Figure 1 provides a simple schematic model to conceptualize the potential learning from recent research in the hazard perception field. The base model is predicated on previous research that presents a “crash sequence” of cognitive processing and action that occurs between hazard detection and crash avoidance.23,24
This base model is exercised to depict difference in pre‐crash and crash sequences for three different driver types, assuming similar traffic conditions and circumstances. The left to right direction represents elapsed time (in seconds) and the X represents a potential impact. The “driving context and milieu” is included to recognize the multitude of related factors (as particularly discussed by Shope7
) that influence the differing crash sequences (for example, the multilevel circumstances that allow an individual to drive while impaired).
Figure 1Schematic crash sequence model. Reprinted with the approval of the Center for Injury Research and Prevention.
The first diagram depicts a scenario typical of (unimpaired) experienced drivers. Due to attentive visual searching or “scanning” of the traffic environment, the driver detects a potential upcoming hazard—an event or situation, for example—and then a chain of decision making processes occurs. The driver must determine or recognize both that the event or situation is indeed a hazard, and that a reaction is required in order to avoid a collision due to that hazard. Next a response must be selected and enacted such that the crash can be avoided or the severity of the crash minimized. This decision making process and response takes the driver two seconds to complete (in accordance with typical hazard response times established in research24
). The result is that the driver is able to avoid a potential crash, or reduce the severity of an unavoidable crash (therefore, the potential impact, X, is depicted with a broken line).
The second diagram depicts an alternative scenario, which can be conceived of as the same experienced driver in the same traffic environment and circumstances, however, the driver is now distracted. The distraction may be, for example, talking on a cell phone or simply looking at something or someone inside the vehicle and not at the traffic environment. Therefore the driver detects the potential hazard a fraction of a second later than in the first diagram. The driver still requires two seconds to undergo the decision making process through to enacting a response, however, the distraction has delayed detection by half a second and, therefore, only 1.5 seconds are “available”. As a result there is a crash that could otherwise have been avoided or have been less severe if the driver had not been distracted and, therefore, perceived the hazard earlier.
The third diagram depicts the potential scenario (in the same traffic environment and circumstances) of a new, inexperienced (unimpaired) young driver. The driver is scanning but not effectively because of their inexperience and, therefore, detects the potential hazard at one quarter of a second delay to the experienced driver in the first diagram. As the driver is inexperienced, each subsequent phase of decision making and response requires a fraction of a second longer than the experienced driver, and therefore the 1.75 seconds “available” are insufficient to avoid or reduce the severity of the crash, and a major crash results.
The contention that all of the pre‐crash sequence phases take fractions longer for the inexperienced driver has long been suggested.23
suggests, however, that this might not be the case in all scenarios, such that an alternative depiction of the inexperienced driver could more closely resemble the second diagram of the distracted experienced driver scenario. To take this a step further, the combination of ineffective scanning with distraction or speeding, for example, could be viewed as effecting even longer delays to detection, such as one half of a second, such that a very severe crash could not be avoided despite equally quick processing and response as an experienced driver. The third diagram may more accurately represent the impaired inexperienced driver, for example, due to fatigue, alcohol, or other drug use, and lengthen each phase to the extent that a fatal crash results.
Innovative research such as this offers exciting new targets for program development and evaluation. These preliminary simulator findings and a preliminary on‐road validation study21
suggest that programs that can improve hazard perception skills and other measures to minimize the time to detection, such as by addressing distractions and time with eyes off road, can have a meaningful impact on reducing the severity of crashes, if not achieving crash avoidance. It may be that advances in vehicles to reduce speeds and vehicle following distances (increasing the driver's field of view) will not only reduce impact speeds, but also reduce crash occurrences due to earlier detection of hazards. Further research is indeed needed, but the targets for intervention and evaluation are highlighted and the technology exists today or in the near future.
Understanding that only fractions of seconds make all the difference between a near crash, minor crash, or severe crash may demonstrate to young drivers why behaviors such as dialing a cell phone, reaching for a CD case on the floor, or turning around to face a rear seat passenger while driving are risky activities. It is possible that behaviors such as these are just as common among older, more experienced drivers yet do not similarly impact their crash risk due to better hazard perception skills, including more time with “eyes on road”. For example, an early study found that 29% of novices made glances at an in‐vehicle distraction that were lengthier than the maximum glance duration of experienced drivers.25
Current and future developments and evaluations offer much promise in elucidating such causal pathways of young driver risk.