centrally with increased cognitive workload, then one could explain the decreases in lateral position variability as a result of changes in lateral eye movements.

Text messaging during simulated driving.

Objective: This research aims to identify the impact of text messaging on simulated driving performance.

Background: In the past decade, a number of on-road, epidemiological, and simulator-based studies reported the negative impact of talking on a cell phone on driving behavior. However, the impact of text messaging on simulated driving performance is still not fully understood.

An investigation of driver distraction near the tipping point of traffic flow stability.

Objective: The purpose of this study was to explore the interrelationship between driver distraction and characteristics of driver behavior associated with reduced highway traffic efficiency.

Background: Research on the three-phase traffic theory and on behavioral driving suggests that a number of characteristics associated with efficient traffic flow may be affected by driver distraction. Previous studies have been limited, however, by the fact that researchers typically do not allow participants to change lanes, nor do they account for the impact of varying traffic states on driving performance.

Effects of simulator practice and real-world experience on cell-phone-related driver distraction.

Objective: Our research examined the effects of practice on cell-phone-related driver distraction.

Background: The driving literature is ambiguous as to whether practice can reduce driver distraction from concurrent cell phone conversation.

Methods: Drivers reporting either high or low real-world cell phone usage were selected to participate in four 90-min simulated driving sessions on successive days.