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Will Automated Vehicles be Safer than Conventional Vehicles? One of the critically important questions that has emerged about advanced technologies in transportation is how to test the actual effects of these advanced systems on safety, particularly how to evaluate the safety of highly automated driving systems. Richard Young's Critical Analysis of Prototype Autonomous Vehicle Crash Rates does a deep dive into these questions by reviewing and then critically analyzing the first six scientific studies of AV crash rates.

Engineering reliability models from RAND, MobilEye, and Volvo concluded that billions of miles of on-road data were required to validate that the real-world fatality rate of an “Automated Driving System-equipped vehicle” (AV) fleet for an improvement over human-driven conventional vehicles (CV). RAND said 5 billion miles for 20%, MobileEye 30 billion for 99.9%, and Volvo 5 billion for 50% improvement. All these models used the Gaussian distribution, which is inaccurate for low crash numbers. The current study proposes a new epidemiologic method and criterion to validate real-world AV data with 95% confidence for zero to ten fatal crashes. The upper confidence limit (UL) of the AV fatal crash rate has to be lower than the CV fatal crash rate with 95% confidence. That criterion is met if the UL of the AV fatal crash incidence rate ratio estimate is below one.

Four major epidemiological study designs are reviewed: cohort, case-control, case-cohort, and case-crossover. In the medical field, these study designs and their analysis methods are commonly used to estimate the effect of exposure to a disease on an outcome (such as death). The formal epidemiological definition of each design in the medical field is here translated into the context of real-world and naturalistic driving safety studies. For example, instead of an outcome of death, the outcome becomes a crash or other safety-relevant event. Instead of exposure to a disease, the exposure becomes a driver activity such as a secondary task (e.g., talking on a cell phone), a driver impairment (e.g., drunk or drugged), or a driver behavior error (e.g., speeding). The effect size measures of the exposure on the outcome include the rate ratio, risk ratio, and odds ratio.

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.

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.

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.

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.

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.

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.

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).

This study reanalyzes the data from a recent experimental report from the University of Utah investigating the effect on driving performance of auditory-vocal secondary tasks (such as cell phone and passenger conversations, speech-to-text, and a complex artificial cognitive task). The current objective is to estimate the relative risk of crashes associated with such auditory-vocal tasks. Contrary to the Utah study's assumption of an increase in crash risk from the attentional effects of cognitive load, a deeper analysis of the Utah data shows that driver self-regulation provides an effective countermeasure that offsets possible increases in crash risk. For example, drivers self-regulated their following distances to compensate for the slight increases in brake response time while performing auditory-vocal tasks. This new finding is supported by naturalistic driving data showing that cell phone conversation does not increase crash risk above that of normal baseline driving.

A key aim of research into cell phone tasks is to obtain an unbiased estimate of their relative risk (RR) for crashes. This paper re-examines five RR estimates of cell phone conversation in automobiles. The Toronto and Australian studies estimated an RR near 4, but used subjective estimates of driving and crash times. The OnStar, 100-Car, and a recent naturalistic study used objective measures of driving and crash times and estimated an RR near 1, not 4 - a major discrepancy. Analysis of data from GPS trip studies shows that people were in the car only 20% of the time on any given prior day at the same clock time they were in the car on a later day. Hence, the Toronto estimate of driving time during control windows must be reduced from 10 to 2 min.

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.

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.

This paper discusses radio usage habits observed during analysis of 700 hours of video sampled from the 100-Car Naturalistic Driving Study database. Analysts used large-scale printouts of each vehicle's radio faceplate and recorded interactions based on video analysis of hand movement and location (without the assistance of audio recordings). The duration and specific manipulations or adjustments were recorded for each interaction. The results summarize the length and type of interactions, most often-used controls, and total percentage of time drivers interacted with the radio.

How do in-vehicle telematics devices influence mind-on-the-drive? We determined the spatio-temporal properties of the brain mechanisms during a simple visual event detection and motor response in a validated driving-like protocol. We used the safe and non-invasive brain imaging methods of functional magnetic resonance imaging (fMRI) and Magnetoencephalography (MEG) to locate the essential brain activated structures and their corresponding temporal dynamics. This study sets the foundation for determining the fundamental brain mechanisms by which secondary tasks (such as cell phone use) may affect the responses to visual events in a laboratory setting. Improved knowledge of the brain mechanisms underlying selective attention in such driving-like situations may give rise to methods for improving mind-on-the-drive.

The Alliance of Automotive Manufacturers (Alliance) has produced a document in which Principle 1.4 gives criteria and methods for calculating downvision angles to navigation and telematics displays in vehicles. This paper describes the details of the criteria and methods for determining compliance. Visual displays placed high in the vehicle instrument panel help drivers to use their peripheral vision to monitor the roadway for major developments, even during brief glances to the display. The Alliance has developed two criteria to define the maximum allowable downward viewing angle for displayed information in North American vehicles. One criterion is for use in two-dimensional Computer Aided Design (CAD) analyses, and one is for use in three-dimensional CAD analyses. Alliance Principle 1.4 is consistent with known driver performance research data, and known facts about the peripheral sensitivity of the human visual system.