Strayer et al.’s conclusion that their “cognitive distraction scale” for auditory-vocal tasks indicates “significant impairments to driving” is not supported by their data. Additional analysis demonstrates that slower brake reaction times during auditory-vocal tasks were fully compensated for by longer following distances to the lead car. Naturalistic driving data demonstrate that cellular conversation decreases crash risk, the opposite of the article’s assumption. Hence, the scale’s internal and external validities for indicating driving impairment are highly questionable.

Dingus and colleagues (Proc. Nat. Acad. Sci. U.S.A. 2016, 113, 2636–2641) reported a crash odds ratio (OR) estimate of 2.2 with a 95% confidence interval (CI) from 1.6 to 3.1 for hand-held cell phone conversation (hereafter, “Talk”) in the SHRP 2 naturalistic driving database. This estimate is substantially higher than the effect sizes near one in prior real-world and naturalistic driving studies of conversation on wireless cellular devices (whether hand-held, hands-free portable, or hands-free integrated). Two upward biases were discovered in the Dingus study. First, it selected many Talk-exposed drivers who simultaneously performed additional secondary tasks besides Talk but selected Talk-unexposed drivers with no secondary tasks. This “selection bias” was removed by: (1) filtering out records with additional tasks from the Talk-exposed group; or (2) adding records with other tasks to the Talk-unexposed group. Second, it included records with driver behavior errors, a confounding bias that was also removed by filtering out such records. After removing both biases, the Talk OR point estimates declined to below 1, now consistent with prior studies. Pooling the adjusted SHRP 2 Talk OR estimates with prior study effect size estimates to improve precision, the population effect size for wireless cellular conversation while driving is estimated as 0.72 (CI 0.60–0.88).

NHTSA issued a 12-s total eyes-off-road time (TEORT) glance criterion for its visual-manual guidelines simulator test. This criterion relied on NHTSA’s analysis of manual radio-tuning data from its simulator study using a 2010 Toyota Prius premium radio and its test-track study of five 2005 to 2010 vehicles using various radio-tuning control types. However, this 12-s criterion was biased falsely downward. First, NHTSA oversampled the youngest participants (with the shortest TEORT scores) in the simulator study and oversampled the newest radios (with the shortest TEORT scores) in the test track study. Second, NHTSA incorrectly assumed that track and simulator metrics had a one-to-one correspondence, but the track TEORT was substantially shorter than the simulator TEORT for a matched Prius task with matched older participant ages. Third, NHTSA set its criterion at exactly the 85th percentile of the TEORT data rather than its upper confidence limit; hence, traditional radio-tuning tasks would fail repeated tests simply because of random test-to-test variation. The current study, after adjusting for these and other biases, estimated a criterion of 21.4 s from the identical NHTSA data sets. Emerging knowledge that relies on naturalistic driving studies suggests that cumulative glance metrics such as TEORT have low predictive validity for crashes compared with other metrics. A new test should be developed with metrics of higher predictive validity for relative crash risk on the basis of a growing scientific consensus about the relationship—both positive and negative—between eye glances, relative crash risk, and secondary task performance while driving.

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. That UL was estimated using the mid-P exact method for calculating confidence limits for a dual Poisson process, using a one-tailed 95% confidence level. The required AV mileage was adjusted by trial and error until the UL was just below 1. The method was applied to the real-world fatal crash and mileage data from California (Waymo and GM Cruise), U.S. (Uber and Tesla), China (Tesla), and Sweden (Volvo) to calculate the AV miles required to validate a “Vision Zero” AV fatal crash objective with 95% confidence. For AV fleets in California, U.S., and China, the AV miles required are only in the millions. In Sweden, 1.9 billion AV miles are required because its CV fatal crash rates are already so low. In conclusion, epidemiologic analysis of real-world data finds that only millions of real-world AV data are typically required to validate AV safety with 95% confidence to a “Vision Zero” fatal crash criterion.