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Publication Number: FHWA-HRT-04-137
Date: December 2005
Enhanced Night Visibility Series, Volume VI: Phase II—Study 4: Visual Performance During Nighttime Driving in Fog
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CHAPTER 4—DISCUSSION AND CONCLUSIONS
As discussed in ENV Volume III, the literature review suggested that new VES technologies, including HID, configurations supplemented by UV–A headlamps, and IR–TIS, would outperform HLB in the experimental conditions for this study. Although some of these technologies indeed outperformed HLB, even IR–TIS, which had the largest detection distance increase over HLB of 6.7 m (22 ft), was not statistically different. On the other side of the spectrum, the worst-performing VES, HID, fell short of the detection distance for the HLB by about 8.5 m (28 ft). Similar to the other ENV studies where HLB outperformed the HID, the means indicate that HLB provided significantly greater detection and recognition distances than did HID. This result was expected; backscatter impairs the detection and recognition of objects because of decreased dark adaptation and a greater white wall effect, and the HID headlamps had greater backscatter than did the other headlamps in the fog condition. This increase in backscatter for HID was also observed in the baseline estimate from a no-fog condition.
The pedestrian dressed in white crossing the road was selected as the object for this experiment in part because it was the object with the greatest opportunity to perform well with UV–A. UV–A headlamps improved detection and recognition distance of this pedestrian when they were used together with HLB; however, the 6.1 m (20 ft) improvements suggested by this study are not of the magnitude of the ones reported by Mahach et al. and Nitzburg et al., nor are they statistically significant or meaningful for implementation purposes.(19,20)
Two previous studies in the ENV project (ENV Volumes IV and V) showed that visibility in adverse weather is severely restricted for all age groups and that there are no significant differences for detection and recognition distances among them. This effect is partially repeated during the fog condition. All age groups showed shorter detection and recognition distances in fog when compared to the detection and recognition distances obtained in the clear condition study (ENV Volume III); however, fog seems to negatively affect detection and recognition even more for the younger group than for the other two groups. It should be noted that the 8.4 m (27 ft) difference in detection and recognition between younger drivers and older drivers is relatively small. To explain the statistical difference between the two older groups and the younger group, additional data checks were performed. As mentioned previously, the range of fog density was similar for all age groups. In addition, the average travel speed was approximately 14 km/h (9 mi/h) for all age groups, eliminating the possibility of a difference in the time available to detect the pedestrian.
The instances where the pedestrian was not detected were reviewed. It was found that only younger drivers failed to detect the pedestrian crossing the road. Younger drivers missed the pedestrian a total of six times under visibility conditions similar to those of middle-aged and older drivers, who all detected the pedestrian.
Potential outliers (denoted with a circle in figure 16 through figure 18) were also examined. The middle-aged group had two potential outliers, and the older group had one (figure 17 and figure 18). All three cases were reviewed. Speed, fog density, and experimenter notes reflect no abnormalities; therefore, the data points were kept in the original analysis (results reported in this volume). To determine if these three data points caused the significant difference, the full analysis was performed again without them; the same results were obtained. The mean distances for older and middle-aged drivers decreased negligibly by 0.3 m (1 ft) and 0.6 m (2 ft), respectively, not affecting the results previously obtained.
Participants’ vision was also reviewed. The literature suggests that the decline in vision generally begins slowly after 40, followed by an accelerated decline after 60.(5,6,8) The same age-dependent trends of decreased visual acuity and contrast sensitivity mentioned in ENV Volumes III, IV, and V are evident for the participants in this investigation. Figure 19 shows participants’ visual acuity, and figure 20 through figure 24 show participants’ percentage of contrast for the left eye (PCL) and right eye (PCR) for test lines A through E, which represent 1.5, 3.0, 6.0, 12.0, and 18.0 cycles per degree (cpd), respectively.
Figure 20. Bar graph. Participants’ contrast sensitivity at 1.5 cpd (cycles per degree) divided by age group.
None of the measurements obtained for this study justify why younger drivers were not able to detect pedestrians as far away as the other two age groups. One potential hypothesis is that young drivers have less experience driving in fog conditions, so they have more difficulties in detecting hazards. Skill acquisition and risk-taking analysis are two potential factors that might help explain this issue.(21) For younger drivers, the skill to detect hazards when their vision capabilities are impaired might not be fully acquired at the stage at which the participants of this study were recruited (mean age for younger drivers was 21 years old). As mentioned, this is a skill, and therefore, it is not dependent on knowledge or physical capabilities. If the younger drivers have not been exposed to fog repeatedly, they might have a more difficult time acquiring that skill. Fog is a weather condition that is not as common as other weather conditions. To put it in perspective, rain occurs in the United States an average of 29 percent of the time during a year, while fog occurs only 6 percent of the time; however, fog’s fatality rate is higher, indicating that more problems are encountered during this weather condition.(22) As shown in previous research, experience contributes to effective negotiation of different driving scenarios.(23)
In addition, risk perception of different driving scenarios increases with age.(24) Previous research has shown that older drivers with vision deficiencies tend to unconsciously be more alert of their surroundings. It has been suggested that older drivers compensate for impairments not only by adapting their driving behavior, but also by using the compensatory potential still available to them.(25) Therefore, smaller perceivable cues (e.g., just noticeable changes in contrast) might trigger the awareness of a potential roadway hazard quicker in an older driver than in a younger counterpart when visibility is as restricted as it is in fog. Additional research should be conducted to determine if this age finding is due to experience or an experimental artifact.
When compared to the clear weather condition, fog (producing 0.05 to 0.79 lx of backscatter) severely decreased visibility for all VESs (table 17). Overall, the decrease in detection distance in fog is the largest among all the adverse weather conditions tested. The detection distances of all VESs were reduced by more than 70 percent.
The IR–TIS had the longest detection distance in fog. This was different from the rain condition, where it was severely restricted, and the snow condition, where it was not evaluated because snow covered the lens of the IR camera. The rank order of the VESs in fog by detection distance stayed fairly similar to the clear condition and the other adverse weather conditions (table 17). As suggested in the other adverse weather studies, this result might lead to a hypothesis that the rank order would remain constant under any level of adverse weather. While a definitive finding would require testing at varying levels of fog for different objects, there is nothing in the data to suggest that UV–A augmentation would significantly improve detection or recognition distances under different fog densities.
While there were some significant differences in the detection and recognition distances among different VESs during nighttime driving in the fog condition, these differences would result in minimal improvements to driver reaction times for the object tested (pedestrian in white clothing). Table 18 shows the mean detection distances for all the VESs in comparison to the HLB system, which was used as a baseline because of its widespread availability.
These differences in distance can be translated to gains or losses in reaction time (table 19). In previous research, reaction time has been used to evaluate time margins for crash avoidance behavior when encountering obstacles in the driving path.(26) Significant differences between the HLB and other VESs were less than 7.6 m (25 ft), which translates to less than 1 additional second of reaction time, even at relatively low speeds (i.e., 40 km/h or 25 mi/h; see table 19). Overall, use of the IR–TIS resulted in a detection improvement over other systems; however, this difference was not statistically significant for most of them. On average, the HID configuration, provided the lowest detection and recognition distances.
While these distances and reaction times provide an indication of the advantages of one system over another, they fail to describe completely any potential safety benefits or concerns based on VES use; however, with a limited number of assumptions, the VES-specific detection distances in fog can be compared against various speed-dependent stopping distances. Collision avoidance research dealing with different aspects of visibility suggests that time-to-collision is an important parameter in the enhancement of driving safety.(27) For consistency, time-to-collision is presented as “stopping distance” (distance to collision) for direct comparisons to the detection distances from the current study. Stopping distance is the sum of two components: (1) the distance needed for the braking reaction time (BRT) and (2) braking distance (table 20). Braking distance is the distance that a vehicle travels while slowing to a complete stop.(28)
For a vehicle that uniformly decelerates to a stop, the braking distance (dBD) is dependent upon initial velocity (V), gravitational acceleration (g), coefficient of friction (f) between the vehicle tires and the pavement, and the gradient (G) of the road surface, with the gradient measured as a percent of slope. The equation in figure 25 provides the calculation of the braking distance (dBD) under these conditions:
The total stopping distance (d) is the sum of the braking distance (dBD) and the distance traveled during the brake reaction time. The results from driver braking performance studies suggest that the 95th percentile BRT to an unexpected object scenario in open road conditions is about 2.5 s. (See references 29, 30, 31, and 32.) For a vehicle traveling at a uniform velocity, the distance traveled during BRT is the product of the reaction time and the velocity. Assuming a straight, level road with a gradient of zero percent (G = 0), the equation for the total stopping distance is as shown in figure 26:
The equation in figure 26 may be used with either metric or English units, with distance (d) in meters or feet, velocity (V) in m/s or ft/s, and a value for the acceleration due to gravity (g) of 9.8 m/s2 or 32.2 ft/s2.
The American Association of State Highway and Transportation Officials (AASHTO) provides separate equations for stopping distance with metric and English units, in which the acceleration due to gravity (g) and the coefficient of friction (f) are combined into a deceleration rate, and the velocity (V) is in units of km/h or mi/h, respectively.(29) The equation in figure 26 was used in this report because it does not require conversion factors and allows for a more direct comparison of the effect of varying the coefficient of friction (f).
Depending upon the density and type of fog, the driving surface may be dry or wet. To provide the greatest sensitivity in calculation of the differences between the VESs used in this study, the coefficient of friction used for these calculations is based on Lindeburg data for dry surface conditions.(33) The data obtained from Lindeburg is comprehensive in terms of type of surface, tire condition, and speed. A mean value of 0.65 was obtained for the coefficient of friction for dry surfaces (across all dry conditions). To accommodate most types of vehicles’ braking capabilities, a conservative approach was taken for the calculations, and 0.60 was used as the coefficient of friction. Using this approach, stopping distances were calculated as shown in table 20.
The previous calculations represent a simple and ideal condition, but it allows for some visualization of the VESs’ capabilities. Based on these calculations, the average detection distances for all VESs, with the exception of HID and HLB–LP, provide enough time for the driver to react and brake at speeds of up to approximately 40 km/h (25 mi/h) for a pedestrian crossing the street dressed in white clothing (table 21; in this table, an “X” means the stopping distance might be compromised). Above 40 km/h (25 mi/h), the stopping distances might be compromised for all the evaluated VESs; however, some caveats apply. First, these distances were obtained while drivers were moving at approximately 16 km/h (10 mi/h), and their ability to detect objects will not necessarily remain the same as speed increases. Second, VESs that are currently close to the stopping distance or that need a longer stopping distance might quickly become less effective when conditions worsen (e.g., wet pavement, worn tires, down hill condition, different object than the one tested).
Most of the findings for the fog condition are consistent with the findings obtained for the clear condition (ENV Volume III). The following conclusions can be made regarding the VESs tested during the fog condition: