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Federal Highway Administration Research and Technology
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Publication Number: FHWA-HRT-04-133
Date: December 2005
Enhanced Night Visibility, Volume II: Overview of Phase I and Development of Phase II
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CHAPTER 7—LITERATURE REVIEW
Before beginning the studies in Phase II, the research team conducted a literature review on nighttime driving with an emphasis on vision, age, nighttime driving, object detection and recognition, different types of vision enhancement systems, and driving in adverse weather conditions.
Vision begins with light, but before this light is seen, it must be transformed into electrical energy by the receptors of the eye. This electrical energy must travel a long and complex path to the visual cortex and beyond.(8) Visible light, the band of electromagnetic energy with a wavelength between 380 and 720 nm, is the stimulus for vision. Light can be perceived by looking directly at the source that emits these wavelengths, such as the sun or a light bulb; however, most perceived light is reflected into the eyes from objects in the environment. That reflection provides information about the nature of these objects.
As light enters the eye, the process of vision begins. The first step is focusing light onto the receptors of the retina, done by the cornea and lens. Electrical signals generated in the receptors pass through the retina in the first neural network on the way to the brain. Rods and cones are the two basic receptor types. In essence, these receptors create a mosaic on the retina. Rods (responsible for scotopic vision) and cones (responsible for photopic vision) differ in a number of areas (table 5).
Only all-cone foveal vision enables the detection of small details, which explains why nighttime drivers might not be able to identify details in a scene. Scotopic vision tends to allow object detection until the object is illuminated by the vehicle’s headlamps and the identification of more details is possible through photopic vision.
Testing of visual acuity, which is typically measured using high-contrast stimuli, indicates the visual system's capacity to resolve fine detail in optimum conditions. Contrast sensitivity testing measures detection abilities in different contrast levels and corresponds to how well the person can perform common, everyday visual tasks such as detecting and identifying a road sign at dusk. Many people have good acuity, even 20/20, but they still have problems seeing in conditions of decreased contrast, such as in rain or at night. By analogy, the Snellen acuity test evaluates vision quantity, whereas the contrast sensitivity test evaluates vision quality. When the contrast between an object and its background is low (i.e., low quality), the object must be larger (i.e., increased quantity) for it to be discriminated equivalently as a smaller object with greater contrast (i.e., higher quality).
Contrast has many definitions including modulation contrast, luminous contrast, and luminance ratio. The formulas for these three definitions are shown in the equations in (figure 4 through figure 6).
Lmax = maximum luminance
Lmin = minimum luminance
Various characteristics of the environment and the individual are known to affect acuity and contrast sensitivity. Increased levels of light or background luminance increase acuity and contrast sensitivity. Higher levels of light activate the cones, resulting in higher acuity and sensitivity.(12) Visual acuity and contrast sensitivity decline with age. The movement of an object or the observer (or both) decreases visual acuity. The ability to visually discriminate in these circumstances (e.g., looking at objects on the side of the road while driving) is called "dynamic visual acuity." Burg states that acuity deteriorates with increased relative motion.(13) Scialfa et al. suggest that static and dynamic visual acuity may be moderately correlated.(14) Therefore, testing a driver’s static acuity might provide an indication of dynamic acuity.
By the year 2020, it is anticipated that 17 percent of the United States population will be 65 or older, resulting in more than 50 million eligible older drivers.(15) These drivers may be more likely to suffer a crash. Many studies have examined traffic crashes involving drivers aged 65 and older, researching these drivers’ physical, mental, and psychomotor skills as a potential cause of crashes. (See references 16, 17, 18, 19, and 20.) Accident analyses have shown that crash patterns and older drivers’ accident maneuvers are similar in many motorized countries, regardless of the different traffic environments and rules.(17,21,22) Deficiencies in physical and mental functions of older drivers are said to play a major role in crash occurrence; however, an exact cause-and-effect relationship between crash occurrence and the possible deficiencies of older drivers has not been determined.
When calculating crash rates based on the number of licensed drivers, crash rates for older drivers are lower than for younger drivers.(23) If crash rates are computed with an estimated distance traveled, overall crash rates are higher for drivers age 65 and older, with a steep rise at age 75. (See references 20, 24, 25, and 26.)
As mentioned previously, visual acuity and contrast sensitivity decline with age. The decline generally begins slowly after the age of 40 followed by an accelerated decline after age 60.(27,28,29) Lens opacity increases and the pupil diameter decreases with age. The maximum area of the iris in eyes of people aged 60 is about half that of those aged 20. These factors allow less light to reach the retina in older persons.(30) Weale determined that there is a 50 percent reduction in retinal illumination at age 50 compared to age 20, and this reduction increases to 66 percent at age 60.(31) Hills and Burg indicated no significant correlation between vision measures and crash data for participants under the age of 54, but for those 54 and older, acuity showed significant correlations with crash data.(32)
Depending on the complexity of the required tasks, the ability to see is degraded when illumination is reduced below certain levels. Nighttime driving is one such task in which the ability to see is often inadequate and frequently exposes road users to high levels of risk.
The traffic volume at night is much lower than during daytime (20 percent and 80 percent, respectively); however, the fatality rate for nighttime driving in the United States is about two to four times the daytime rate when the factor of VMT is considered.(33) In an interesting analysis of traffic crashes, Vanstrum and Landen showed that, after removing the effects of alcohol in drivers, there were 1.21 and 2.79 fatalities per hundred million miles of exposure in the day and night, respectively.(34) Thus, the risk of a fatality at night among drivers not impaired by alcohol is about 2.3 times higher than in daytime.
It is difficult to properly account for all of the variables that can affect traffic fatalities, but clearly the most dramatic difference between day and night driving is the large reduction in visibility because of the decreased levels of illumination, particularly when drivers are dependent solely on headlamps, and the increased glare from other illumination sources including other vehicles.
Past analyses point to the continued need to improve the visibility of salient objects at night by using fixed lighting, better and consistent roadway delineations, reflective traffic signs, and increased reflectivity of pedestrian and cyclist clothing. Such improvements are necessary in clear atmospheric conditions, but they are essential in adverse weather conditions; fatality rates for adverse weather conditions during nighttime driving are more than 25 percent greater than for daytime driving.(35) While vehicle headlamps can improve, it will be difficult to make significant advances in their effectiveness unless radically new concepts are developed. Furthermore, many current automobile designs reduce the visibility of drivers because of lower eye heights, lower headlamp mounting, and more raked windshields. The introduction of sport utility vehicles represents an alternative for higher eye height and headlamp mounting, but these vehicles tend to create a glare problem for drivers of oncoming, lower-profile vehicles.
Reduced visibility poses a serious problem to driving because both longitudinal and lateral control are based on environmental references.(36) Most drivers reduce their speed when confronted with sight restrictions, but these speed reductions are usually insufficient when considering sight distance.(37,38) Reduced visibility may also disturb drivers’ lateral control by forcing them to adjust their lateral position based on a small number of close and rapidly changing visual cues. Thus, in conditions of reduced visibility, lateral control will probably be more erratic without speed reductions. Tenkink demonstrated that the amount of lateral variation in lane position increased with decreasing sight distance when the speed level was held constant, whereas in free-speed conditions both speed and lateral variance decreased.(39) The speed difference between a straight road and a curved road was found to be smaller for short sight distances than for longer ones, and drivers’ lateral stability in curves differed between sight conditions. Both findings indicate that drivers’ lateral control was affected by restricted sight.
The results of Chrysler, Danielson, and Kirby show that when regular low-beam headlamps are used in clear conditions, younger drivers are able to detect “small road hazards” at longer distances than older drivers.(40) The younger group detected a static object (18 cm tall and 33 cm wide) at an average of 89.9 m, and the older group did so at an average of 70.1 m. When a mannequin 107 cm tall (simulating a children or static pedestrian) was presented to the participants, age was also a significant factor. The older group's mean detection distance for the mannequin was 80.8 m and the younger group's mean was 109.7 m. This research effort simulated adverse weather conditions by having participants wear blurred plastic visors that reduced the contrast of the scene. During simulated adverse weather conditions, the same age trend continued, but the detection distance for the static objects had an average reduction of 51 percent when compared to the clear condition. A detection distance reduction of 37 percent occurred for the small pedestrian objects during the adverse weather simulation.
There is general agreement that automobile low-beam headlamps provide, at best, marginal visibility for low-contrast objects such as pedestrians.(41) Furthermore, it is well known that pedestrians tend to overestimate their own nighttime visibility.(42) This combination of these two factors is especially critical for older drivers because of their generally impaired vision.
When doing roadway object visibility research, both detection and recognition distances have often been collected to analyze the degree to which the different vision enhancement system (VES) configurations enhanced nighttime visibility while driving. (See references 43, 44, 45, 46, and 47.)
Timely detection of traffic control devices and hazards on the roadway is an essential part of safe driving. As mentioned previously, most drivers tend to over-drive their low-beam headlamps and operate at very short preview times at night, which could explain the high rates of nighttime crashes.(48) Therefore, alternative systems that enhance night visibility are needed.
If reduced visibility is one of the primary direct causes of increased crash risk at night, are there technologies that can reduce crash risk by providing enhanced visual cues to drivers? Various VESs that are said to accomplish this are currently in development by original equipment manufacturers and tier-one suppliers. Some of the VESs discussed in this section are currently commercially available, while others remain in prototype testing. These systems differ in various aspects, including cost and spectral distribution (figure 7 and figure 8).
Although the development of lighting technology was quite advanced in the 1970s, government restrictions prohibited halogen headlamps in the United States until the early 1980s. Until that time, traditional headlamps were incandescent sealed beams, which gave off a lot of heat and required great power, drawbacks that halogen headlamps reduced. A conventional incandescent bulb generates 16 to 18 lumens (lm) of light per W compared to 20 to 22 or more lm/W for a standard halogen bulb or 28 to 33 lm/W for some high output halogen bulbs. Currently, halogens are the standard headlamps for vehicles in the United States.
Halogen systems provide illumination by routing electricity through a high-resistance tungsten filament surrounded by halogen gas. The glowing tungsten filament produces visible-spectrum light of greater luminance than that of the conventional filament. Luminous flux measures of 200 to 300 lm are typical of halogen bulbs. Combined with sophisticated reflectors and lenses, today’s halogen bulbs can provide a low-beam output of 500 or even up to 1,000 lm.(49) Halogen lighting systems usually cost between $40 and $100 per vehicle.(50)
Unfortunately, about 80 percent of the output of halogen headlamp systems is wasted as heat in the infrared spectrum.(50,51) Furthermore, the filament oxidizes and erodes over time, resulting in light with a yellowish hue. The filament is also susceptible to damage from shock, vibration, or impact, limiting its useful lifespan.
HID systems represent a major breakthrough in headlamp technology. Unlike halogen lamps, HID systems produce light in a gas discharge lamp by ionization rather than by a glowing tungsten filament. The arc tube used in the system is composed of a quartz glass envelope that surrounds two electrodes. Inside the tube are pressurized xenon and mercury gases and metal-halide salts. The system applies a very high voltage between the electrodes that results in an arc, which in turn creates visible light.(50,52) This process usually takes a few minutes to stabilize, although current designs allow the process to be sufficiently complete within seconds. The HID lamp can provide an output varying between 1,000 and 3,000 lm at the bulb.
HID lighting systems are more efficient than standard halogen headlamps, producing about 75 lm/W. They also produce at least 70 percent more light than traditional halogen lamps, require less power, and produce less heat. In contrast to a halogen bulb, an HID bulb produces a brighter, blue-white hue, which improves visibility distance by more than 50 percent compared to traditional headlamps and enhances reflective features of road signs and lane markers.(50,52) Furthermore, the no-filament design allows a system life up to six times longer than the life of a halogen bulb.(53) The system also provides more flexibility in headlamp design and allows breakage-resistant polycarbonate plastic to be used as the cover lens material.
Despite its promising features, HID technology has not been implemented widely for several reasons. The system is relatively expensive—roughly $800 to $2,000 per vehicle—and therefore, it is currently available in only a few luxury car models. NHTSA requires complete replacement of the system if a component fails, increasing service costs.(52) Furthermore, the technology requires a complex sensor system in the vehicle to maintain proper aiming; poorly aimed HID light results in glare for the oncoming driver.(51) In addition, as is often the case with new technology, the increased viewing distance provided by HID headlamps may create a false sense of security when driving at night.
Another major drawback of HID headlamps is that they offer relatively low color rendering capabilities. The perceived color of an object depends on the spectral power distribution of the light source used to illuminate the object and the spectral reflectance of the object. Unlike the continuous spectral power distributions of daylight or halogen lamps, HID lamps have high concentrations of energy at several narrow-band wavelength regions, while at other regions they have little or no energy. Thus, not all spectral frequencies will be reflected back to the driver, affecting the driver’s color perception. The primary concern with this low color rendering has been with the color perception of traffic signs, especially red signs (such as stop signs), because most HID lamps are deficient at the long-wavelength end of the visible spectrum. Sivak and Flannagan present an extensive summary of research that relates to this matter.(54) These authors state that there are two fundamental issues that have not yet been addressed in past HID research: how important is color (in addition to other dimensions such as shape and legend content) in achieving conspicuity and comprehension of traffic signs in general and red signs in particular, and if color is important, how large a decrement in color rendition is acceptable from a safety point of view? Empirical evidence addressing these two issues does not yet exist.
Researchers have investigated various methods of making objects and pedestrians more visible at night, thus increasing the reaction time for drivers. One of these methods uses UV light as an auxiliary technology to more traditional headlamp systems (e.g., halogen or HID). Although the concept of UV headlamps has existed for some time, a new UV lamp technology developed in Sweden has given researchers fresh insights into the possibilities of its use. These prototype UV headlamps are aimed similarly to high-beam headlamps. They are intended for use with fluorescent TCDs. The headlamps emit UV radiation in the spectral range of 320 to 380 nm, which is invisible to the human eye. The short-wavelength light emitted by the UV headlamps reacts with the fluorescent properties of objects that it contacts to produce visible light. These UV headlamps potentially could offer high-beam performance without glare to oncoming drivers; however, because most objects in roadways are not fluorescent, UV headlamps would always be used with low- or high-beam headlamps.
To date, a number of European and United States studies have begun establishing the technical feasibility of the Swedish approach. Field studies show that prototype UV–A headlamps significantly improve visibility for pedestrians and fluorescent TCDs. (See references 47, 55, 56, and 57.) Analysis of the spectrophotometric output of the UV–A headlamps shows them to be safe and suitable.(58)
Mahach et al. and Nitzburg, Seifert, Knoblauch, and Turner suggest in their research that UV–A headlamps could improve visibility distances.(1,57) Nitzburg performed a pedestrian visibility study in a static environment (i.e., the car's transmission was in the "park" position) with the participant in the passenger side of the vehicle. Between detection and recognition trials, the vehicle advanced on the pedestrian in increments of 30.5 m. The study used a windshield shutter to limit the time available for visual search, giving a 2-s stimulus exposure time after each 30.5 m advancement. Results suggested an improvement on visibility distances of more than 200 percent when the detection distances of halogen headlamps supplemented with UV–A headlamps were compared to those of halogen headlamps alone.
Several issues regarding UV headlamps remain unaddressed, and it is essential to identify any unintended adverse effects of UV technology that could block implementation, even if these systems improve object detection distances. First, the appearance of UV headlamps to oncoming drivers might be considered unacceptable. Studies conducted to date have, for the most part, evaluated only the appearance of the roadway and pedestrians as seen by the driver of the UV-equipped vehicle. While it is true that the eye is insensitive to optical radiation below 400 nm, it is not completely precise to say that UV–A light is invisible to the normal eye. The UV–A light causes some fluorescence to occur within the ocular media, particularly the lens, and thus observers—oncoming drivers, potentially—experience the headlamps as shimmering, purplish-blue light. When viewed through a windshield, this fluorescence is lessened because much of the UV–A radiation is absorbed, but pedestrians and motorcyclists do not have this advantage.
As stated earlier, the second UV headlamp issue that needs to be addressed before implementation is the possibility of driver adaptation or overconfidence. UV–A headlamps can increase the visibility distance for fluorescent roadway delineation and other fluorescent objects such as pedestrians who are wearing light-colored or fluorescent clothing. Visibility of dark, nonfluorescent objects does not increase in UV–A illumination. Will drivers adapt to improved roadway visibility by driving faster, and therefore detecting dark roadway hazards or pedestrians at shorter distances? Such an argument could be made by opponents of this technology, and while a counterargument exists in principle (e.g., reliance on retroreflective TCDs involves a similar risk), the issue needs to be evaluated empirically. If driver adaptation or overconfidence does increase the risk of crashes with roadway objects and especially darkly clad pedestrians, these issues need to be considered in a detailed human-factors analysis.
There is also a need to accurately assess the benefits of this technology in adverse weather conditions because this could be an area where UV headlamps offer substantial performance advantages compared to other systems. While the potential for increasing pedestrian safety in these circumstances certainly exists for this VES, the system remains untested in adverse weather conditions.
Infrared thermal imaging systems (IR–TIS) are already in production, and they are optional in several high-end luxury cars as additions to halogen headlamps. This type of system is composed of an infrared (IR) camera and a heads-up display (HUD). IR cameras sense infrared energy to see in the dark. Because IR energy is emitted proportionally to the temperature of an object, the warmer the object, the more energy it emits and the more visible it is. The image obtained from an IR camera provides a thermal signature of the scene. This image can be stored or displayed on a standard video monitor. The IR system presents objects that exhibit temperature differentials when compared to the environment (e.g., pedestrians, cyclists, animals) as an image in different shades of gray. IR systems do not enhance the environment as seen by the driver but instead display an alternative version of the environment that contains additional information not available from the areas illuminated by the traditional headlamp system.
The research of Barham, Oxley, and Ayala on a prototype IR system found that participants using the prototype were able to spot pedestrians 89.9 to 100 m away, providing the driver with 5 s to react if driving at 72.4 km/h or 20.1 m/s (45 mi/h or 66 ft/s).(43) This research was performed statically, and motion likely would decrease these detection distances. Because IR systems display information on a HUD, they also impose a secondary task to the driver: monitoring the images on the HUD while driving. Secondary tasks while driving can be causes of driver distraction, which can lead to driving performance degradation.
An HUD is a logical alternative for presenting the type of information provided by IR-TIS. While the use of IR–TIS HUDs in passenger cars is relatively new, car manufacturers have used HUDs to visually present other information through the windshield to the driver since 1988.(59) An HUD allows the driver to access visually displayed information in closer proximity to forward scene events than does a conventional head-down (HD) instrument panel display. Heads-up information traditionally includes digital speed, high-beam indicator, and master and specific warnings. In most driving conditions, only the speedometer is shown on the HUD, which is translucent and either blue- or yellow-green. In addition, HUD information is redundantly displayed at conventional HD locations, and the driver can dim the HUD or turn it off.
GM production HUDs are positioned at a nominal 4° look-down angle, centerline to the driver, and at front bumper distance (about 2.4 m or 7.9 ft). The Nissan and Toyota production HUDs are positioned at a nominal 7° look-down angle, 8° to 11° from driver centerline, and at image distances ranging from about 0.9 to 2.1 m (3 to 6.9 ft). (60,61) HUD look-down angle settings vary somewhat across drivers, depending on each driver’s eye position and preference. The owner’s manuals of HUD-equipped vehicles advise drivers to adjust the HUD as low as possible in the field of view, with the entire HUD image remaining fully visible (i.e., so the HUD appears just above the vehicle’s hood).
The next generation of HUDs may include information not redundantly displayed at traditional HD locations, provided technological advances can ensure HUD image visibility comparable to HD displays in a range of conditions. These advances involve increasing image source luminance and HUD optical system efficiency. Assuming this technological challenge can be overcome, automotive HUDs have potential to improve the driver-vehicle interface, present information that could not be effectively communicated on an HD display, and increase display space and interface design flexibility. In addition, future HUDs may include more advanced content such as navigation guidance, headway (car-following) aid, intelligent cruise control, forward collision warning, lane maintenance aid, infrared night vision displays, and roadway-to-vehicle communication information.(62) These relatively unexplored areas may yield the greatest potential benefits of HUD technology.
There are numerous concerns regarding the overall benefit of VESs because of potential adverse effects on driving performance and behavior.(63) Increased speeds because of increased driving confidence and comfort, degraded depth perception, degraded object recognition, and missed peripheral targets because of attention tunneling and restricted display field of view are among the problems that may be linked to VES use.
There is currently no consensus regarding net VES benefits because there are certain aspects of performance and behavior that are improved and others that are degraded when using particular VESs. (See references 46, 47, 64, 65, and 66.) Using a prototype VES with a conformal HUD, Barham, Oxley, and Alexander determined that the detection distance to pedestrian and child dummy objects increased by 39 and 18 m (128 and 59 ft), respectively, when compared to existing halogen headlamps.(64) The system provided no benefit to the legibility of road signs. This study is particularly noteworthy because it used a sample of older drivers aged 65 to 80. Replicating the Barham, Oxley, and Alexander findings using a prototype VES in a controlled field experiment, Stahl et al. demonstrated enhanced visibility of pedestrian (47.9 m increase) and child objects (63.1-m or 207-ft increase) compared to halogen headlamps, but no benefit for legibility of road signs.(47)Nilsson and Alm reported improvements in detection performance in simulated fog conditions in a driving simulator; however, lane deviations and speed increased.(46) Despite generalization concerns caused by the use of a simulator, the Nilsson and Alm findings suggest that studies must include multiple dependent variables to obtain a more complete picture of the overall effectiveness of VESs.
Incorporating measures of mental workload, speed choice, and pedestrian detections, Ward, Stapleton, and Parkes showed that speed reductions had no effect on pedestrian detections in the presence of VESs among drivers using a prototype VES on a closed test track.(66) The authors attributed the drivers’ decision to reduce their speed to increased workload imposed by the VES. The lack of an effect on pedestrian detection was not explained, although it is plausible that workload could explain the lack of a detection distance benefit.
To determine how to evaluate the potential benefits or limitations of VESs, Gish, Staplin, and Perel performed a small-scale investigation of driver performance and behavior using a mockup VES.(67) Four younger (26 to 36 years) and four older (56 to 70 years) participants drove an instrumented vehicle and orally reported the detection and recognition of targets placed along a predefined route while performing speed monitoring and navigation tasks. Although the mockup VES provided drivers with longer preview distances than low-beam headlamps alone, results suggested that drivers could not always take full advantage of this enhancement because of the visual, scanning, and cognitive demands of the driving tasks. Also, older drivers were less willing to use the mockup VES. Based on oral reports, the consensus among all observers was that the VES increased curve detection distances relative to low-beam headlamps alone.
Other researchers have focused on the potential of VESs in automotive applications to improve peripheral detection. For example, Bossi used a combination of infrared sensor and HUD technologies to research the effect of VES on driver peripheral visual performance.(68) To investigate unwanted effects on peripheral detection performance, Bossi, Ward, and Parkes studied the effect of a simulated VES on detection performance in dark and dusk viewing conditions.(65) The authors reported some degradation in peripheral target detection and recognition performance. Although this can be attributed to the presence of the simulated VES, it could also be attributed to sensory factors, eye scanning patterns, or attention factors. It is important to determine the reason for the peripheral performance degradation, if it exists, in order to recommend a VES design to overcome this problem.
Comparisons among these studies are complicated because the technical approaches ranged from using simulated VESs (e.g., a monochrome monitor displaying the same road scene as the forward screen) in a laboratory setting to actual dynamic viewing conditions using prototype VESs along a closed test track. Although enhanced performance is certainly a necessary condition for a net VES benefit, the effectiveness of supplemental VESs as night driving crash countermeasures cannot be determined from previous research.
The driving task involves performing a number of functions, the most important of which is guiding the vehicle within the roadway geometrics and TCDs while detecting other vehicles and nonmotorists and judging their speed, position, and possible behavior. Driving is largely a visual task; therefore, poor visibility conditions such as rain, fog, or snow may impose severe demands on drivers because their ability to collect necessary visual information is markedly degraded. The driving task becomes even more complex when such weather-related conditions of reduced visibility are accompanied by wet surfaces and darkness. The effect of these conditions on driver behavior has been a matter of concern for many years and the subject of past research.
Collins, Neale, and Dingus studied several factors that can affect the visibility and conspicuity of road signs, taking into consideration participant age (younger and older), weather (clear and rain), time of day (day and night), and in-vehicle signing information system (ISIS) use (ISIS and no ISIS).(69) Khattak, Kantor, and Council analyzed the impacts of adverse weather interactions with driver and roadway characteristics on occurrence and injury severity of selected crash types.(70) Another example of adverse weather research is the DRIVE II project ROSES, which dealt with improving traffic safety in adverse weather conditions.(71) The ROSES system consists of in-vehicle safety monitoring and driver support equipment and an infrastructure-based central monitoring system that combines inputs on road and weather conditions from various sources to derive the current and predicted safety level and recommended maximum speed. Khattak, Koppelman, and Schofer developed a conceptual framework to assess the impact of adverse weather on travel behavior.(72) The framework was used to evaluate the effects of weather and traffic information, individual attributes, and situational factors on drivers’ willingness to change normal travel patterns. Similarly, Vos presented a traffic simulation model based on literature sources and model analysis.(73) This model incorporates the influences of reduced friction and visibility. Simulation of a sudden visibility reduction shows that road capacity and traffic safety both decrease in such conditions. The gamut of these research topics represents the variety of adverse weather driving situations or conditions.
The literature review illustrates the need to research enhanced night visibility for driving. Following is a list of the primary issues from the literature review that were considered for the Phase II studies: