DOCSLIB.ORG

Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report Pan He, Aotian Wu, Anand Rangarajan, Sanjay Ranka <[email protected], [email protected], [email protected], [email protected]> Contract: BDV31-977-81 Date: May 2019 Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report DISCLAIMER The work was supported in part by the Florida Department of Transportation. The opinions, findings and conclusions expressed in this publication are those of the author(s) and not necessarily those of the Florida Department of Transportation or the U.S. Department of Transportation. ii Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report METRIC CONVERSION TABLE SYMBOL WHEN YOU KNOW MULTIPLY BY TO FIND SYMBOL in inches 25.4 millimeters mm ft. feet 0.305 meters m yd. yards 0.914 meters m mi miles 1.61 kilometers km Table 1: U.S. UNITS TO METRIC (SI) UNITS SYMBOL WHEN YOU KNOW MULTIPLY BY TO FIND SYMBOL mm millimeters 0.039 inches in m meters 3.28 feet ft. m meters 1.09 yards yd. km kilometers 0.621 miles mi Table 2: METRIC (SI) UNITS TO U.S. UNITS iii Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report DOCUMENTATION PAGE Technical Report Documentation Page 1. Report No. 2. Government Accession No. 3. Recipient’s Catalog No. 4. Title and Subtitle 5. Report Date Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology May 2019 Final Report 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Pan He, Aotian Wu, Anand Rangarajan, Sanjay Ranka 9. Performing Organization Name and Address, 10. Work Unit No. (TRAIS) Computer and Information Science and Engineering, University of Florida 432 Newell Dr, Gainesville, FL 32611 11. Contract or Grant No. BDV31-977-81 12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered Florida Department of Transportation Final Report 605 Suwannee Street, MS 30 8/21/2017 - 7/22/2019 Tallahassee, FL 32399 14. Sponsoring Agency Code 15. Supplementary Notes 16. Abstract The primary objective of the present work was to develop video processing and machine learning methods to automatically detect and classify trucks traveling on Florida highways. The assembly of an automated system to detect, classify, and recognize various truck types from video and to derive their attributes is presented in this report. These extracted attributes were then used to determine commodity types, which can be used downstream for tracking commodity movements. To accomplish this, a set of high resolution videos (made available by the Florida Department of Transportation, FDOT) using freeway roadside passive cameras was utilized to create benchmark datasets. These videos were captured at different times of day, mainly at two freeway locations, and on various days during the past two years. The set of images derived from the videos was processed by our developed system to train and evaluate different approaches. The approaches drew upon recent work in deep convolutional neural networks for object detection and classification, semantic segmentation, and feature extraction, as well as drawing from traditional methods such as decision trees and geometric features (like edges and corners). We developed deep learning algorithms that leveraged transfer learning to determine whether an image frame has a truck and, if the answer is affirmative, localize the area from the image frame where the truck is most likely to be present. In particular, (1) we developed deep learning algorithms for detecting the location of a truck in a video frame followed by determining whether the image corresponds to a truck or a non-truck, (2) we developed a hybrid truck classification approach that integrates deep learning models and geometric truck features for classifying trucks into one of the nine FHWA classes (FHWA classes 5 through 13), (3) we developed algorithms for recognizing and classifying various truck attributes such as tractor type, trailer type, and refrigeration units that are useful in commodity prediction, and (4) we developed techniques for extracting vendor information corresponding to a truck, using logo and text detection. 17. Key Word 18. Distribution Statement Machine learning; Deep neural networks; Truck classification; Trailer classification; Logo detection; Commodity identification No restrictions. 19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price. Unclassified. Unclassified. 76 Form DOT F 1700.7 Reproduction of completed page authorized iv Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report ACKNOWLEDGMENTS The authors would like to thank the Florida Department of Transportation (FDOT) for provid- ing real truck video data and for insightful discussions during the course of the project. v Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report EXECUTIVE SUMMARY Freight analysis is an established approach in many states and major metropolitan areas. It is aimed at integrating various data sources, thus creating a comprehensive picture of freight movement. The main goal of the present work was to develop video processing and machine learning methods to automatically detect and classify trucks traveling on Florida highways. We used the Federal Highway Administration (FHWA) truck taxonomy for truck classification. Next, we automatically performed logo detection and recognition because this allowed for precise determination of commodity types in the detected truck. The assembly of an automated system for detection, classification, and recognition of var- ious truck types from video and deriving their attributes is addressed in this report. These extracted attributes were used to determine commodity types, which can be used down- stream for tracking commodity movements. To accomplish this, a set of high resolution videos (made available by FDOT) using freeway roadside passive cameras was utilized to create benchmark datasets. These videos were captured at different times of day, mainly at two freeway locations, and on various days during the past two years. The set of images derived from the videos was processed by our developed system to train and evaluate different approaches. The approaches drew upon recent work in deep neural networks for object detection and classification, semantic segmentation, feature extraction, as well as drawing from traditional methods such as decision trees and geometric features (like edges and corners). We developed deep learning algorithms that used transfer learning to determine whether an image frame has a truck and, if the answer is affirmative, localize the area from the image frame where the truck is most likely to be present. In particular, 1. We developed deep learning algorithms for detecting the location of a truck in a video frame followed by whether the image corresponds to a truck or a non-truck. 2. We developed a hybrid truck classification approach that integrates deep learning models and geometric truck features for classifying trucks into one of the nine FHWA classes (FHWA classes 5 through 13). 3. We developed algorithms for recognizing and classifying various truck attributes, such as tractor types, trailer types and refrigeration units, that are useful in commodity pre- vi Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report diction. 4. We developed techniques for extracting vendor information corresponding to a truck using logo and text detection. This consisted of two steps: determining the location of the text or logo and detecting company information using text- or logo-based identifi- cation. For enclosed trailer types (which correspond to a large fraction of trailer types), this is an effective way of determining the commodity. Results obtained from our datasets show that our scheme for truck classification has > 90% accuracy for classifying trucks into one of the nine classes and is relatively independent of the actual camera angle. Additionally, our algorithms for trailer detection achieved > 85% accuracy for classifying a tractor and trailer and a 95% accuracy for detecting a refrigerator unit. Additionally, we were able to demonstrate determination of vendor information using text- or logo-based detection and classification, which in turn is associated with commodities using the NAICS database. vii Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report CONTENTS Contents DISCLAIMER ii METRIC CONVERSION TABLE iii DOCUMENTATION PAGE iv ACKNOWLEDGMENTS v EXECUTIVE SUMMARY vi LIST OF FIGURES x LIST OF TABLES xiv 1 Introduction 1 2 Literature Review 3 3 Methodology 6 3.1 Overview . 6 3.2 Truck Model . 8 3.2.1 Truck Detection Component . 8 3.2.2 Truck Classification Component . 10 3.2.3 Results for Truck Classification . 19 3.2.4 Tractor and Trailer Classification Component . 21 3.2.5 Results for Tractor and Trailer Classification . 24 3.2.6 Refrigerator Unit Detection Component . 27 3.3 Commodity Classification . 28 3.3.1 Text-based Logo Detection and Recognition . 29 3.3.2 Image-based Logo Detection and Recognition . 32 4 Conclusions 44 5 References 47 Appendices 51 viii Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report CONTENTS A Machine Learning Terminology and Acronyms 51 B Visualization and Annotation Tool Development 55 C FHWA Vehicle Classification 56 D Refrigerator Unit Model 57 E Wheel Model 58 F FDOT Image Library 60 G Universal Logo Detector 61 ix Truck Taxonomy and Classification Using Video and Weigh-In Motion (WIM) Technology Final Report LIST OF FIGURES List of Figures 1 A pipeline of all the developed techniques. 6 2 The YOLO detector divides the image into an S × S grid and, using shared computation, passes the whole image through the model to obtain image fea- tures for each cell. Predictions are encoded as a 3D tensor of size S × S × (B × 5 + C).
Load more

Recommended publications
  • UAW Ford Agreements Cvr 1Up.Indd 2 11/15/16 7:07 AM SKILLED TRADES AGREEMENTS and LETTERS of UNDERSTANDING
    SKILLED TRADES AGREEMENTS AND LETTERS OF UNDERSTANDING between UAW® and the FORD MOTOR COMPANY Agreements Dated November 5, 2015 133 MICHIGAN (Effective November 23, 2015) ♲ printed on recycled paper PRINTED IN U.S.A. 64353-UAW Ford Skilled Trades Cvr 1up.indd 1 10/26/16 8:24 AM National Ford Department Staff 2015 Negotiations Jimmy Settles Vice President and Director UAW Ford, Aerospace, Chaplaincy and Insurance Greg Drudi Roy Escandon Angelique Peterson- Don Godfrey Jeffrey Faber Mayberry Brett Fox Ford Motor Company and the UAW recognize Darryl Nolen Gregory Poet Kenneth Gafa their respective responsibilities under federal Bob Tiseo Reggie Ransom and state laws relating to fair employment Phil Argento Michael Gammella Lorenzo Robinson practices. Tracy Ausen Raenell Glenn Michael Robison Carol Bagdady R. Brian Goff Nick Rutovic The Company and the Union recognize the Matthew Barnett Ruth Golden Angelo Sacino Monica Bass moral principles involved in the area of civil Jane Granger Les Shaw David Berry rights and have reaffirmed in their Collective Andre Green Michael Shoemaker Carlo Bishop Bargaining Agreement their commitment not Joe Gucciardo Casandra Shortridge Shawn Campbell to discriminate because of race, religion, color, Dan Huddleston Larry Shrader Jerry Carson age, sex, sexual orientation, union activity, Michael Joseph Garry Sommerville Alfonzo Cash Thomas Kanitz national origin, or against any employee with Jeffrey Terry Tiffany Coger Brandon Keatts disabilities. Kevin Tolbert Gerard Coiffard Michael Kerr Vaughan Tolliver Sean
    [Show full text]
  • A New Heavy-Duty Vehicle Visual Classification and Activity Estimation Method for Regional Mobile Source Emissions Modeling
    A NEW HEAVY-DUTY VEHICLE VISUAL CLASSIFICATION AND ACTIVITY ESTIMATION METHOD FOR REGIONAL MOBILE SOURCE EMISSIONS MODELING A Thesis Presented to The Academic Faculty by Seungju Yoon In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Civil and Environmental Engineering Georgia Institute of Technology August 2005 A NEW HEAVY-DUTY VEHICLE VISUAL CLASSIFICATION AND ACTIVITY ESTIMATION METHOD FOR REGIONAL MOBILE SOURCE EMISSIONS MODELING Approved: Dr. Michael O. Rodgers, Advisor Dr. Randall L. Guensler Dr. Michael D. Meyer Dr. Michael P. Hunter Dr. Jennifer H. Ogle July 15, 2005 ACKNOWLEDGEMENTS Many people sacrificed time and energy allowing me to complete this thesis. Thanks to all of you. Most of all, my wife Juhyun and my son Taehyuan deserve much credit for encouragement and patience throughout this process. Two other people deserve special acknowledgment in helping me finish my degree and dissertation. Drs. Michael O. Rodgers and Randall L. Guensler have allowed and encouraged me to finish this research and guided me to see the bigger and important issues. To family, advisors, and fellow students, you all assisted in large and small ways for which I will always be indebted. iii TABLE OF CONTENTS ACKNOWLEDGEMENTS............................................................................................... iii LIST OF TABLES........................................................................................................... viii LIST OF FIGURES .............................................................................................................x
    [Show full text]
  • Registration Manual Issued by Nebraska Game and Parks
    Rhonda Lahm, Julie Maaske, Director Deputy Director NEBRASKA COUNTY TREASURER’S MANUAL R E G I S T R A T I O N Provided by Nebraska Department of Motor Vehicles Driver and Vehicle Records Division 301 Centennial Mall South P.O. Box 94789 Lincoln, NE 68509-4789 www.dmv.nebraska.gov Help Desk (402) 471-3918 Toll Free (800) 972-6299 Fax Number (402) 471-8694 Betty Johnson, Administrator Deb Sabata Program Manager Cindy Incontro, Sandy Wood, Business Applications Support Administrative Assistant Technician Supervisor TABLE OF CONTENTS Chapter 1 - Definitions Definitions............................................................................................................ 1-1 Chapter 2 - Registrations Motor Vehicles Exempt from Registration .......................................................... 2-1 Registration - Vehicle .......................................................................................... 2-2 Registration - Boat ............................................................................................... 2-4 Proof of Financial Responsibility ........................................................................ 2-5 Nebraska Insurance Database .............................................................................. 2-9 Registration Renewal ......................................................................................... 2-11 Registration Procedure – Owner Retains Salvage ............................................. 2-13 Passenger...........................................................................................................
    [Show full text]
  • Manual on Classification of Motor Vehicle Traffic Crashes Eighth Edition
    AMERICAN NATIONAL STANDARD ANSI D.16-2017 8th Edition 2017 - Manual on Classification of 16 . Motor Vehicle Traffic Crashes ANSI D American National Standard ANSI D.16 – 2017 ANSI D.16 – 2017 American National Standard Manual on Classification of Motor Vehicle Traffic Crashes Eighth Edition Secretariat Association of Transportation Safety Information Professionals Prepared by the D.16-2017 Committee on Classification of Motor Vehicle Traffic Crashes under the direction of the Association of Transportation Safety Information Professionals Approved December 18, 2017 American National Standards Institute, Inc. American National Standard ANSI D.16 – 2017 AMERICAN NATIONAL STANDARD Approval of an American National Standard requires verification by ANSI that the requirements for due process, consensus, and other criteria for approval have been met by the standards developer. Consensus is established when, in the judgment of the ANSI Board of Standards Review, substantial agreement has been reached by directly and materially affected interests. Substantial agreement means much more than a simple majority, but not necessarily unanimity. Consensus requires that all views and objections be considered, and that a concerted effort be made toward their resolution. The use of American National Standards is completely voluntary; their existence does not in any respect preclude anyone, whether he has approved the standards or not, from manufacturing, marketing, purchasing or using products, processes, or procedures not conforming to the standards. The American National Standards Institute does not develop standards and will in no circumstance give an interpretation of any American National Standard. Moreover, no person shall have the right or authority to issue an interpretation of an American National Standard in the name of the American National Standards Institute.
    [Show full text]
  • Automobile Industry.Pdf
    Automobile Industry Hassan, Salman & Ahmad BA 630 - Spring 19 Introduction Truck Classification by the U.S. Department of Transportation’s GVWR and Vehicle Inventory Use Service (VIUS) Categories Class Weight Body Style Duty Class 1 6,000 lbs & less Minivan Cargo Van SUV Pickup truck Light Duty Full-Size Class 2 6,0001 to 10,000 lbs Van Cargo Van Step Van Pickup Heavy-duty Medium Class 3 10,001 to 14,000 lbs Walk-in Box Truck City Delivery Pickup Duty Source: energy.gov Producer's perspective Item Car Truck Frame Unibody Body On Frame Body small big Engine Light duty Heavy duty Channel of distribution Car dealers Truck dealers Manufacture More automation Less automation Customer’s perspective Price Economy luxury Sports cars Capacity Mid size Full size Pickup Truck Semi Truck Heavy Truck Med Truck Body Style Sedan Hatchback SUV Wagon Minivan Types of cars appropriate for passengers Geographic Scope Sales of passenger cars in selected countries Cars Imports by Country in 2018 worldwide from 2005 to 2017 (in million units) Country value worth of cars of total cars imports United States $178.5 billion 23.10% Germany $61.5 billion 8% China $49.6 billion 6.40% United Kingdom $43.9 billion 5.70% Car Exports by Country in 2018 Country value worth of cars of total cars exports Germany $154.7 billion 20.80% Japan $99.1 billion 13.30% United States $51.4 billion 6.90% Mexico $49.4 billion 6.60% Source: oica.net; worldstopexports.com Our Definition for The Industry Manufacturing passenger automobile or light duty trucks including transmission, engine, and bodies.
    [Show full text]
  • And Length-Based Vehicle Classi Cation, Federal Highway Administration – Pooled Fund Program [TPF-5(192)]
    Loop- and Length-Based Vehicle Classication, Federal Highway Administration – Pooled Fund Program [TPF-5(192)] Erik Minge, Primary Author SRF Consulting Group, Inc. Minneapolis, Minnesota November 2012 Research Project Final Report 2012-33 To request this document in an alternative format, please contact the Affirmative Action Office at 651-366-4723 or 1-800-657-3774 (Greater Minnesota); 711 or 1-800-627-3529 (Minnesota Relay). You may also send an e-mail to [email protected]. (Please request at least one week in advance). Technical Report Documentation Page 1. Report No. 2. 3. Recipients Accession No. MN/RC 2012-33 4. Title and Subtitle 5. Report Date Loop- and Length-Based Vehicle Classification, Federal November 2012 Highway Administration – Pooled Fund Program [TPF-5(192)] 6. 7. Author(s) 8. Performing Organization Report No. Erik Minge, Scott Petersen, Herbert Weinblatt, Benjamin Coifman, and Earl Hoekman 9. Performing Organization Name and Address 10. Project/Task/Work Unit No. SRF Consulting Group, Inc. One Carlson Parkway, Suite 150 11. Contract (C) or Grant (G) No. Minneapolis, MN 55416 (C) 95029 12. Sponsoring Organization Name and Address 13. Type of Report and Period Covered Minnesota Department of Transportation Final Report Research Services 14. Sponsoring Agency Code 395 John Ireland Boulevard, MS 330 St. Paul, MN 55155 15. Supplementary Notes http://www.lrrb.org/pdf/201233.pdf 16. Abstract (Limit: 250 words) While most vehicle classification currently conducted in the United States is axle-based, some applications could be supplemented or replaced by length-based data. Common length-based methods are more widespread and can be less expensive, including loop detectors and several types of non-loop sensors (both sidefire and in-road sensors).
    [Show full text]
  • 1 Bucket-Trucks and Buses 1. Introduction 2. E-Trucks
    Bucket-Trucks and Buses By John Benson January, 2019 1. Introduction It seemed like a no-brainer. Electric utilities have a huge number of trucks (at least my home electric utility, PG&E, does). Why don't they buy electric trucks, or perhaps range- extended hybrid trucks? So I searched, and discovered, (1) they could fairly easily do this, but (2) apparently they don't. Thus this paper started to take shape. Then I saw some really important information on electric buses. I covered these fairly thoroughly in the recent paper linked below, but this news came afterward, so I will cover the new information at the end of this paper. https://www.energycentral.com/c/cp/ev-update 2. E-Trucks The first electric trucks are just starting to roll to customers right now. In subsection 2.2 below we cover those manufacturers that make medium-duty trucks (suitable for utility trucks) that have started to deliver trucks or at least have orders. Immediately below, we lay some groundwork. 2.1. Truck Classifications Sometimes it's necessary to define terms before we can discuss a subject. Since we will be talking about trucks, the first step is to review the formal classification of these vehicles.1 Truck Duty classification Weight limit Examples class (GVWR)2 Class 1 Light truck 0–6,000 pounds Dodge Dakota, Chevrolet Colorado, Toyota Tacoma, Nissan Frontier, Class 2 Light truck 6,001–10,000 Ram 1500, Chevrolet Silverado 1500, Ford F-150 pounds Class 3 Medium truck 10,001–14,000 Ram 3500, GMC Sierra 3500, Ford F-350 pounds Class 4 Medium truck 14,001–16,000
    [Show full text]
  • Characteristics and Emissions of Heavy-Duty Vehicles in Tennessee Under the MOBILE6 Model
    Transportation Research Record 1842 I 99 Paper No. 03-3953 Characteristics and Emissions of Heavy-Duty Vehicles in Tennessee Under the MOBILE6 Model Terry L. Miller, Wayne T. Davis, Gregory D. Reed, Prakash Doraiswamy, and Joshua S. Fu Heavy-duty vehicle (HDV) classifications used for modeling emissions vehicles (HDGVs) and heavy-duty diesel vehicles (HDDVs). With in the MOBILE6 model have been expanded from 2 classifications in MOBILE6, the U.S. Environmental Protection Agency (EPA) has MOBILE5 to 16 classifications in MOBILE6. The new classifications are provided for much more sensitivity in modeling mobile-source emis- based on vehicle weight and fuel used (i.e., gasoline or diesel). The sions by expanding the number of vehicle types to 28—of which heavier vehicles have higher emissions, so it is important to use cor- 19 are HDV types. The HDVs include eight classes of HDGVs, eight rect vehicle weight distributions. Tennessee’s HDV registration data classes of HDDVs, and three classes of buses (gasoline and diesel). show a distribution very similar to the national defaults, but with Table 1 lists the class designations and definitions. more vehicles in the heaviest weight category (HDV8B). More than The bases for distinguishing different vehicles are the body type, 50% of Tennessee’s HDVs fall in the lightest vehicle category fuel used, and the gross vehicle weight rating (GVWR) based on the (HDV2B). The biggest difference in truck characteristics in Tennessee manufacturer’s specifications. GVWR is the maximum recommended versus national defaults in MOBILE6 is the higher HDV fraction on loaded-vehicle weight, including passengers and cargo.
    [Show full text]
  • Driver Shortage
    Driver Shortage Defining the Issue A critical concern raised by the American Trucking Association is the lack of qualified and certified tractor trailer drivers. Class 8 (heavy) tractor trailer drivers operate trucks with gross vehicle weight exceeding 26,000 pounds. They move freight along intercity routes, and can be away from home for a few days or for weeks at a time. This job requires endurance and many drivers have 11 hour shifts per day. Class 8 truck drivers are subject to drug testing and health screening to ensure safety. Most heavy truck drivers have a high school diploma and have attended professional truck driving school. A heavy truck driver must have a commercial driver’s license (CDL). A heavy truck driver must be 21 years old to apply for an interstate CDL. 18-21 years olds can hold a CDL for the purpose of intrastate trucking. In 2014 (according to Bureau of Transportation Statistics), median pay was $39,520 per year or approximately ($19.00/Hour). There were 1,797,700 heavy truck jobs in 2014. Outlook for heavy truck driving jobs from 2014-24 is estimated to grow by 5% (average growth). More than 98,800 drivers will be needed to fill the projected job openings over this period of time. Legislative Background Commercial truck classification is determined by the vehicle weight. The Department of Transportation’s Federal Highway Administration (FHWA) has established three weight classifications. Class 7-8 trucks are considered heavy duty. The Federal Motor Carrier Safety Administration (FMCSA) specifies the requirements for Commercial vehicle driver’s licenses in part 383.
    [Show full text]
  • Trucks and Heavy-Duty Vehicles Technical Requirements and Gaps for Lightweight and Propulsion Materials
    VEHICLE TECHNOLOGIES OFFICE WORKSHOP REPORT: Trucks and Heavy-Duty Vehicles Technical Requirements and Gaps for Lightweight and Propulsion Materials February 2013 FINAL REPORT This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. WORKSHOP REPORT: Trucks and Heavy-Duty Vehicles Technical Requirements and Gaps for Lightweight and Propulsion Materials EXECUTIVE SUMMARY The Vehicle Technologies Office (VTO) hosted a Lightweight and Propulsion Materials Workshop in March 2011 in Dearborn, Michigan. The Materials area of the Vehicle Technologies Office focuses on developing lightweight materials for structures and propulsion materials for more efficient powertrain systems. This meeting focused on gaining industry’s perspective on the out-year material requirements of trucks and heavy duty vehicles (HDVs) as well as current technology gaps that limit adoption of designs utilizing these lighter weight materials. The industry experts who participated in this workshop included original equipment manufacturers (OEMs) tier-one suppliers and materials suppliers to the light truck and HDV value chain.
    [Show full text]
  • Accurate Vehicle Classification Including Motorcycles Using Piezoelectric Sensors
    ECONOMIC ENHANCEMENT THROUGH INFRASTRUCTURE STEWARDSHIP ACCURATE VEHICLE CLASSIFICATION INCLUDING MOTORCYCLES USING PIEZOELECTRIC SENSORS HAZEM REFAI, PH.D. OTCREOS9.1-42-F Oklahoma Transportation Center Phone: 405.732.6580 2601 Liberty Parkway, Suite 110 Fax: 405.732.6586 Midwest City, Oklahoma 73110 www.oktc.org DISCLAIMER The contents of this report reflect the views of the authors, who are responsible for the facts and accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. i TECHNICAL REPORT DOCUMETATION PAGE 1. REPORT NO. 2. GOVERNMENT ACCESSION NO. 3. RECIPIENTS CATALOG NO. OTCREOS9.1-42-F 4. TITLE AND SUBTITLE 5. REPORT DATE Accurate Vehicle Classification Including Motorcycles using March 30, 2013 Piezoelectric Sensors. 6. PERFORMING ORGANIZATION CODE 7. AUTHOR(S) 8. PERFORMING ORGANIZATION REPORT Hazem Refai, Ph.D. 9. PERFORMING ORGANIZATION NAME AND ADDRESS 10. WORK UNIT NO. School of electrical and computer engineering University of Oklahoma-Tulsa 11. CONTRACT OR GRANT NO. 4502 E 41st St. Rm 4W139 DTRT06-G-0016 Tulsa, OK, 74135 12. SPONSORING AGENCY NAME AND ADDRESS 13. TYPE OF REPORT AND PERIOD COVERED Oklahoma Transportation Center Final (Fiscal) 201 ATRC Stillwater, OK 74078 July 2009 – March 2013 (Technical) 2601 Liberty Parkway, Suite 110 14. SPONSORING AGENCY CODE Midwest City, OK 73110 15. SUPPLEMENTARY NOTES University Transportation Center 16. ABSTRACT State and federal departments of transportation are charged with classifying vehicles and monitoring mileage traveled. Accurate data reporting enables suitable roadway design for safety and capacity.
    [Show full text]
  • WHEREAS, the President in Homeland
    ORDINANCE 581 AN ORDINANCE AMENDING CHAPTER 225, SECTION 225-8 (I), OF THE CODE OF THE TOWN OF ELSMERE CONCERNING THE OVERNIGHT PARKING OF COMMERCIAL OR CONSTRUCTION VEHICLES. Sponsored by: 1st District Councilman John Jaremchuk First and Second Reading: July 10, 2014 Council Meeting Results: Passed 7 In Favor, 0 Opposed Third and Final Reading: August 14, 2014 Council Meeting Results: Passed 6 In Favor, 0 Opposed with 1 Absent Ordinance 581 An ordinance amending Chapter 225, Section 225-8 (I), of the Code of the Town of Elsmere concerning the overnight parking of commercial or construction vehicles. Page 1 of 5 ORDINANCE 581 AN ORDINANCE AMENDING CHAPTER 225, SECTION 225-8 (I), OF THE CODE OF THE TOWN OF ELSMERE CONCERNING THE OVERNIGHT PARKING OF COMMERCIAL OR CONSTRUCTION VEHICLES. WHEREAS, the current code of the Town of Elsmere prohibits the parking of any vehicle for commercial advertising purposes from being parked in any district at any time; and WHEREAS, the commercial advertisement of a business or the services a business offers, is commonly displayed on vehicles ranging from small two door passenger vehicles to pick-up trucks and larger vehicles; and WHEREAS, many times employees who are on call or serve in a management position for their company or business are assigned to take these vehicles home; and WHEREAS, it is likely that these vehicles could be determined to be a vehicle for commercial advertising purposes and therefore a violation of the existing code; and WHEREAS, the Mayor and Council do not believe this was the legislative intent of the original legislation.
    [Show full text]