A study examined the effects of reduced visibility of scene information because of fog on car-following performance. Drivers were presented with a straight roadway scene in a driving simulator and were asked to maintain a predetermined driving distance in response to speed variations of a lead vehicle. Lead vehicle speed varied according to a sum of three prime sine wave frequencies. Five simulated fog density conditions and three average lead vehicle velocities were examined. Car-following performance was assessed using distance headway, variance of distance headway, root-mean-square (RMS) velocity error, control gain, phase angle, and squared coherence. Distance headway decreased only at the highest fog density condition examined. RMS velocity error increased with an increase in fog density. These results indicate that drivers had greater difficulty responding to changes in lead vehicle speed than to changes in headway. Results for squared coherence indicated that the effects of fog were greatest for the highest rate of change in lead vehicle speed (i.e., highest frequency examined). The importance of visual factors for optimal car-following performance is discussed.

1. Effects of Reduced Visibility from Fog
on Car-Following Performance
Julie J. Kang, Rui Ni, and George J. Andersen
A study examined the effects of reduced visibility of scene information changes in LV speed. Driving simulation experiments were con-
because of fog on car-following performance. Drivers were presented with ducted that examined car-following performance when LV speed
a straight roadway scene in a driving simulator and were asked to main- varied according to a sum of sines function and a ramp function. In
tain a predetermined driving distance in response to speed variations of a addition, car-following performance was examined under real world
lead vehicle. Lead vehicle speed varied according to a sum of three prime driving conditions. Results of their study indicated that the DVA
sine wave frequencies. Five simulated fog density conditions and three model was more predictive of driver performance for simulator and
average lead vehicle velocities were examined. Car-following perfor- real-world car-following performance compared with models based
mance was assessed using distance headway, variance of distance head- on actual speed and distance.
way, root-mean-square (RMS) velocity error, control gain, phase angle, A unique characteristic of the DVA model is that visual angle and
and squared coherence. Distance headway decreased only at the highest change in visual angle are the sole sources of information for car fol-
fog density condition examined. RMS velocity error increased with an lowing. Given the richness of visual information in a driving scene
increase in fog density. These results indicate that drivers had greater dif- for distance and speed perception (e.g., texture, perspective, relative
ficulty responding to changes in lead vehicle speed than to changes in size) one would expect that drivers use other sources of information.
headway. Results for squared coherence indicated that the effects of An important question is whether any studies have found evidence of
fog were greatest for the highest rate of change in lead vehicle speed (i.e., use of other sources of visual information in a driving scene. A study
highest frequency examined). The importance of visual factors for opti- by Andersen and Sauer specifically examined that question (4).
mal car-following performance is discussed. Drivers were asked to perform a car-following task in which LV speed
varied according to a sum of sines function. They examined condi-
tions in which a driving scene was present (texture roadway, perspec-
An important driving task is to maintain a safe following distance tive of roadway, relative size information from roadway striping)
behind a lead vehicle (LV). This task—referred to as car following— or absent (only the LV was visible). They found more accurate car-
is important for traffic safety. Failure to maintain a safe distance following performance when a driving scene was present, indicat-
during car following may result in increased risk of a collision. ing that drivers use visual information in the surrounding scene to
Results of police accident reports indicate that over 30% of crashes maintain distance and regulate speed during car following.
are the result of rear-end collisions (1). It is likely that many of those Several conditions during real world driving exist that can reduce
accidents are the result of drivers failing to maintain a safe following visibility of the driving scene. For example, nighttime or fog condi-
distance or to respond to changes in LV speed. tions can cause reduced visibility. In the present study, one particular
condition is examined—the presence of fog. The presence of fog in a
driving scene results in a reduction of contrast that varies exponen-
DRIVING BY VISUAL ANGLE tially as a function of distance. Consequently, reduced contrast due to
fog can decrease distance information, such as the visible horizon (5)
Previous research on car following has proposed that drivers use or texture gradients (e.g., roadway texture) in the scene (6).
distance and speed information (2). However, drivers do not have Several studies have found evidence suggesting that the pres-
direct access to this information. Instead, distance and speed infor- ence of fog can have an impact on driver performance. Some stud-
mation is based on the driver’s perception of distance and speed ies have found decreased headway during car following under
and thus dependent on visual information for distance and speed. simulated fog conditions (7–9). Other studies have found that
Recently, Andersen and Sauer have proposed and tested a model fog can reduce the perceived speed of the driver’s vehicle (10).
[driving by visual angle (DVA) model] for car following based on Finally, Yonas and Zimmerman found a reduced ability of subjects
visual information for distance and speed (3, 4). Specifically, their to detect an approaching or receding object under reduced contrast
model uses visual angle and change in visual angle as the primary conditions (11).
source of information for maintaining distance and for detecting However, there are several limitations to previously published
research concerning fog and driving performance. For example, the
Department of Psychology, University of California–Riverside, Olmstead Hall, 900 Broughton et al. study examined the effects of fog and the change in
University Avenue, Riverside, CA 92521. Corresponding author: G. Andersen, distance headway (relative to a LV) following a right-hand turn (7).
john.andersen@ucr.edu. A limitation of that research is that it does not assess continuous car-
following performance. In addition, drivers adopted different driving
Transportation Research Record: Journal of the Transportation Research Board,
No. 2069, Transportation Research Board of the National Academies, Washington,
strategies under simulated fog conditions. Indeed, under the highest
D.C., 2008, pp. 9–15. fog conditions, some drivers adopted headway distances that were so
DOI: 10.3141/2069-02 great the LV was not visible, whereas other drivers adopted a driving
9

2. 10 Transportation Research Record 2069
distance that was well within the range of visibility. The Snowden et control response is smaller than the input, thus indicating that the
al. study examined the effects of reduced contrast of the driving scene driver is not responding with a sufficiently large control response
(associated with increase fog) on speed perception (10). The visual at that frequency. Phase angle is a measure of the time lag between
displays, however, did not include variation in contrast as a function the input and the control response, expressed in degrees relative to a
of distance that is present under real world fog conditions. Studies 180 degree cycle of the sine wave. It provides information on the
that examined speed perception and fog (12) and included the cor- response lag to changes in LV speed at a particular frequency. Squared
rect simulation of fog (i.e., where contrast varied as a function of coherence is a measure of squared correlation between the input and
distance) failed to replicate the results of Snowden and colleagues response at a particular frequency and provides a measure of the
(10). Finally, the Yonas and Zimmerman study also failed to vary variance accounted in tracking performance. Squared coherence is
contrast as a function of distance when simulating fog (11). defined as the ratio of the squared cross-amplitude values (of signal
The goal of the present study was to examine the effects of fog on and response frequencies) to the product of the spectrum density
car-following performance when fog was correctly simulated and estimates (of signal and response frequencies).
when the car-following task involved continuous control. To exam-
ine this issue, drivers were presented with a driving scene consist-
ing of a straight roadway in an urban setting (see Figure 1). An LV EXPERIMENT
was located directly in front of the driver, and speed of the LV var-
ied according to a sum of three sine wave frequencies. Average Drivers
speed of the LV was 40, 60, or 80 km/h. Drivers were presented with
an initial following distance at a constant speed for 5 s, followed by Twelve college students (mean age and standard deviation of the
variations in LV speed according to the sum of sines function. The group were 23.1 and 3.4) were recruited for the study and were paid
sum of sines function was based on three nonharmonic frequencies for their participation. Before the experiment, all drivers submitted
and resulted in a variation in LV speed that was not repetitive. Five to visual, attentional, and cognitive tests including useful field of
levels of simulated fog were examined. view, Snellen’s static acuity, contrast sensitivity, Wechsler Adult
Car-following performance was assessed using a variety of mea- Intelligence Scale and Kaufman Brief Intelligence Test, and color
sures that examine both overall performance for a single trial and blindness. All participants reported normal or corrected-to-normal
specific aspects of performance in response to the sum of sines func- vision and were currently licensed drivers. Mean contrast sensitivity
tion. Those analyses are referred to as global and local measures of the group, assessed using Pelli-Robson Contrast sensitivity chart,
of performance. Global measures of performance were derived by was 1.68 (standard deviation of 0.09).
calculating, on each trial, the average distance headway (between
driver and LV), variance of distance headway (a measure of overall
error in maintaining the predetermined following distance), and root- Design
mean-square (RMS) error in matching LV speed.
Local measures of performance were derived, on each trial, using Three independent variables were examined: simulated fog (simu-
a fast Fourier transform (FFT) and examining gain, phase angle, lated fog density levels of 0.0, 0.05, 0.10, 0.15, 0.20), average LV
and squared coherence. Gain is a measure of the amplitude of the velocity (40, 60, 80 km/h), and frequency of velocity change
response relative to the input signal at a particular frequency, and (0.033, 0.083, 0.117 Hz). All variables were run as within-subjects
it is informative about the response sensitivity of the driver. Gain variables.
values greater than 1 indicate that the control response is larger than
the input, thus indicating that the driver is responding with greater
control than is necessary. Gain values less than 1 indicate that the Apparatus
Displays were presented on a Dell 670 workstation. A Thrustmaster
Formula T2 steering system, including acceleration and brake pedals,
was used for closed-loop control of the simulator. The foot pedal and
a BG Systems serial box (analog to digital converter system) were
used to produce closed-loop control that was updated at 36 Hz. Pre-
sentations were on a 25.8 by 34.7 degree display. The display update
was 36 Hz.
Driving Simulation Scenario
The roadway consisted of three traffic lanes (representing a three-
lane, one-way road) with the driver and LV located in the center
lane. The LV was a red-colored sedan (6.3 degree visual angle at
a headway distance of 18 m). The Michaelson contrast of the tail-
lights and vehicle body, under the 0.0 fog density condition, was
0.48. Average luminance of the driving scene was 24.7 cd/m2. A
black-and-white gravel texture pattern was used to simulate asphalt.
FIGURE 1 Sample image of driving simulation scene. The fog Dashed lines (2 m in length positioned every 2 m along the roadway)
density level depicted is the intermediate (0.10) value. were used to simulate lane markers. The city buildings and LV were

3. Kang, Ni, and Andersen 11
produced by digitally photographing real buildings and a vehicle The procedure was that drivers were seated in the simulator and
and using the digital images as texture maps for the roadway scenes. told to maintain their initial separation from the LV despite changes
The images were digitally altered to increase the realism of the sim- in LV velocity. Each trial consisted of two phases. During the first
ulator scene (e.g., remove specular highlights, add shading) and were phase (lasting 5 s), the driver’s vehicle was positioned 18 m behind the
scaled to be appropriate with the geometry of the simulation. Lane LV with a velocity that matched the LV speed (40, 60, or 80 km/h).
width was 3.8 m. Control input (acceleration–deceleration) was not allowed during this
Drivers were presented with a car-following scenario in which phase, and drivers were instructed that the headway distance during
the LV varied its velocity according to a sum of 3 equal-energy sinu- this phase was the desired headway distance.
soids (i.e., peak accelerations and decelerations of each sine wave in Following the first phase, a tone sounded indicating the start of
the signal were equivalent). The corresponding amplitudes for these the second phase of the trial. During this phase, control input was
sinusoids were 9.722, 3.889, and 2.778 km/h. The range of speeds allowed, and LV speed varied according to the sum of sine wave
produced by the sum of sines function was ± 12.3 km/h about the frequencies. Drivers were instructed that the LV speed would
mean speed. At the beginning of each trial run, drivers were given 5 s change and to maintain the following distance indicated during
of driving at a constant speed 18 m behind the constant speed LV to the first phase by using the acceleration and brake pedals. Drivers
establish a perception of the desired distance to be maintained. were given 15 min of practice in the simulator with changes in LV
The three sinusoids were out of phase with one another. The initial speed (determined by a single sine wave of 0.033 Hz) to become
phase of the high and middle frequency was selected randomly, with familiar with control dynamics of the simulator. Then drivers were
the phase value of the low frequency selected to produce a sum of given two trials of each combination of velocity and fog density,
zero. This manipulation ensured that the velocity profile of the LV, for a total of 30 trials. Trial duration was 1 min. Drivers were given
following the 5 s of constant speed, would vary from trial to trial with a brief break following 15 trials. Duration of the experimental session
a smooth speed transition following the period of constant speed. was 1 h.
Feedback for the car-following task was used by activating a horn
sound if the headway (distance between driver and LV) exceeded
Simulated Fog 21 m. The purpose of the horn was to ensure that the driver closely
attended the car-following task, and it was intended to simulate an
To simulate realistic effects of fog, the computer simulation used the impatient driver behind the driver’s vehicle. The horn sound was used
formula L(s, θ) = LoFex(s) + Lin(S, θ) to produce variations in fog as feedback for both practice and experimental trials. The average
(13). The formula reduces overall contrast of the driving scene and frequency of horn activation per trial was 1.8.
produces a contrast gradient that varies exponentially as a function
of distance. This formula is important, for previous research con-
cerning simulated fog and driving varied contrast but not changes in RESULTS
the contrast gradient as a function of distance (10, 11). Lo represents
the light reflected from the object. Fex(s) is the amount of light that Two types of performance were assessed. Global driving perfor-
reaches the driver divided by the amount of light decay that occurs mance was determined by deriving measures of mean following dis-
with distance. Fex(s) can be replaced with e−bms to represent the decay tance, variance of following distance, and RMS speed error (driver
factor for fog using a Mei-scattering criterion. Lin(S, θ) represents the velocity relative to LV velocity). Local performance measures were
scattering of light from fog. The specific fog values in the simulation, determined by analyzing the velocity of the driver’s vehicle using FFT
using this equation, were 0.0, 0.05, 0.10, 0.15, and 0.20. These values and deriving control gain (output–input) phase angle, and squared
were selected to represent a range of conditions from high visibility coherence values for each frequency. Previous studies have used mea-
(0.0 fog condition) to low visibility (0.20 fog condition). The low- sures of control gain, phase angle. and squared coherence to assess
visibility condition was selected based on informal observations car-following performance (2, 3, 14).
indicating that under this condition, the LV was barely visible at the
desired following distance of 18 m.
To provide metrics that can be used to replicate the same simu- Distance Measure
lated fog conditions, the variation in contrast was calculated (using
a LiteMate photometer) as a function of fog density. Contrast was Mean Distance Headway
determined by measuring luminance differences between the rear The mean distance headway (distance between driver and LV in
tire of the LV (darkest region of the view of the LV) and bumper of meters) was calculated for each driver in each condition and analyzed
the LV (lightest region of the view of the LV). Measurements were in a 5 (fog density) by 3 (velocity) analysis of variance (ANOVA).
taken with the LV at a simulated distance of 18 m (desired follow- The main effect of fog on mean distance headway was significant,
ing distance). Contrast values were derived using the Michaelson F(4, 44) = 21.8, p < .05 (see Figure 2). Post hoc tests [Tukey HSD
contrast formula: (honestly significant difference) tests] indicated significant dif-
ferences (p > .05) in mean distance headway between the 0.20 fog
contrast =
( luminance max − luminance min ) density condition and all other fog density conditions.
( luminance max + luminance min ) There was a significant main effect of velocity on mean distance
headway, F(2, 22) = 20.26, p < .05. Mean distance headway for the
Contrast values for the 0.0, 0.05, 0.10, 0.15, and 0.20 simulated 40, 60, and 80 km/h speeds were 17.6, 19.1, and 19.9, respectively.
fog density levels were 0.55, 0.20, 0.10, 0.05, and 0.002. The LV Post hoc tests indicated significant differences between the 40 and
was visible to all drivers under the simulated fog conditions at the 60 km/h conditions and between the 40 and 80 km/h speed condi-
predetermined driving distance. tions. An important issue is whether the effect of velocity on

4. 12 Transportation Research Record 2069
21.0
20.5
Mean Distance Headway (meters)
20.0
19.5
19.0
18.5
18.0
17.5
17.0
16.5
16.0
0.0 0.05 0.10 0.15 0.20
Fog Density
FIGURE 2 Effect of fog density on mean distance headway.
distance headway was the result of drivers maintaining a constant were not significant, p < .05. In addition, the interaction of fog and
following interval (following distance divided by velocity). The aver- velocity was not significant, F(8, 48) = 0.79, p < .05.
age following interval for the 40, 60, and 80 km/h velocity conditions
were 1.58, 1.14, and 0.89 s, respectively. These values indicate that
the following interval varied across velocity conditions, suggesting Speed Measure
that drivers did not maintain a fixed following interval across different
velocity conditions. The interaction between velocity and fog density RMS Velocity Error
was not significant, F(8, 88) = 0.43, p > .05.
The RMS velocity error was derived for each driver in each fog and
velocity condition and analyzed in a 5 (fog density) by 3 (velocity)
Distance Headway Variance ANOVA. There was a significant main effect of fog on RMS error,
F(4, 44) = 9.99, p < .05 (see Figure 3). Generally, velocity control
The variance of distance headway (distance between driver and LV) error increased with an increase in fog density. Post hoc comparisons
was calculated for each driver in each condition and analyzed in indicated significant differences (p < .05) between the fog density
a 5 (fog density) by 3 (velocity) analysis of variance (ANOVA). The conditions of 0.15 and 0.05, 0.15 and 0.0, 0.20 and 0.10, 0.20 and
main effects of fog [ F(4, 44) = 0.51] and velocity [ F(2, 22) = 2.43] 0.05, and 0.20 and 0.0.
8
7
6
RMS Velocity Error (km/h)
5
4
3
2
1
0
0.0 0.05 0.10 0.15 0.20
Fog Density
FIGURE 3 Effect of fog density on mean RMS velocity error.

5. Kang, Ni, and Andersen 13
There was a significant main effect of velocity on RMS veloc- particular frequency) values derived for each frequency. The aver-
ity error, F(2, 22) = 5.33, p < .05. The average RMS velocity error age gain, phase angle, and squared coherence scores were derived
for the 40, 60, and 80 km/h velocity conditions were 5.826, 5.820, for each driver and analyzed in a 3 (velocity) by 3 (frequency) by
and 6.41, respectively. Post hoc tests (Tukey HSD tests) indicated 5 (fog density) ANOVA.
significant differences ( p < .05) between the 80 and the 40 km/h,
and between the 80 and the 60 km/h velocity conditions. The inter-
action between velocity and fog was not significant, F(8, 88) = .47, Control Gain
p > .05.
Effects of fog and frequency are shown in Figure 4. The main effect
of velocity was significant, F(2, 22) = 3.9, p < .05. The average
Fast Fourier Transform Analyses control gain for the 40, 60, and 80 km/h velocity conditions were
1.13, 1.08, and 1.10, respectively. The main effect of frequency
The velocity of the driver’s vehicle was recorded and analyzed was significant, F(2, 22) = 42.2, p < .05. The average control gain
using FFT with control gain (output–input) phase angle, and squared for the 0.033, 0.083, and 0.117 Hz frequencies were 1.00, 1.08,
coherence (squared correlation between input and response at a and 1.22, respectively. The main effect of fog was not significant,
1.5
0.00 Fog Density
1.4 0.05 Fog Density
0.10 Fog Density
0.15 Fog Density
1.3 0.20 Fog Density
Control Gain
1.2
1.1
1.0
0.9
0.033 0.083 0.117
Frequency (Hz)
(a)
0
-3
-6
-9
Phase Angle (deg)
-12
-15
-18 0.00 Fog Density
0.05 Fog Density
-21 0.10 Fog Density
0.15 Fog Density
-24 0.20 Fog Density
-27
-30
0.033 0.083 0.117
Frequency (Hz)
(b)
FIGURE 4 Functions of frequency and fog density: (a) control gain
and (b) phase angle.

6. 14 Transportation Research Record 2069
F(4, 44) = 1.07, p > .05. In addition, no significant interactions were DISCUSSION OF RESULTS
obtained with fog as a variable, p > .05. These results suggest that
variations in fog density did not affect overall sensitivity to respond Results of the present study suggest several important conclusions
to variations in LV speed. in regard to effects of fog on car-following performance. Results for
distance headway indicated a significant effect of fog density, with
the greatest effect occurring under the highest fog condition exam-
Control Phase Angle ined. This finding is inconsistent with results of the Broughton et al.
study (7 ). In their study, they found increased distance headway
Overall effects of fog and frequency are shown in Figure 4. The at lower fog density levels comparable to the second highest level
main effect of velocity was significant, F(2, 22) = 10.4, p < .05. The of fog density examined in the present study (a contrast ratio of
average phase angle for the 40, 60, and 80 km/h velocity conditions 0.04 at 18.5 m in the Broughton et al. study compared to the 0.02
were −12.6, −6.6, and −6.6 degrees, respectively. The main effect contrast ratio in the present study). Two factors may account for
of frequency was significant, F(2, 22) = 24.5, p < .05. The average the different results obtained in the present study and those of the
control gain for the 0.033, 0.083, and 0.117 Hz frequencies were Broughton et al. study.
−1.14, −9.7, and −14.9 degrees, respectively. The main effect of Differences in instructions used in the two studies may be one
fog was not significant, F(4, 44) = 1.07, p > .05. In addition, no such factor. In the Broughton et al. study, drivers were not given any
significant interactions were obtained with fog as a variable, p > .05. instructions about car following (7). Instead, they were instructed to
These results suggest that variations in fog density did not affect the follow the rules of the road during driving. In contrast, drivers in the
time to respond to speed variations of the LV. present study were instructed to maintain a following distance
behind the LV. This difference may account for the different driving
strategies adopted by drivers in the Broughton et al. study.
Squared Coherence A second factor concerns speed of the LV. In the present study,
LV speed varied on each trial, requiring drivers to continuously con-
The main effect of fog, F(4, 44) = 4.46, p < .05, and the interaction trol speed. However, LV vehicle speed was constant during the trial
of fog and frequency, F(8, 88) = 2.3 (see Figure 5), were significant, in the Broughton et al. study.
p < .05. The mean squared coherence for the 0.0, 0.05, 0.10, 0.15, Results for driver speed indicated greater RMS error in respond-
and 0.20 fog density levels were 0.954, 0.953, 0.957, 0.944, and ing to changes in LV speed with increased fog density. The increase
0.921, respectively. Post hoc comparisons (Tukey HSD tests) indi- in RMS error occurred from moderate to high fog density conditions
cated significant differences between the 0.20 fog density condi- examined. The researchers believe the increased error in responding
tion and the 0.0, 0.05, and 0.10 fog density conditions. According to to lead vehicle speed was due to a decreased ability of drivers to
these results, squared coherence decreased for the highest fog den- see surrounding texture in the driving scene, which is used to per-
sity condition examined, particularly at the highest frequency exam- ceive their own vehicle speed. Inability to accurately determine vehi-
ined. This finding suggests that when the fog density level results in cle speed likely resulted in overcorrections to changes in LV speed,
minimum visibility of LV, the driver has considerable difficulty in resulting in greater error. Indeed, the highest control gain occurred
responding to faster variations in LV speed. The main effect of fre- when drivers were presented with the highest simulated fog condi-
quency, F(2, 22) = 28.9, and velocity, F(2, 22) = 6.1, were signifi- tion. Since the gain value was greater than 1, results indicate that
cant. However, there was no significant interaction between fog drivers had the greatest degree of over controlling for this condi-
density and velocity, F(8, 80) = 0.80, p > .05. tion. Additional evidence of this effect was obtained for the squared
1.00
0.95
Squared Coherence
0.90
0.85 0.00 Fog Density
0.05 Fog Density
0.10 Fog Density
0.80 0.15 Fog Density
0.20 Fog Density
0.75
0.70
0.033 0.083 0.117
Frequency (Hz)
FIGURE 5 Squared coherence as function of frequency and fog density.

7. Kang, Ni, and Andersen 15
coherence measure. The significant fog by frequency interaction for fog on headway distance in car following is significant only when
squared coherence indicated a decrease in variance accounted for at fog density levels result in minimum visibility of the LV. In contrast,
the highest frequency. This finding indicates an increased error in the effect of fog on velocity RMS error occurs under moderate lev-
responding to LV speed at the highest frequency, resulting in the els of fog density. These results suggest two human factor applica-
decreased squared coherence value. These results suggest that one tions. The first application concerns car-following models. Most
factor that might increase the risk of a rear-end collision under fog car-following models include components for distance headway
conditions is the reduced ability of drivers to regulate vehicle speed and velocity. The DVA model also includes both components (3).
due to reduced visibility of the surrounding driving scene. Modifications to the DVA model should focus on changes to the
The present study examined the effects of fog on car following velocity component of the model. For example, adding a noise com-
when driving on a straight roadway. An important question is whether ponent for estimating velocity change, particularly velocity changes
similar results would be obtained for curved roadways. The presence that occur at higher frequencies, may result in increased predictive
of a curved roadway introduces two important factors for driving per- power of car-following performance under fog conditions. A second
formance that would likely lead to different performance, when application concerns in-vehicle warning or adaptive control systems
compared with performance on straight roadways. For example, dur- for reduced visibility conditions. Results of the present study indicate
ing driving in fog with a straight roadway, the driver can assume no that drivers have greater difficulty, under fog conditions, with adjust-
change in the path of the roadway. However, when driving a curved ing speed in response to a change in LV speed rather than main-
roadway, the driver must detect and respond to a change in path that taining distance headway. This finding suggests that warning or
may not be visible under high fog density conditions. Indeed, the adaptive control systems might be developed that primarily respond
availability of preview information (i.e., visibility of the curved to variations in LV speed.
roadway that the driver must steer) would vary as a function of fog
density. Increased fog density results in decreased visibility of the
roadway located at greater distances. Under these conditions, the REFERENCES
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in speed rather than do variations in headway distance. The effect of The Vehicle User Characteristics Committee sponsored publication of this paper.