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Historically Attempts To Directly Relate Measures Of Functional Capability To Motor Vehicle Crashes

Associations that are modest at best, typically accounting for well under ten percent of the variability in the criterion measure (crash rates). This includes both sensory (visual) performance measures such as acuity and contrast sensitivity, plus measures of perceptual and cognitive skills including immediate memory span, complex reaction time, discrimination of embedded figures, and an array of additional functional capabilities, using various testing techniques.

The reasons for this failure are many, as reported by Peck (1993) and others. Most importantly: since crashes are rare, most drivers remain crash-free for many years, thus restricting the range for this variable in any analysis; and, crashes are not a direct and inevitable result of unsafe driving behaviors, but are the consequences of interactions between a driver's behavior, situational factors, and the actions of other motorists.

The most successful of the efforts simplistically modeled in Figure 1 has examined the relationship between involvement in selected intersection crash types and measures of attentional and pre-attentional behavior, most notably research addressing the functional or "useful" field of view (UFOV). This body of work has predominantly considered crashes retrospectively, however, and with samples who have been selected specifically on the basis of prior crash involvement. Under these methodological constraints, the crash variance accounted for has been reported to exceed 25 percent (cf. Ball, Owsley, Sloane, Roenker, and Bruni, 1993). In contrast, another related study by the California Department of Motor Vehicles (CA DMV) using 3,669 randomly-selected license renewal applicants showed correlations between UFOV measures and crashes for drivers age 70 and older that were statistically significant, after adjusting for gender, age, and driving exposure, but the percentage of crash variance accounted for fell to just over 4 percent (Hennessy, 1995).
The importance of this work, regardless of specific outcomes, is that few now accept sensory (visual) ability alone as necessary and sufficient for safe driving. Instead, a broader focus incorporating attentional stages of information processing has gained acceptance among researchers and practitioners alike, and appears to hold promise for both screening and diagnostic tests to identify high-risk drivers.

A further evolution of thinking in this area of research has been to broaden criterion measures to focus upon driving competency, apart from the outright occurrence of a crash. This construct-valid approach offers several distinct advantages. First, measures of competency may be developed which are directly observable. Second, the instances of incompetency, manifested as driving errors in a particular performance context with describable physical attributes, level of task demand, degree of familiarity/expectancy for the vehicle operator, etc., occur with a much higher frequency than crashes do. Gebers (1990), in applying a theoretical (Newbold-Cobb) model to 3-year crash rates for the California driving population, calculated that the maximum correlation that could be obtained between an infallible test battery or predictor variable and crash rates was 0.33; this reflects the restriction of range and variability in crash occurrence that were noted above. Using directly observable measures of driver performance deemed to be acceptable surrogates for crash risk (i.e., significantly correlated with crashes), this limiting factor in testing hypothesized consequences on safety of drivers' diminished functional capability is removed.

The next logical step is to determine how (age-related) diminished functional capabilities may predict driving errors, particularly critical errors that a strong consensus among traffic safety experts would characterize as direct antecedents of crashes. A recent study which has followed this paradigm has been reported by Janke and Hersch (1997). As modeled in Figure 2, clear associations between one or more measures of functional ability and driving competency could provide the strongest argument to date that this approach to prediction of crash risk will ultimately be fruitful. At the same time, the identified functional measures would assume priority as candidates for subsequent research studies and pilot programs by licensing agencies.

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	Did You Know?
	Motor Vehicle Crashes Are The Leading Cause Of Death From Ages 3 Through 33 Years Old For people in the age range of 3 to 33 years, the leading cause of death is being involved in a car accident. People, who are between 15 to 24 years, and those who are above 75 years of age, are who are the most severely affected by car accidents.