Search Results

The Dimensional Model of Driver Demand is extended to include Auditory-Vocal (i.e., pure “voice” tasks), and Mixed-Mode tasks (i.e., a combination of Auditory-Vocal mode with visual-only, or with Visual-Manual modes). The extended model was validated with data from 24 participants using the 2014 Toyota Corolla infotainment system in a video-based surrogate driving venue. Twenty-two driver performance metrics were collected, including total eyes-off-road time (TEORT), mean single glance duration (MSGD), and proportion of long single glances (LGP). Other key metrics included response time (RT) and miss rate to a Tactile Detection Response Task (TDRT). The 22 metrics were simplified using Principal Component Analysis to two dimensions. The major dimension, explaining 60% of total variance, we interpret as the attentional effects of cognitive demand. The minor dimension, explaining 20% of total variance, we interpret as physical demand.

A principal component analysis of the test track data from the Crash Avoidance Metrics Partnership Driver Workload Metrics project provided evidence for two major components in distraction during driving. The first was related primarily to driver workload, while the second was related to event detection and response. This result confirms previous test track findings. A new finding was that “mean single glance duration” (the average dwell time of the eye on the display or control needed to perform the task), loaded on the second dimension (associated with event detection and response), rather than the first (associated with driver workload). Hence, the duration of single glances to a secondary task is more important for event detection and response when driving than total eyes-off-road time or number of glances. These findings fit with the role of a single off-road glance immediately before a crash being predictive of crash probability.

Up to now, there is no standard methodology that addresses how driver distraction is affected by perceptual demand and working memory demand - aside from visual allocation. In 2009, the Peripheral Detection Task (PDT) became a NHTSA recommended measure for driver distraction [1]. Then the PDT task was renamed as the Detection Response Task (DRT) because the International Standards Organization (ISO) has identified this task as a potential method for assessing selective attention in detection of visual, auditory, tactile and haptic events while driving. The DRT is also under consideration for adoption as an ISO standard surrogate test for driver performance for new telematics designs. The Wayne State University (WSU) driver imaging group [2, 3] improved the PDT and created the Enhanced Peripheral Detection Task I (EPDT-I) [4]. The EPDT-I is composed of a simple visual event detection task and a video of a real-world driving scene.

This study investigated driver glances while engaging in infotainment tasks in a stationary vehicle while surrogate driving: watching a driving video recorded from a driver’s viewpoint and projected on a large screen, performing a lane-tracking task, and performing the Tactile Detection Response Task (TDRT) to measure attentional effects of secondary tasks on event detection and response. Twenty-four participants were seated in a 2014 Toyota Corolla production vehicle with the navigation system option. They performed the lane-tracking task using the vehicle’s steering wheel, fitted with a laser pointer to indicate wheel movement on the driving video. Participants simultaneously performed the TDRT and a variety of infotainment tasks, including Manual and Mixed-Mode versions of Destination Entry and Cancel, Contact Dialing, Radio Tuning, Radio Preset selection, and other Manual tasks. Participants also completed the 0-and 1-Back pure auditory-vocal tasks.

This proof-of-concept demonstrates a new method to predict the relative crash risk in naturalistic driving that is caused (or prevented) by the effects on attention of visual-manual secondary tasks performed while driving in a track experiment. The method required five steps. (1) Estimate valid relative crash/near-crash risks of visual-manual secondary tasks measured during naturalistic driving. These data were taken from a prior SAE publication of unbiased estimates of the relative crash/near-crash risks of secondary tasks in the 100-Car naturalistic driving study. (2) Calculate the “physical demand” and “cognitive demand” scores for visual-manual secondary tasks performed while driving on a track.

In this age of the Internet of Things, people expect in-vehicle interfaces to work just like a smartphone. Our understanding of the reality of in-vehicle interfaces is quite contrary to that. We review the fundamental principles and metrics for automotive visual-manual driver distraction guidelines. We note the rise in portable device usage in vehicles, and debunk the myth of increased crash risk when conversing on a wireless device. We advocate that portable electronic device makers such as Apple and Google should adopt driver distraction guidelines for application developers (whether for tethered or untethered device use in the vehicle). We present two design implications relevant to safe driving. First, the Rule of Platform Appropriateness: design with basic principles of ergonomics, and with driver's limited visual, manual and cognitive capacity, in mind. Second, the Rule of Simplicity: thoughtful reduction in the complexity of in-vehicle interfaces.

Dingus and colleagues recently estimated the crash odds ratios (ORs) for secondary tasks in the Strategic Highway Research Program Phase 2 (SHRP 2) naturalistic driving study. Their OR estimate for hand-held cell phone conversation (Talk) was 2.2, with a 95% confidence interval (CI) from 1.6 to 3.1. This Talk OR estimate is above 1, contrary to previous estimates below 1. A replication discovered two upward biases in their analysis methods. First, for video clips with exposure to a particular secondary task, Dingus and colleagues selected clips not only with exposure to that task, but often with concurrent exposure to other secondary tasks. However, for video clips without exposure to that task, Dingus and colleagues selected video clips without other secondary tasks. Hence, the OR estimate was elevated simply because of an imbalanced selection of video clips, not because of risk from a particular secondary task.

This study revises the odds ratios (ORs) of secondary tasks estimated by Virginia Tech Transportation Institute (VTTI), who conducted the 100-Car naturalistic driving study. An independent and objective re-counting and re-analysis of all secondary tasks observed in the 100-Car databases removed misclassification errors and epidemiological biases. The corrected estimates of secondary task crude OR and Population Attributable Risk Percent (PAR%) for crashes and near-crashes vs. a random baseline were substantially lower for almost every secondary task, compared to the VTTI estimates previously reported. These corrected estimates were then adjusted for confounding from demographics, time of day, weekday-weekend, and closeness to junction by employing secondary task counts from a matched baseline from a later VTTI 100-Car analysis. This matched baseline caused most OR estimates to decline even further.

As advanced electronic technology continues to be integrated into in-vehicle and portable devices, it is important to understand how drivers handle multitasking in order to maintain safe driving while reducing driver distraction. NHTSA has made driver distraction mitigation a major initiative. Currently, several types of Detection Response Tasks (DRTs) for assessing selective attention by detecting and responding to visual or tactile events while driving have been under development by an ISO WG8 DRT group. Among these DRTs, the tactile version (TDRT) is considered as a sensitive surrogate measure for driver attention without visual-manual interference in driving, according to the ISO DRT Draft Standard. In our previous study of cognitive demand, our results showed that the TDRT is the only surrogate DRT task with an acute sensitivity to a cognitive demand increase in an auditory-vocal task (i.e., n-Back verbal working memory task).

There is little agreement in the field of driving safety as to how to define cognitive distraction, much less how to measure it. Without a definition and metric, it is impossible to make scientific and engineering progress on determining the extent to which cognitive distraction causes crashes, and ways to mitigate it if it does. We show here that different studies are inconsistent in their definitions of cognitive distraction. For example, some definitions do not include cellular conversation, while others do. Some definitions confound cognitive distraction with visual distraction, or cognitive distraction with cognitive workload. Other studies define cognitive distraction in terms of a state of the driver, and others in terms of tasks that may distract the driver. It is little wonder that some studies find that cognitive distraction is a negligible factor in causing crashes, while others assert that cognitive distraction causes more crashes than drunk driving.

Many metrics have been used in an attempt to predict the effects of secondary tasks on driving behavior. Such metrics often give rise to seemingly paradoxical results, with one metric suggesting increased demand and another metric suggesting decreased demand for the same task. For example, for some tasks, drivers maintain their lane well yet detect events relatively poorly. For other tasks, drivers maintain their lane relatively poorly yet detect events relatively well. These seeming paradoxes are not time-accuracy trade-offs or experimental artifacts, because for other tasks, drivers do both well. The paradoxes are resolved if driver demand is modeled in two orthogonal dimensions rather than a single “driver workload” dimension. Principal components analysis (PCA) was applied to the published data from four simulator, track, and open road studies of visual-manual secondary task effects on driving.