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Destination Areas provide faculty and students with new tools to identify and solve complex, 21st-century problems in which Virginia Tech already has significant strengths and can take a global leadership role. The initiative represents the next step in the evolution of the land-grant university to meet economic and societal needs of the world.
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Browsing Destination Areas (DAs) by Content Type "Government document"
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- AERIS : Eco-Vehicle Speed Control at Signalized Intersections Using I2V CommunicationRakha, Hesham A.; Kamalanathsharma, Raj Kishore; Ahn, Kyoungho (United States. Joint Program Office for Intelligent Transportation Systems, 2012-06)This report concentrates on a velocity advisory tool, or decision support system, for vehicles approaching an intersection using communication capabilities between the infrastructure and vehicles. The system uses available signal change information, vehicle characteristics, lead vehicle characteristics, and intersection features to compute the fuel-optimal vehicle trajectory. The proposed system involves a complex optimization logic incorporating roadway characteristics, lead vehicle information, vehicle acceleration capabilities and microscopic fuel consumption models to generate a fuel-optimal speed profile. The research also develops a MATLAB application in order to demonstrate the potential of an in-vehicle application of such a technology.
- Agent-Based Game Theory Modeling for Driverless Vehicles at IntersectionsRakha, Hesham A.; Zohdy, Ismail H.; Kamalanathsharma, Raj Kishore (United States. Department of Transportation, 2013-02-19)This report presents three research efforts that were published in various journals. The first research effort presents a reactive-driving agent based algorithm for modeling driver left turn gap acceptance behavior at signalized intersections. This model considers the interaction between driver characteristics and vehicle physical capabilities. The model explicitly captures the vehicle constraints on driving behavior using a vehicle dynamics model. In addition, the model uses the driver's input and the psychological deliberation in accepting/rejecting a gap. The model is developed using a total of 301 accepted gaps and subsequently validated using 2,429 rejected gaps at the same site and also validated using 1,485 gap decisions (323 accepted and 1,162 rejected) at another site. The proposed model is considered as a mix between traditional and reactive methods for decision making and consists of three main components: input, data processing and output. The input component uses sensing information, vehicle and driver characteristics to process the data and estimate the critical gap value. Thereafter, the agent decides to either accept or reject the offered gap by comparing to a driver-specific critical gap (the offered gap should be greater than the critical gap for it to be accepted). The results demonstrate that the agent-based model is superior to the standard logistic regression model because it produces consistent performance for accepted and rejected gaps (correct predictions in 90% of the observations) and the model is easily transferable to different sites. The proposed modeling framework can be generalized to capture different vehicle types, roadway configurations, traffic movements, intersection characteristics, and weather effects on driver gap acceptance behavior. The findings of this research effort is considered as an essential stage for modeling autonomous/driverless vehicles The second effort develops a heuristic optimization algorithm for automated vehicles (equipped with cooperative adaptive cruise control CACC systems) at uncontrolled intersections using a game theory framework. The proposed system models the automated vehicles as reactive agents interacting and collaborating with the intersection controller (manager agent) to minimize the total delay. The system is evaluated using a case study considering two different intersection control scenarios: a four-way stop control and the proposed intersection controller framework. In both scenarios, four automated vehicles (a single vehicle per approach) were simulated using a Monte Carlo simulation that was repeated 1000 times. The results show that the proposed system reduces the total delay relative to a traditional stop control by 35 seconds on average, which corresponds to an approximately 70 percent reduction in the total delay. The third effort presents a new tool for optimizing the movements of autonomous/driverless vehicles through intersections: iCACC. The main concept of the proposed tool is to control vehicle trajectories using Cooperative Adaptive Cruise Control (CACC) systems to avoid collisions and minimize intersection delay. Simulations were executed to compare conventional signal control with iCACC considering two measures of effectiveness - delay and fuel consumption. Savings in delay and fuel consumption in the range of 91 and 82 percent relative to conventional signal control were demonstrated, respectively. It is anticipated that the findings of this report may contribute in the future of advanced vehicles control and connected vehicles applications.
- Assessment of a Drowsy Driver Warning System for Heavy Vehicle Drivers: Final ReportOlson, Rebecca Lynn; Morgan, Justin F.; Hanowski, Richard J.; Daily, Brian; Zimmermann, Richard P.; Blanco, Myra; Bocanegra, Joseph L.; Fitch, Gregory M.; Flintsch, Alejandra Medina (United States. National Highway Traffic Safety Administration, 2008)Drowsiness has a globally negative impact on performance, slowing reaction time, decreasing situational awareness, and impairing judgment. A field operational test of an early prototype Drowsy Driver Warning System was conducted as a result of 12 years of field and laboratory studies by the National Highway Traffic Administration and the Federal Motor Carrier Safety Administration. This project included Control and Test groups. The final data set for the analysis consisted of 102 drivers from 3 for-hire trucking fleets using 46 instrumented trucks. Fifty-seven drivers were line-haul and 45 were long-haul operators. The data set contained nearly 12.4 terabytes of truck instrumentation data, kinematic data, and video recordings for 2.4 million miles of driving and 48,000 driving-data hours recorded, resulting in the largest data set ever collected by the U.S. Department of Transportation. In this study, 53 research questions were addressed related to safety benefits, acceptance, and deployment. Novel data reduction procedures and data analyses were used. Results showed that drivers in the Test Group were less drowsy. Drivers with favoring opinions of the system tended to have an increase in safety benefits. Results of the assessment revealed that the early prototype device had an overall positive impact on driver safety.
- Data Mining and Gap Analysis for Weather Responsive Traffic Management ProgramKrechmer, Daniel; Rakha, Hesham A.; Howard, Mark; Huang, Weimin; Zohdy, Ismail H.; Du, Jianhe (United States. Federal Highway Administration, 2010)Weather causes a variety of impacts on the transportation system. An Oak Ridge National Laboratory study estimated the delay experienced by American drivers due to snow, ice, and fog in 1999 at 46 million hours. While severe winter storms, hurricanes, or flooding can result in major stoppages or evacuations of transportation systems and cost millions of dollars, the day-to-day weather events such as rain, fog, snow, and freezing rain can have a serious impact on the mobility and safety of the transportation system users. Despite the documented impacts of adverse weather on transportation, the linkages between inclement weather conditions and traffic flow in existing analysis tools remain tenuous. This is primarily a result of limitations on the data used in research activities. The overall goal of this research was to identify gaps in the data necessary to develop weather responsive traffic management studies. Activities conducted to achieve this included 1) A comprehensive search and documentation of traffic and weather data in the United States and abroad that could be used for WRTM; 2) surveys, phone calls and site visits with organizations that have suitable traffic data on inclement weather; 3) identification of critical gaps in regards to the collection and processing of traffic data on inclement weather conditions; and 4) recommendation of strategies for gathering and processing data that will be used in WRTM studies. The study found that there are a number of useful research efforts underway both domestically and internationally that are yielding useful data for WRTM analysis. In some cases the scopes are limited and confidentiality issues were found in a number of European studies. There is increasing availability of quality traffic and weather data being generated by transportation and public/private weather information sources in the U.S. The analysis conducted for this project found that this data can be helpful in identifying adverse weather impacts on speed and lane usage. The report recommends that FHWA work closely with agencies as they expand their RWIS to assure that weather data is of adequate quality for WRTM analysis. FHWA also should continue to fund specific research and evaluation activities in conjunction with the Integrated Corridor Management Program or other WRTM initiatives.
- The Drowsy Driver Warning System Field Operational Test: Data Collection Methods: Final ReportHanowski, Richard J.; Blanco, Myra; Nakata, Akiko; Hickman, Jeffrey S.; Schaudt, William A.; Fumero, Maria C.; Olson, Rebecca Lynn; Jermeland, Julie; Greening, Michael; Holbrook, G. Thomas; Knipling, Ronald R.; Madison, Phillip (United States. National Highway Traffic Safety Administration, 2008-09)A Drowsy Driver Warning System (DDWS) detects physiological and/or performance indications of driver drowsiness and provides feedback to drivers regarding their state. The primary function of a DDWS is to provide information that will alert drivers to their drowsy state and motivate them to seek rest or take other corrective steps to increase alertness. The system tested in this study was the Driver Fatigue Monitor (DFM) developed by Attention Technologies, Inc., which estimates PERCLOS (percent eye closure). The primary goal of this field operational test (FOT) was to determine the safety benefits and operational capabilities, limitations, and characteristics of the DFM. The FOT was conducted in a naturalistic driving environment and data were collected from actual truck drivers driving commercial trucks. During the course of the study, 46 trucks were instrumented with a Data Acquisition System (DAS). Over 100 data variables such as the PERCLOS output from the DFM and driving performance data (e.g., lane position, speed, and longitudinal acceleration) were collected. Other collected measures included video, actigraphy, and questionnaires. The FOT had 103 drivers participate. Drivers were randomly assigned to either control (24 drivers) or experimental groups (79 drivers). The data collected include the following: approximately 46,000 driving-data hours; 397 load history files from 103 drivers; approximately 195,000 hours of activity/sleep data; questionnaires from all drivers; fleet management surveys from each company; and focus group results collected from 14 drivers during two post-study focus group sessions. The focus of this report is the description of the data collection procedures.
- Human Factors Evaluation of Level 2 and Level 3 Automated Driving Concepts: Concepts of OperationMarinik, Andrew; Bishop, Richard; Fitchett, Vikki L.; Morgan, Justin F.; Trimble, Tammy E.; Blanco, Myra (United States. National Highway Traffic Safety Administration, 2014-07)The Concepts of Operation document evaluates the functional framework of operations for Level 2 and Level 3 automated vehicle systems. This is done by defining the varying levels of automation, the operator vehicle interactions, and system components; and further, by assessing the automation relevant parameters from a scenario-based analysis stand-point. Specific to the “Human Factors Evaluation of Level 2 and Level 3 Automated Driving Concepts" research effort, scenarios and literature are used to identify the range of near- to mid-term production-intent systems such that follow-on research topics with highest impact potential can be identified through commonalities in operational concepts.
- Human Factors Evaluation of Level 2 and Level 3 Automated Driving Concepts: Past Research, State of Automation Technology, and Emerging System ConceptsTrimble, Tammy E.; Bishop, Richard; Morgan, Justin F.; Blanco, Myra (United States. National Highway Traffic Safety Administration, 2014-07)Within the context of automation Levels 2 and 3, this report documents the proceedings from a literature review of key human factors studies that was performed related to automated vehicle operations. This document expands and updates the results from a prior literature review that was performed for the US DOT. Content within this document reflects the latest research and OEM activity as of June 2013. Studies both directly addressing automated driving, and those relevant to automated driving concepts have been included. Additionally, documents beyond the academic literature, such as articles, summaries, and presentations from original equipment manufacturers and suppliers, have been researched. Information from both United States and international projects and researchers is included. This document also identifies automated-driving relevant databases in support of future research efforts.
- Human Performance Evaluation of Light Vehicle Brake Assist SystemsFitch, Gregory M.; Blanco, Myra; Morgan, Justin F.; Rice, Jeanne C.; Wharton, Amy E.; Wierwille, Walter W.; Hanowski, Richard J. (United States. National Highway Traffic Safety Administration, 2010-04)The Brake Assist System (BAS) is a safety feature that supplements drivers' inadequate braking force during panic braking maneuvers upon the detection of a rapid brake pedal application. This report presents an evaluation of drivers' panic braking performance using BAS. Two vehicles with electronic BASs were selected: a 2006 Mercedes-Benz R350 and a 2007 Volvo S80. Sixty-four participants, balanced for age and gender, drove one of the instrumented vehicles at 45 mph and stopped at an unexpected barricade. Following debriefing, drivers performed another braking maneuver at the barricade, were shown how to perform a hard stop, and performed hard-braking maneuvers in which BAS was either enabled or disabled. Twenty-eight percent of drivers activated BAS subsequent to the demonstration. In the most conservative analysis, where the effect of BAS activation was isolated from driver panic-braking variability, it was found that BAS-active stopping distances were on average 1.43 ft (s.e. = 1.19 ft) shorter than BAS-disabled stopping distances. Yet, two drivers, who differed in age, sex, and vehicle driven, exhibited reductions in stopping distance exceeding 10 ft. Overall, the as-tested BAS has potential safety benefit that could be accrued from reduced stopping distance, but were not realized in this evaluation. Moreover, BAS implementations that do not completely rely on the driver may offer greater safety benefits.
- Predictive Eco-Cruise Control (ECC) System: Model Development, Modeling and Potential BenefitsRakha, Hesham A.; Ahn, Kyoungho; Park, Sangjun (United States. Department of Transportation. Research and Innovative Technology Administration, 2013-02-19)The research develops a reference model of a predictive eco-cruise control (ECC) system that intelligently modulates vehicle speed within a pre-set speed range to minimize vehicle fuel consumption levels using roadway topographic information. The study includes five basic tasks: (a) develop a vehicle powertrain model that can be easily implemented within eco-driving tools, (b) develop a simple fuel consumption model that computes instantaneous vehicle fuel consumption levels based on power exerted, (c) evaluate manual driving and conventional cruise control (CC) driving using field-collected data, (d) develop a predictive ECC system that uses the developed vehicle powertrain and fuel consumption models, and (e) evaluate the potential benefits of the proposed predictive ECC system on a pre-trip and fleet-aggregate basis. This study develops a predictive ECC system that can save fuel and reduce CO2 emissions using road topography information. The performance of the system is tested by simulating a vehicle trip on a section of Interstate 81 in the state of Virginia. The results demonstrate fuel savings of up to 15 percent with execution times within real time. The study found that the implementation of the predictive ECC system could help achieving better fuel economy and air quality.