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The purpose of this study is to understand and quantify the simultaneous effects of roadway speed category, driver age, driver gender, vehicle class, and location on the rates of longitudinal and lateral acceleration epochs. The rate of usual as well as harsh acceleration epochs are used to extract insights on driving risk and driver comfort preferences. However, an analysis of acceleration rates at multiple thresholds incorporating various effects while using a large-scale and diverse dataset is missing. This analysis will fill this research gap. Data from the 2nd Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) was used for this analysis. The rate of occurrence of acceleration epochs was modeled using negative binomial distribution based generalized linear mixed effect models. Roadway speed category, driver age, driver gender, vehicle class, and location were used as the fixed effects and the driver identifier was used as the random effect. Incidence rate ratios were then calculated to compare subcategories of each fixed effect. Roadway speed category has the strongest effect on longitudinal and lateral accelerations of all magnitudes. Acceleration epoch rates consistently decrease as the roadway speed category increases. The difference in the rates depends on the threshold and is up to three orders of magnitude. Driver age is another significant factor with clear trends for longitudinal and lateral acceleration epochs. Younger and older drivers experience higher rates of longitudinal accelerations and decelerations. However, the rate of lateral accelerations consistently decreases with age. Vehicle class also has a significant effect on the rate of harsh accelerations with minivans consistently experiencing lower rates. Accepted version

This project characterized the performance of Connected Vehicle Systems (CVS) on motorcycles based on two key components: global positioning and wireless communication systems. Considering that Global Positioning System (GPS) and 5.9 GHz Dedicated Short-Range Communications (DSRC) may be affected by motorcycle rider occlusion, antenna mounting configurations were investigated. In order to assess the performance of these systems, the Virginia Tech Transportation Institute’s (VTTI) Data Acquisition System (DAS) was utilized to record key GPS and DSRC variables from the vehicle’s CVS Vehicle Awareness Device (VAD). In this project, a total of four vehicles were used where one motorcycle had a forward mounted antenna, another motorcycle had a rear mounted antenna, and two automobiles had centermounted antennas. These instrumented vehicles were then subject to several static and dynamic test scenarios on closed test track and public roadways to characterize performance against each other. Further, these test scenarios took into account motorcycle rider occlusion, relative ranges, and diverse topographical roadway environments. From the results, both rider occlusion and approach ranges were shown to have an impact on communications performance. In situations where the antenna on the motorcycle had direct lineof-sight with another vehicle’s antenna, a noticeable increase in performance can be seen in comparison to situations where the line of sight is occluded. Further, the forward-mounted antenna configuration provided a wider span of communication ranges in open-sky. In comparison, the rear-mounted antenna configuration experienced a narrower communication range. In terms of position performance, environments where objects occluded the sky, such as deep urban and mountain regions, relatively degraded performance when compared to open sky environments were observed.

Crash warning systems have been deployed in the high-end vehicle market segment for some time and are trickling down to additional motor vehicle industry segments each year. The motorcycle segment, however, has no deployed crash warning system to date. With the active development of next generation crash warning systems based on connected vehicle technologies, this study explored possible interface designs for motorcycle crash warning systems and evaluated their rider acceptance and effectiveness in a connected vehicle context. Four prototype warning interface displays covering three warning mode alternatives (auditory, visual, and haptic) were designed and developed for motorcycles. They were tested on-road with three connected vehicle safety applications - intersection movement assist, forward collision warning, and lane departure warning - which were selected according to the most impactful crash types identified for motorcycles. It showed that a combination of warning modalities was preferred to a single display by 87.2% of participants and combined auditory and haptic displays showed considerable promise for implementation. Auditory display is easily implemented given the adoption rate of in-helmet auditory systems. Its weakness of presenting directional information in this study may be remedied by using simple speech or with the help of haptic design, which performed well at providing such information and was also found to be attractive to riders. The findings revealed both opportunities and challenges of visual displays for motorcycle crash warning systems. More importantly, differences among riders of three major motorcycle types (cruiser, sport, and touring) in terms of riders’ acceptance of a crash warning interface were revealed. Based on the results, recommendations were provided for an appropriate crash warning interface design for motorcycles and riders in a connected vehicle environment.

Visual attention is a key component of the driving task. Identifying times when the driver is not looking toward the forward views has been shown to be a successful indicator of distracted driving (Olson, Hanowski, Hickman, & Bocanegra, 2009). Previous technologies have successfully captured eye-glance location, but have come at great expense in terms of both cost and intrusiveness. This project explored the possibility of using a low cost and unobtrusive Inertial Measurement Unit (IMU). Through data collected from the IMU, the aim was to identify vehicle kinematic signatures associated with times the driver is looking away from the forward view. Two methods were utilized. The first of the two methods relied on yaw rate anomalies to detect steering corrections. The second utilized the detection of sharp lateral accelerations which occurred as a result of corrective steering maneuvers. These methods both showed promise, as the yaw rate anomalies method and the lateral acceleration anomalies method both showed improved detection rates over the baseline.

This report describes the contents of the Roadway Epochs Database, which is a collection of tables providing indices for users to access vehicle data from the 100-Car and Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Studies based on the characteristics of the roadways traveled by study vehicles. This document is designed to give an overview of the organization of the tables as well as a brief introduction to the available vehicle and infrastructure data. The document opens with a section outlining intended uses and general information regarding the Roadway Epochs Database. The database contains 35 tables containing data from five state Departments of Transportation (DOTs), Nokia/Navteq, the SHRP 2 Roadway Information Database (RID), and the Federal Highway Administration (FHWA) Office of Highway Performance Monitoring. Epochs of naturalistic data are tied to information such as the number of lanes, average annual daily traffic counts for heavy trucks and light vehicles, International Roughness Index scores, vehicle miles traveled, functional class, and identified features such as transition zones, frontage roads, bridges, and tunnels. The document closes with a few sample queries and a brief introduction to Structured Query Language (SQL).

Records of latitude and longitude pairs describing an approximate path of travel are logged by many types of Global Positioning System (GPS)-enabled devices. These logs of latitude/longitude (lat/lon) pairs specify geographic locations but include error inherent in the GPS system. Digital map data include representations of roads, trails, airways, etc., with links and nodes that are located geospatially but that also include error. The following report describes an algorithm for matching GPS points to the correct road link in digital map data by using road network connectivity. The algorithm was applied to the naturalistic driving data from the second Strategic Highway Research Program (SHRP 2), the largest naturalistic driving study to date. The data set from SHRP 2 consists of 5.5 million trips, which generated approximately 3.7 billion latitude/longitude pairs that needed to be matched to roads represented in digital maps. When identifying roads from GPS data at this scale, both the processing speed and the accuracy of the algorithm are important. To evaluate the output accuracy, a sample of 100 randomly selected trips was compared to a manual route identification. The results indicate that the algorithm assigned driving data to the correct link 91% of the time. When the driving data were not on a link, the algorithm correctly recognized this 86% of the time.

Presented at the Fourth International Symposium on Naturalistic Driving Research in Blacksburg, VA

The results of a survey of 424 motorcyclists are presented in order to provide information for motorcycle safety research. Survey questions address topics related to rider demographics, riding experience (years, mileage, and trip description), motorcycle characteristics, rider training, safety issues (maintenance, equipment, and crash data), and respondent interest (willingness, reasons for hesitation, and acceptable compensation). The respondents' interest in participating in an on-road motorcycle study is analyzed relative to the other questions, and deterrents to participation, as well as desired compensation, are also discussed in order to design such studies to encourage participation. Results of a cluster analysis are also presented to theorize possible categorizations of riders in terms of the collected variables. This information will be a useful starting block in the design and execution of motorcycle safety research.

Motorcycle-related research questions of interest to the National Highway Traffic Safety Administration (NHTSA) were reviewed. Instrumentation techniques and study procedures that have been used for light- and heavy-vehicle studies were adapted for use in answering the motorcycle-related questions. Three motorcyclists rode with instrumentation for a total of over 3,100 miles. The final data acquisition system and instrumentation recorded acceleration in three axes, yaw, pitch, roll, geographic location, rear-wheel speed, position in lane, turn-signal use, braking, range and closing speed to forward objects, and five video views. The sensor and video data were collected continuously while the bike was running. Development of helmet-mounted eye tracking and three dimensional head tracking instrumentation for use in naturalistic studies was attempted. Study components including recruiting, screening, questionnaires, and garage procedures were also tested. Analyses were conducted to illustrate possible uses of the data and to confirm the effectiveness of the adapted instrumentation. An independent evaluator reviewed the project, including the technical approach, instrumentation, data and questionnaires. Demonstration of motorcycle instrumentation that will support the majority of NHTS's motorcycle research questions was successful. Instrumentation for fine measurement of gaze location in naturalistic situations was not successful. Identification of coarse scan behavior and general areas where riders are looking (e.g., forward, left, right, down, rearward) was possible. United States. National Highway Traffic Safety Administration. Office of Behavioral Safety Research

By employing the functionality of GIS, code was written which allows for an automated process to compare the GPS data recorded in the naturalistic driving data with geographic map data from the U.S. Census Bureau and road data from various sources, such as state departments of transportation or other providers. Points recorded in the naturalistic driving data which fall outside the boundaries of the Census Bureau's urbanized Areas or urban Clusters are determined to be rural. The points are further evaluated to determine whether or not the vehicle was being driven on an interstate highway. Points that are determined to be rural and not on interstate highways are segments of interest in addressing the rural road crash problem. Brad R. Cannon, Shane B. McLaughlin and Jonathan M. Hankey

The purpose of this investigation is to identify factors associated with run-off-road (ROR) crashes. Events from the 100-Car Naturalistic Driving Study that constituted ROR crash or near-crash events were investigated to identify conditions in which the events occurred and contributing factors. ROR events occur more frequently per mile in low-visibility (including darkness) and low-friction conditions than in clear and dry conditions. Approximately half of the events (56%) occurred on straight roadways, with the remainder occurring in curves (30%) and intersection turns (14%). The most frequently identified contributing factor among the ROR events was distraction. Changes in roadway boundaries (e.g., discontinuities) also appear to be a common factor. Short following distances appear to be more commonly a factor than lead-vehicle braking. Other factors include fatigue/impairment, low friction, vehicle encroaching on the subject vehicle, low-speed maneuvering errors, and late route selection. United States. National Highway Traffic Safety Administration. Office of Human-Vehicle Performance Research

This volume, an executive summary of the Enhanced Night Visibility project, is the first of 18 volumes that report on the project's evaluation of the merit of implementing supplemental ultraviolet headlamps, supplemental infrared systems, and other vision enhancement systems (VESs) to enhance drivers' nighttime roadway safety. The entire project evaluated 18 VESs in terms of their ability to provide object detection and recognition. Objects included scenarios with pedestrians standing or walking in different locations on the roadway. Pedestrians were dressed in black, white, or blue clothing to produce varying levels of contrast with their surroundings. Detection and recognition testing took place in clear weather, rain, snow, and fog conditions. Project research also evaluated a subset of the VESs for their effect on drivers' disability and discomfort glare. The VESs were also tested for their value in facilitating drivers' detection of pavement markings and other traffic control devices. The results indicated that supplemental ultraviolet headlamps do not provide sufficient benefit to justify further testing; however, supplemental infrared vision enhancement systems do offer an improvement over headlamps alone for detection of pedestrians. Near infrared systems have the potential to provide an added benefit in detecting pedestrians in inclement weather, but the implementation of NIR technology is the key to achieving this benefit. United States. Federal Highway Administration. Office of Safety R&D

Phase III-- Study 1 was performed to further explore findings on far infrared (FIR) systems from Phase II, to investigate near infrared (NIR) and high intensity discharge (HID) technologies, and to investigate detection and recognition of retroreflective infrastructure components. The empirical testing for this study was performed at the Virginia Smart Road testing facility during clear weather conditions. A total of 18 participants were involved in the study. A 6 by 3 by 17 mixed-factorial design was used to investigate the effects of 6 different types of vision enhancement systems, 3 age groups, and 17 object presentations on detection and recognition distances; subjective evaluations were obtained for the different systems as well. The results of the empirical testing suggest that infrared (IR) systems, when designed correctly, can provide pedestrian detection benefit in clear weather, particularly for pedestrians in dark clothing and veiled in the glare of oncoming headlamps. A wider field of view display appears to facilitate detection in curves of 1,250-m radius. Retroreflective objects may be detected earlier in an NIR display, but require direct visual observation to recognize the object or read signage. HID systems did not provide detection benefit over the baseline halogen headlamps tested. United States. Federal Highway Administration. Office of Safety R&D

Road departure crashes are among the deadliest crash modes in the U.S. each year. In response, automakers have been developing lane departure active safety systems to alert drivers to impending departures. These lane departure warning (LDW) and lane departure prevention (LDP) systems have great potential to reduce the frequency and mitigate the severity of serious lane and road departure crashes. The objective of this thesis was to characterize lane and road departures to better understand the effect of systems such as LDW and LDP on single vehicle road departure crashes. The research includes the following: 1) a characterization of lane departures through analysis of normal lane keeping behavior, 2) a characterization of road departure crashes through the development and validation of a real-world crash database of road departures (NCHRP 17-43 Lite), and 3) develop enhancements to the Virginia Tech LDW U.S. fleetwide benefits model. Normal lane keeping behavior was found to vary with road characteristics such as lane width and road curvature. Consideration of the dynamic driving behaviors observed in the naturalistic driving study (NDS) data is important to avoid LDW false alarms and driver annoyance. Departure characteristics computed in normal driving were much less severe than the departure parameters measured in real-world road departure crashes. The real-world crash data collected in NCHRP 17-43 Lite database was essential in developing enhancements to the existing Virginia Tech LDW fleetwide benefits model. Replacement of regression model predictions with measured crash data and improvement of the injury criteria resulted in an 11-16% effectiveness for road departure crashes, and an 11-15% reduction in seriously injured drivers. Master of Science

This report presents a method for estimating the presence and duration of preceding and lead vehicle in front of a motorcycle using an object detection algorithm guided by radar data. The video and radar data were collected as part of a large transportation project. The data are recorded by the ego vehicle during a trip while in a naturalistic research study. The goal is to validate objects detected by radar using vision, to identify moving preceding vehicles and the lead vehicle. The proposed approach takes advantage of radar data in locating the vehicles and other targets and then validates the targets as vehicles using Dual-Tree Branch-and-Bound (Kokkinos, 2011) object detection algorithm. Localization, detection and tracking took 0.0385 seconds per frame on average. Precision and recall of lead vehicle detection is 98.61% and 90.53% respectively. The algorithm presents a comprehensive approach to localize target vehicles in video. The radar object coordinates are mapped on the video frame using perspective projection map- ping. Then persistent radar objects are determined by analyzing their trajectory on video frames. When a radar object appears for three consecutive frames, its called a persistent object. A region of interest (ROI) around the persistent radar object is cropped from the frame, and passed to the object detection algorithm to determine if the persistent object is a car. Once a car is detected the validation of the radar object is complete. We track the detected car in the following frames and refresh the detection after every fourteen frames. The car detection algorithm runs whenever a new persistent radar object is introduced. After validating radar objects, at each timestamp, the lead vehicle is determined using radar object's forward and lateral distance. The time from detecting a lead vehicle to the time when the vehicle disappears or another vehicle becomes lead vehicle, is recorded to get the epochs of following driving mode for that lead vehicle. Finally, the detection result is integrated with MATLAB lane detection system to make a complete system for lead vehicle detection and tracking. The video of interest has 240x720 resolution and approximately 15 frames per second. The car detection algorithm takes 0.1960 seconds on average to detect one car in a machine with Windows operating system and 4GB RAM. But as the detection algorithm is not run for each frame it saves time. Since no annotated motorcycle video dataset is publicly available, two videos of 52 seconds and 26 seconds were manually annotated to test the performance of the approach. The current approach works almost at real time. The algorithm has been tested and results have been reported on 1 video. Master of Science

More motorcyclists are fatally injured each year in guardrail crashes than passengers of any other vehicle, while only accounting for three percent of the vehicle fleet. Since motorcyclists account for a high percentage of these fatalities, the goal of zero deaths on the road cannot be achieved without addressing the safety of motorcyclists. The objective of this research was to determine the factors that lead to serious or fatal injury in motorcycle barrier crashes, given that a crash occurred. The likelihood of serious or fatal injury in barrier crashes was significantly influenced by both barrier type and rider trajectory after striking the barrier. A national study of motorcyclist fatality risk using the Fatality Analysis Reporting System (FARS) and General Estimates System (GES) showed that crashes with guardrail than crashes were about 7 times more likely to be fatal than those with the ground, based on the most harmful event reported. An analysis of 1,000 riders in barrier crashes in three states showed that the odds of serious injury were 1.4 times greater in guardrail crashes than in concrete barrier crashes. These analyses did not take into account the trajectory of the rider after striking the barrier, since this was unknown. The police accident report for 350 barrier crashes in New Jersey was used to determine the rider trajectory in those crashes. Being ejected from the motorcycle after impacting the barrier significantly increased the odds of serious injury over crashes where the rider was not ejected. While providing insight into factors influencing injury severity, these analyses do not provide an understanding of the nature of injuries incurred in these crashes. To further understand how injuries were caused in motorcycle-barrier crashes, we developed a methodology for determining injury mechanisms in motorcycle-barrier collisions. Using this methodology, we investigated 9 serious motorcycle-to-barrier crashes. In these crashes, as well as in an analysis of 106 barrier crashes in Maryland, the thorax and lower extremities most commonly suffered serious injury. Of particular concern are the posts and top of the rail, both of which can lead to lacerations and blunt trauma. Ph. D.