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• Enhanced Night Visibility, Volume IV: Phase II—Study 2: Visual Performance During Nighttime Driving in Rain

Enhanced Night Visibility, Volume IV: Phase II—Study 2: Visual Performance During Nighttime Driving in Rain

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CHAPTER 4—DISCUSSION AND CONCLUSIONS

As mentioned in the Methods section (chapter 2), the headlamp aiming protocol used for this study resulted in a deviation in the maximum intensity location from where it typically is specified for some headlamp types. Details about this deviation are discussed in ENV Volume XVII, Characterization of Experimental Vision Enhancement Systems. As a result of the headlamp aiming, the presented detection and recognition distances were likely increased for the HLB and HOH configurations and likely decreased for the HHB configuration. It is important to consider the results presented in this study in the context and conditions tested. If different halogen headlamps or aiming methods are used, different results might be obtained.

DETECTION AND RECOGNITION CAPABILITIES

While there were some significant differences in the detection and recognition distances among different VESs during nighttime driving in rain conditions, these differences would result in minimal improvements to driver reaction times for the objects tested. On average, objects were detected at distances of 67.4 m (221 ft) or closer. The HLB system, which was used as a baseline due to its widespread availability, provided an average detection distance of 60.4 m (198 ft). In this particular study, only the five UV–A + HLB system outperformed the HLB system, and only by 6.7 m (22 ft), representing an 11 percent difference. Faring the worst were the IR–TIS (in a reversal of the results from Phase II, Study 1, Clear Weather, ENV Volume III), HID, and HLB–LP, which all underperformed compared to the HLB system by about 6.1 m (20 ft), or 10 percent (table 17). When compared to the clear weather condition, rain (approximately 10.2 cm/h or 4 inches/h) severely decreased visibility for the IR–TIS by 74 percent and decreased visibility evenly for all the other VESs by 64 percent to 68 percent (table 19). Thus, except for the IR–TIS (which was more heavily affected by the rain), the rank order of the VESs by detection distance stayed fairly similar from the clear condition to the rain condition. This result might lead to a hypothesis that the rank order would remain constant under any rainfall rate. While a definitive finding would require testing at varying rainfall rates, there is nothing in the data to suggest that UV–A augmentation would significantly improve detection or recognition distances under lower rain rates. It is also intriguing to note that, while very subtle, the five and three UV–A systems (both HLB and HID) retained slightly greater detection distances than did the base HLB and HID systems (table 19).

These differences in distance can be translated to gains or losses in reaction time (table 18). Reaction time has been used in the past to evaluate time margins for crash avoidance behavior when encountering obstacles in the driving path.⁽¹⁹⁾ As mentioned previously, significant differences between the HLB and other VESs were less than 6.7 m (22 ft), which translates to less than 1 second of additional reaction time, even at relatively low speeds (i.e., 40 km/h (25 mi/h); see table 18).

While these distances and reaction times help indicate the advantages of one system over another, they fail to completely describe any potential safety benefits or concerns based on VES use; however, with a limited number of assumptions, the VES-specific detection distances under rain conditions 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.⁽²⁰⁾ For consistency, time-to-collision is presented as distance-to-collision, or stopping distance, 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.⁽²¹⁾ For a vehicle that uniformly decelerates to a stop, the braking distance (d_BD) 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 39 provides the calculation of the braking distance (d_BD) under these conditions:

Figure 39. Equation. Braking distance.

The total stopping distance (d) is the sum of the braking distance (d_BD) 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 22, 23, 24, and 25.) 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 40:

Figure 40. Equation. Total stopping distance for brake reaction time plus braking distance.

The equation in figure 40 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.⁽²²⁾ The equation in figure 40 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).

To calculate total stopping distance, this study used AASHTO’s suggested deceleration rate (a) of 11.2 ft/s² (3.4 m/s²), resulting in a friction coefficient for wet pavement of 0.35 as seen in the equation in figure 41.⁽²²⁾

Figure 41. Equation. AASHTO calculation of coefficient of friction for wet pavement.

Stopping distances in rain conditions increase over dry-pavement distances because of the reduced coefficient of friction between the tires and the pavement. Using the equations and variables, stopping distances were calculated (table 20).

The previous calculations represent a simple condition, but they allow for some visualization of VES capabilities. Based on these calculations, the average detection distances for each VES tested in the rain condition (i.e., rate of 10.2 cm/h (4 inches/h), windshield wiper on highest speed) are not long enough to provide adequate stopping distances for vehicle speeds at anything close to or greater than
56.3 km/h (35 mi/h); however, some caveats apply. First, these distances were obtained while drivers were moving at approximately 16.1 km/h (10 mi/h), and drivers’ abilities to detect objects will not necessarily remain the same as speed increases. Second, systems that are currently close to the adequate stopping distance or that require a larger stopping distance might quickly become less effective when conditions worsen (e.g., worn tires, downhill condition, heavier rain). Third and most important, when detection distances are analyzed in more detail by examining the significant (p < 0.05) VES by Object interaction, different conclusions can be reached (table 21 through table 32). Several VES and object combinations resulted in detection distances that might compromise stopping distances.

As in the clear weather study (ENV Volume III), detection and recognition distances under the rain condition were strongly affected by the characteristics of the object, but the type of VES modulated this effect. The HID, HLB–LP, and IR–TIS provided the shortest detection distances for low-contrast objects; the HLB supplemented by UV–A allowed drivers to detect the pedestrians and cyclists dressed with white clothing farther away. These observations are even more apparent when described in terms of stopping distances (table 21 through table 32; in these tables, an “X” means the stopping distance might be compromised, and an asterisk means the same thing but in an unlikely scenario).

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 outperform HLB, not all do, and the improvements, while statistically significant, are not practical.

In general, HID systems followed the same trend discussed during the clear weather conditions study (ENV Volume III), in which they were outperformed by the rest of the systems. The same issues that were suggested then may have negatively affected the performance of this technology under the rain condition as well. It is possible that the HID system tested here differed significantly from the HID systems tested in other investigations in terms of cutoff and intensity; the characteristics of these systems vary considerably among manufacturers. While data generated by this investigation (see ENV Volume XVII, Characterization of Experimental Vision Enhancement Systems) agree with Jost’s findings regarding the fact that an HID system provides more luminous flux than regular tungsten headlamps, there appear to be some shortcomings with how that luminous flux is used.⁽²⁶⁾ The large amount of visible light generated by HID systems requires a dramatic cutoff angle to comply with glare standards. Although this provides more foreground luminance, the HID VES provides less illumination as the distance from the vehicle increases when compared to the other VESs (e.g., halogen). This increased foreground luminance actually might have an adverse effect on a driver’s performance by increasing the driver’s light adaptation, thus decreasing the driver’s capability to detect objects in dark environments. An example of this potential safety concern is evident in the comparison of this study’s subjective ratings of certain VESs to their detection and recognition distances. There were no significant differences between the subjective ratings of the HID VESs and the HLB VESs; however, in general, the HID systems received better ratings than the HLB systems even though the HLB systems (especially the five UV–A + HLB) provided longer detection and recognition distances. Thus, the higher level of foreground lighting appears to make drivers believe that the HID systems are better in terms of overall visibility and safety.

Rain negatively affected IR–TIS. While this system provided excellent performance levels under clear weather, it exhibited the shortest detection and recognition distances observed in the current study. System technology is the reason for this performance reversal. Because a temperature differential between the rain and the environment usually exists, rain droplets are visible to the IR system. Thus, rain droplets are displayed on the heads-up display (HUD), effectively washing out the display, like the picture on a television screen receiving considerable signal interference (i.e., “snow”). In the rain, drivers were not able to use the system effectively most of the time, and they were left with the HLB–LP (i.e., headlamps for that vehicle). Indeed, no performance differences are observed between the IR–TIS and the traditional HLB–LP. Following are some of the comments participants made about the system at the end of the study:

“...the (sedan), you know with the night vision, it doesn’t really do anything during the rain, it just looks all fuzzy.” (Participant #37, younger male.)

“This (sedan) with this heads-up display with night vision works good outside the rain. In the rain, it is terrible. When you see a person, it is like a ghost. I had to stop and the guy walked out there about 20 feet in front of me and I could barely see him in the rain. Outside the rain, it does good. I believe it needs some kind of contrasting detail to what’s white turns up black there and what’s black turns up white on this night vision. When rain is coming down it is like snow on an old television set, and you can’t distinguish anybody out there. I could see them with my eyes but not on the heads-up display. I think it needs a contrasting knob or something that when you get in rain or snow, you will have to contrast up or down. In dry weather when I was outside the rain, I was impressed with it.” (Participant #32, middle-aged male.)

“I couldn’t see through it (IR–TIS display) when it was raining. I couldn’t see, I didn’t like that at all, I enjoyed it a lot better the time I drove it and it didn’t have it on (HLB–LP), but in the rain I could not see through that thing; maybe it’s something I could get used to.” (Participant #53, older male.)

UV–A headlamps improved detection and recognition of various objects when five UV–A headlamps were used together with HLB, especially for pedestrians and cyclists with white clothing; however, the improvements suggested by this study are not of the magnitude of the ones reported by Mahach et al. and Nitzburg et al.^(27,28) In addition, this extra 6.7 m (22 ft) (i.e., 10 percent improvement) is statistically significant but not meaningful for implementation. At this point, it is not clear if UV–A’s 10 percent improvement over HLB observed in this study might be exceeded during less severe weather conditions. Perhaps results in less severe rain might mimic the detection and recognition behavior under clear weather.

AGE EFFECTS ON DETECTION AND RECOGNITION DISTANCES

In the rain condition, in contrast to the clear weather condition, age did not significantly affect drivers’ detection and recognition distances. During the rain condition, visibility was severely restricted across all age groups, and overall, no significant difference between age groups was observed in terms of detection and recognition distances. The data must be divided by age group, type of object, and VES (i.e., three-way interaction) before a few significant changes in performance appear (mainly for older drivers). However, as discussed in the results section for the three-way interactions, even those results that are statistically significant are not meaningful. Younger and middle-aged drivers exhibited more consistency in their performance across VESs and objects.

As explained in ENV Volume III, visual acuity and contrast sensitivity decline with age. It is theorized that, because of decreased contrast sensitivity and the low visibility conditions of adverse weather, older drivers were able to see from farther away only those objects that fluoresced because of the UV–A headlamps. The same age-dependent trends of decreased visual acuity and contrast sensitivity mentioned in ENV Volume III are evident for this group of participants. Figure 42 shows participants’ visual acuity, and figure 43 through figure 47 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 42. Bar graph. Participants’ visual acuity divided by age group.

Figure 43. Bar graph. Participants’ contrast sensitivity at 1.5 cpd (cycles per degree)
divided by age group.

Figure 44. Bar graph. Participants’ contrast sensitivity at 3.0 cpd divided
by age group.

Figure 45. Bar graph. Participants’ contrast sensitivity at 6.0 cpd divided
by age group.

Figure 46. Bar graph. Participants’ contrast sensitivity at 12.0 cpd divided
by age group.

Figure 47. Bar graph. Participants’ contrast sensitivity at 18.0 cpd divided
by age group.

OBJECT EFFECT ON DETECTION AND RECOGNITION DISTANCES

Comparisons were made in this study to determine whether VESs that showed an increase in detection and recognition distances for pedestrians and cyclists also showed the same trend for other objects, such as the tire tread and the child’s bicycle. HLB headlamps were used in this comparison as a baseline system (table 33 and table 36). The top three detection and recognition distances for each object are highlighted in table 34, table 35, table 37, and table 38 (1st = green, *; 2nd = blue, **; 3rd = yellow, ***).

For this study, there is a marked trend of HLB and HLB with UV–A consistently providing the driver with the best (farthest away from the object) detection and recognition across all objects. The effect of adding UV–A ranges from a 0.03- to a 14.0-m (0.1- to 46-ft) improvement (less than 1 percent to 18 percent) over HLB for detection distances and up to a 13.4-m (44-ft) improvement (19 percent) for recognition distances. When pedestrian detection and recognition results are compared between HLB and HLB with UV–A, the biggest difference is due to pedestrian clothing color. The UV–A allows drivers to detect and recognize pedestrians dressed in light-colored clothing farther away than HLB alone. Following are some of the comments participants made about the UV–A headlamps at the end of the study:

“One of the trucks I drove, I could see the objects the best, I think it was UV–A; it made the white guy look purple, that was a really good headlight, it was my favorite one; I could see further than anything else.” (Participant #55, younger female.)

“The lights that I liked the best I didn’t know that they had UV on at all until I saw the white pedestrian from far away. I could tell those lights were on and those were the ones that I said were the best.” (Participant #1, younger male.)

As mentioned previously, the rain affected the detection and recognition distances for the different objects with the IR–TIS. The pedestrians and the cyclist, who were detected farther away with the IR–TIS during clear weather than with any other VES, were detected primarily with the HLB–LP headlamps on the IR–TIS vehicle during rain. The HID headlamps consistently had the worst (closest to the object) detection and recognition distances across all objects.

Most of the findings for the rain 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 rain condition:

• UV–A technology does not represent a meaningful improvement over the halogen and HID headlamps used in this research.
• The image presented to the drivers from the IR–TIS is negatively affected by heavy rain.
• Clothing contrast, rather than object motion, appears to be responsible for the differences observed between the different types of pedestrians and nonmotorists.
• Although the halogen supplemented with UV–A allowed pedestrians and cyclists with white clothing to be detected farther away, the drivers’ subjective evaluation indicated that HIDs were more helpful in object detection.
• HLB and HLB supplemented with UV–A were consistently the best in facilitating long detection and recognition distances, although the aiming protocol used for this study likely increased detection and recognition distances for the HLB headlamps.