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It is important for large trailers to be outlined with retroreflective tape to make them more conspicuous in roadway environments with diminished ambient lighting. Retroreflective material is also utilized on signs as well as clothing to improve their conspicuity. As used conspicuity tape does not perform at the same level as clean and new tape. Hence, there is a need for visibility testing of retroreflective materials with degraded or reduced effectiveness. In an effort to control the coefficient of retroreflection (RA). A methodology that uniformly obscured parts of the retroreflective materials was developed. Validation testing of this procedure was conducted using glass bead sheeting, as well as microscopic prismatic sheeting. The results from the study showed that, by uniformly obscuring parts of the tape, RA is approximately a linear function of the area exposed to the viewer. Thus, the overall perceived brightness and coefficient of retroreflection readings were reduced.

Rear-end crashes account for more than one in five fatal crashes in the U.S. The rear-end crash scenario most commonly associated with fatal crashes involves a following vehicle traveling 40 to 70 mph closing on a lead vehicle at a rate greater than 30 mph. The current research compiled an analysis of the literature to identify the kinematic factors, environmental factors, traffic-related factors and individual differences that are likely to influence drivers’ responses when closing on a slower-moving or stopped lead vehicle [LV]. In Part 1, several primarily kinematic-based methods for modeling drivers’ responses to a LV were compared for high-speed closing events. Methods utilizing looming (angular growth rate) equations were shown to predict drivers’ responses and time-to-contact methods (Inverse Tau) were conditionally accurate when applied to specific crash scenarios. However, the ratio or nominal response time methods did not predict drivers’ responses in most crash scenarios.

Analyses of driver response time studies and fatal crash statistics were examined to determine: 1. whether all rear-end crash types can be analyzed as one crash type, 2. average braking thresholds for drivers, and 3. the influence cell phone usage has on drivers’ response times when responding to a lead vehicle. The goal of this research is to recommend protocols for investigating LV crashes that is supported by the literature. Two distinct lead vehicle [LV] response time events emerged: LV platoon (two vehicles traveling together in close proximity) and LV looming (a vehicle approaching a stopped or much slower LV). In normal driving, platoon LV events were very common but resulted in very few crashes per exposure. Young drivers were over-represented when they did occur. Onset of the hazardous event was when the LV decelerated, and drivers began braking roughly 3 to 5 seconds before impact.

The turn-across-path from opposite-direction [LTAP-OD] crash type contributes to one of the major fatal crash types in young drivers. Drivers responses in police reportable and severe crashes and near crashes involving an LTAP-OD scenario were evaluated from query of the Second Strategic Highway Research Program [SHRP-2]. This research examined the responses of through drivers. 122 such events were analyzed to extract driver braking behavior, secondary tasks, age, and perception-response times. All measures of through driver variables were compared with respect to turning driver behavior. The study aimed to identify the trigger event for drivers to respond to the left turning vehicle. Time to contact was a significant factor which affected driver response times. Drivers responded significantly faster when subjected to shorter time to contact events compared to longer ones.

Collision statistics show that more than half of all pedestrian fatalities caused by vehicles occur at night. The recognition of objects at night is a crucial component in driver responses and in preventing nighttime pedestrian accidents. To investigate the root cause of this fact pattern, Richard Blackwell conducted a series of experiments in the 1950s through 1970s to evaluate whether restricted viewing time can be used as a surrogate for the imperfect information available to drivers at night. The authors build on these findings and incorporate the responses of drivers to objects in the road at night found in the SHRP-2 naturalistic database. A closed road outdoor study and an indoor study were conducted using an automatic shutter system to limit observation time to approximately ¼ of a second. Results from these limited exposure time studies showed a positive correlation to naturalistic responses, providing a validation of the time-limited exposure technique.

Controlled studies identified several factors that influence drivers' swerving when responding to in an emergency situation. Specifically, driver age, time-to-contact, amplitude of the steering action (steer within lane or swerving into the next lane), distraction, fatigue, natural lighting and available buffer space were identified as factors that influence steering behaviors. The goal of the current research was to identify the extent to which each factor changed swerving performances of drivers who were faced with a crash or near crash. Results from crashes and near crashes were obtained from the InSight (SHRP-2) naturalistic driving study. The results from the controlled studies and the results from the naturalistic driving research were consistent in many ways. Drivers engaged in a visual-manual secondary task were much younger than were the drivers who had no distracting secondary task.

A method for evaluating a driver's response in a nighttime crash scenario is offered. A pedestrian can be said to be within the headlight beam when the line representing the shape of a headlight beam equals the pedestrian approach vector. This method is based upon headlight beam mapping and the illumination necessary for drivers to recognize non-illuminated objects on an unlit road at night. The most notable information gained through this research is to be able to correlate headlight illumination with driver response distances. From 25 nighttime driver response distance experiments, information was gathered from many of the original authors. This information includes position left or right, headlight type, lighting, movement of the object or pedestrian, and the position (standing, slumped or laying).

A primary goal of crash reconstruction (or collision avoidance system) is to determine whether a crash is avoidable or not. A prerequisite for the determination of avoidance is knowledge of the time that is available to a driver. In a path intrusion crash scenario, a method to determine the time available for a major road driver is to know the time a minor road driver accelerated before impact. This research is an attempt to model the time based upon acceleration distance. The current study involved two parts. Part one was a naturalistic study of driver acceleration behavior at two-way-stop controlled intersections. In part two, ten drivers with instrumented vehicles were asked to drive a route that included four acceleration runs at two-way-stop sign control intersections. In the naturalistic study, the accelerations were measured using video recordings and videogrammetry at known distances.

The most common trigger for event data collection in Heavy Vehicle1 ECMs is a sudden decrease in the calculated vehicle speed. The calculated vehicle speed is a by-product of programmed calibrations and measured wheel speed data. In some cases, as is the case with Detroit Diesel ECMs, event data are recorded when the vehicle transitions from a driving state to a stopped state. Event data are reported with respect to time when the calculated vehicle speed change exceeds the preset threshold value or the first recorded 0 mph value. Because the data are not necessarily centered on the collision event itself, determination of impact speed and analysis of driver response can be problematic. A statistical evaluation of crash and non-crash related Heavy Vehicle Event Data Recorder (HVEDR) reports was conducted to identify specific measurable characteristics that can be used to identify the time of impact within reported event data.

Video recordings of real life traffic crashes and near crashes were analysed for driver response choice. These responses were compared to problem solving theories. In emergency situations drivers were likely to make relatively quick decisions. By allocating limited time to the decision, an algorithmic approach (that considers the probabilities of all options) is not possible in most cases. Instead a driver will decide upon a response using an intuitive (heuristic) approach. Intuitive decision-making is quicker and rule-of-thumb based but has predictable limitations. Drivers exhibited functional fixedness in that they did not select a “lesser” collision and nearly 40% of horn use was for chastising other drivers rather than for avoidance. Drivers exhibited difference reduction in that they were more likely to steer away from hazards.

Several previous studies report driver response times when responding to a lead vehicle. There have also been other studies that examined and measured the ability of drivers to detect the relative velocity of a lead vehicle. This study attempts to determine how the relative velocity detection threshold and driver response times fit together. There may be a significant difference between the times at which a lead vehicle is visible versus when it is perceivable as an immediate hazard. This research involved two parts; the first analyzes the raw data reported in previous research. The second part involved measuring responses of subjects using a laptop simulator. The goal of both parts of this research was to compare the subtended angular velocity [SAV] with the response times of drivers to determine if there is a point (threshold) at which response times level off at a fast rate.

The goal of this research was to develop mathematical equations that would estimate the response times of drivers in various situations. This research involved two studies. The first study involved the development of a series of equations that predict driver response times [DRTs]. Compiling a database of over 130 studies that measured DRT and coding for over 20 methodology and substantive variables was the source from which the equations were developed. Multiple Stepwise Linear Regression analysis was performed on the database. The analysis produced an empirical equation that revealed which variables and methods were statistically significant predictors of DRTs. The analysis showed that when all research data was analyzed together an accurate predictor could not be developed. However, when the database was divided into smaller sets based upon where the target emerged, empirical equations were developed. Each of six equations reached statistical significance.