Black Box Data from Accident Vehicles Methods of Retrieval, Translation, and Interpretation William Rosenbluth Black Box Data from Accident Vehicles: Methods of Retrieval, Translation, and Interpretation William Rosenbluth ASTM Stock Number: MONO5 ASTM International 100 Barr Harbor Drive PO Box C700 West Conshohocken, PA 19428–2959 Printed in the U.S.A Library of Congress Cataloging-in-Publication Data Rosenbluth, William, 1939– Black box data from accident vehicles: methods of retrieval, translation, and interpretation/ William Rosenbluth p cm Includes bibliographical references “ASTM Stock Number: MONO5.” ISBN 978-0-8031-7003-2 Automotive event recorders Traffic accident investigation—Instruments Automobiles—Dynamics—Data processing Automobiles—Crash tests I Title TL272.54.R67 2009 629.28’26—dc22 2009021150 Copyright © 2009 ASTM International, West Conshohocken, PA All rights reserved This material may not be reproduced or copied, in whole or in part, in any printed, mechanical, electronic, film, or other distribution and storage media, without the written consent of the publisher Photocopy Rights Authorization to photocopy items for internal, personal, or educational classroom use of specific clients, is granted by ASTM International provided that the appropriate fee is paid to ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428–2959, Tel: 610–832–9634; online:http://www.astm.org/ copyright/ Printed in Newburyport, MA September, 2009 Dedication This book, as was my first, is dedicated to my wife, Jean Joy Rosenbluth Her continuous support, encouragement, and patience facilitated much of the work discussed herein She is the best thing that ever happened to me William Rosenbluth Reston, VA iv Foreword The objective of this publication is to build on the concepts presented in ASTM Monograph 41 by providing specific examples of the translation and interpretation of raw downloaded hexadecimal data into engineering units useful to the engineering investigator This will include illustrations of specific data interpretation and scaling constructs and examples of specific spreadsheet formulations to import and translate those data into useful engineering units Before proceeding with specific and detailed examples, and for those not familiar with ASTM Monograph 4, the broad concepts used in the field are discussed in Chapter Those well versed in the broad concepts can proceed directly to a more detailed discussion of geometric conventions and crash data nomenclature covered in Chapter Lastly, those well versed in concepts, crash event geometric conventions, and crash event data nomenclature can proceed directly to the data examples in Chapter In order to enhance chapter independence and immediate clarity, certain acronyms may be repeatedly defined in succeeding chapters This is to allow each chapter to be independently understood The principles and methods discussed in Chapters and are good engineering science, but they are only of academic value unless they can be applied for a business purpose Analysis and improvement of system designs is one such purpose Another such purpose is to conduct analysis for purposes of illuminating engineering issues in litigation In that context, the investigator is often tested as to his/her methods and their reliability, repeatability, usage by industry peers, and error rates Chapter presents a discussion of some considerations regarding those tests and methods to assure that one can pass those tests The reader should note that many different data retrieval and analysis situations may occur, and that, while this work is designed to present a representative set of such situations, it cannot cover every possible situation Investigation and Interpretation of Black Box Data in Automobiles: A Guide to the Concepts and Formats of Computer Data in Vehicle Safety and Control Systems, jointly published by the ASTM International, West Conshohocken, PA, and the Society of Automotive Engineers 共SAE兲, June 2001 v Acknowledgments The author wishes to acknowledge and thank the following people whose interest, participation and contributions unquestionably enhanced the content and quality of this book: Karen Bosch, Bosch Automotive Consulting, Inc., Phoenix, AZ, for her review of many preliminary drafts, for her implementation of many of the spreadsheet examples discussed here, and for her contributions to the practical illustration of these data Fred H Chandler, Jr., Chandler & Sons Automotive, Sterling, VA, for his skilled participation in many of the tests described herein, for his assistance with the design and testing of special test equipment for many of the tests herein, for his exhaustive searching and obtaining the exemplars in many of the examples herein, for the use of his inspection facility and for the use of his extensive automotive electronics commercial scanner and test tool resources Dr Eugen I Muehldorf, TRW, retired, Potomac, MD, for his critical review of the physics, mathematics and concepts referenced herein Gerald Rosenbluth, Automotive Consulting Services, Inc., Tempe, AZ, for the use of his extensive library of specifications and service data, and for his professional inspection facilities, used to conduct some of the tests documented herein Leon Russell, Esq., Russell & Shiver, Dallas, TX, for his support and encouragement of certain extended retrieval and analysis methods which made new achievements possible Mark Shattuck, Kinetic Engineering, Woodside, CA for his critical and detailed review of the content and reference integrity in the final draft Contents Chapter 1—Introduction Chapter 2—A Review of the Nomenclature, Mathematical and Geometric Conventions Used in Describing Crash Event Data 2.1 2.2 2.3 2.4 2.5 Geometric Conventions for Crash Event Pulses Deployment Metrics as Illustrated for Frontal Air Bag Systems Electronic Data in Air Bag ECUs The Steps to Achieve Engineering Units from Raw Data Inside an ECU/EDR Deriving a Scaling and Transfer Relationship from the Data Chapter 3—Examples and Exercises in Data Translation 3.1 3.2 2 11 Example 1: Creating a Linear Data List from Hex Data Blocks Inductively Deriving a SLOT Factor from Component Specifications, Translating Acceleration Data, and Interpreting that Data into Cumulative Velocity Loss „Delta V… Deductively Identifying EEPROM Parameters from Published Data and Deductively Deriving the Required SLOT Factors that Translate Raw EEPROM Data into Cumulative Velocity Loss „Delta V… Data Import Methods, Mapping Templates, and Introduction to Data Mining Multibyte and Bitmap Translation as Applied to Diagnostic Trouble Codes „DTCs… Analysis of EDR Accelerometer Running Data to Determine Acceleration Impingement Vectors Analysis of ABS Wheel Speed Sensor Running Data to Determine Detected Pulse Rate Consistency Using Hardcopy Crash Test Data to Characterize Specific EDR Performance versus EDR Family Performance 12 Chapter 4—EDR Data Considerations with/respect/to Evidentiary Reliability and Interrogation Procedures 90 3.3 3.4 3.5 3.6 3.7 3.8 4.1 4.2 4.3 4.4 The Role of Event Data and Data Translations Veracity Requisites for EDR Data Retrieval Rules for the Application of EDR Enginering Data in Litigation Protocols, Procedures, and Practices used in Interrogating EDR Data where Proprietary Data and/or Retrieval Methods are Involved 4.5 An Example Protocol for Interrogating EDR Data where Proprietary Data and/or Retrieval Methods are Involved 4.6 An Example of Error Rate Calculations to Satisfy Litigation Tests Subject Index 19 25 42 55 62 68 83 91 91 92 93 94 95 99 MON05-EB/Sep 2009 Introduction IN THE LAST FIVE YEARS IT HAS BECOME INCREASingly common knowledge that many automobiles and heavy trucks incorporate devices which act as data recorders, triggered by the occurrence of an abnormal driving event, such as hard braking or a deceleration event greater than what can be physically experienced in normal road operations.1 A device that can this is known generically as an event data recorder 共EDR兲 EDRs can exist as a stand alone function, independently accumulating data from dedicated sensors, or they can be more global, accumulating data from parameters broadcast on a vehicle network 共data bus兲 Most commonly understood passenger EDRs for passenger vehicles are incorporated within an air bag electronic controller or an engine electronic controller, whereas most commonly understood EDRs for heavy trucks are incorporated solely within an engine controller EDR-like subfunctions can be incorporated into the operation of other system devices such as antilock braking controllers, stability controllers, etc Modern vehicles can often have more than one device functioning as an EDR 共e.g., restraint controller and engine controller兲 EDR data, normally thought of as crash event related, can also be associated with the diagnostic trouble code 共DTC兲 freeze frames.2 This is especially true with heavy truck data Thus, the concept of vehicle black box data is actually an umbrella term, which implies using data components that may be obtained by interrogating several different system units, but which can be assembled as an ensemble to provide a set of electronically saved data useful to the accident investigator The feasibility of having EDR data is made possible by the incorporation of nonvolatile electronic memory in the vehicle functional control device A computer control device is generically known as an electronic control unit 共ECU兲 ECUs are used in modern vehicles to perform logical and parametric control of various vehicle functions and systems, and they have proven to be much more cost effective than traditional mechanical or electromechanical techniques The nonvolatile electronic memory within most ECUs retains its data even when the battery is disconnected.3 This is functionally similar to contemporary digital photographic storage, which uses nonvolatile memory to accomplish that identical function Nonvolatile data storage can be accomplished using flash memory or by using EEPROM.4 Nonvolatile memory data are universally retrieved from any particular ECU via a serial data interface Serial data interfaces can have many formats, data rates, and security keys, so that accessing those data can be a very sophisticated exercise which often requires a small interrogating computer to accomplish the data access Although some EDR data can be retrieved with an electronic device called a scanner,5 most require a microprocessor/data interface Notwithstanding what retrieval device is used, both operate in much the same way as a credit card terminal is used to query a central data bank to authorize a credit purchase, with a series of “handshakes” to validate the data inquiry and response Since most vehicle ECUs are already communicating on one or more intravehicle data networks, data retreival from most ECUs is often accomplished using a retrieval device connected to the appropriate vehicle network These network interfaces generally have standardized connection ports, SAE J1962 and SAE J1587 being the most common Contemporary ECU function is generally accomplished by a central integrated circuit combining an arithmetic core and several peripheral functions 共ALU, RAM, ROM, EEPROM, Comm, etc.6兲 Such a combined device is typically referred to as a microcontroller unit 共MCU兲 or microprocessor unit 共MPU兲 Some MCUs/MPUs contain integrated EEPROM and some operate with EEPROM/flash memory in an integrated circuit device external to the MCU Notwithstanding where it resides, access to those nonvolatile data is generally accomplished via a serial 共network兲 data port on the ECU/EDR That data port is where the retrieval scanner/computer retrieves its data.7 Normal road operations, acceleration, and braking 共deceleration兲 have a theoretical limit of 1g 共where 1g is that acceleration of gravity兲 Typical vehicle maximum acceleration values vary between 0.4 and 0.6g, and typical vehicle maximum deceleration values vary between 0.6 and 0.8g Typical thresholds are 0.65– 0.75g deceleration for hard braking and 1.5– 2.5g deceleration for a potential crash event 共calculation start threshold, “algorithm wakeup”兲 DTCs are also called error codes DTC freeze frame data are data parameters associated with and existing at the time of the confirmation of a DTC condition This can include calibration data, adaptive learning and optimization data, and, of course, crash-event-related data EEPROM= electrically erasable programmable read only memory EEPROM is fabricated using a special semiconductor construction that allows it to retain previously stored data even when the battery is disconnected A similarly functioning technology, flash memory, is also used for this purpose Flash memory is now commonly used in portable data storage devices, the most common of which are digital camera removable memory cards Scanner= small hand held microprocessor capable of sending serial data commands to a vehicle ECU and then receiving and selectively recording/interpreting ECU serial response data ALU= arithmetic logical unit; RAM= random access memory 共data not saved when power is removed兲; and ROM= read only memory 共data permanently frozen in that memory, data not changed after initial write兲 In some cases, that serial data port is not wired into the vehicle wiring harness and must be accessed via a special diagnostic connector Copyright © 2009 by ASTM International www.astm.org CHAPTER 䊏 EXAMPLES AND EXERCISES IN DATA TRANSLATION 87 Fig 3.8.5.1—Evaluation of cumulative velocity loss curves for nine OEM must-deploy crash tests versus the Subject Vehicle cumulative velocity loss curves 共derived via CDR兲 in order to determine whether Delta V in the subject vehicle was above or below the performance of the crash test exemplar must-deploy thresholds Fig 3.8.5.2—Table of key test crash data Note that the TTF data allows a quantitative determination of the Delta V at deploy command, and this can be compared to the Delta V for the unit of interest 共subject unit兲 as discussed above 88 BLACK BOX DATA FROM ACCIDENT VEHICLES 䊏 Fig 3.8.5.3—Evaluation of cumulative velocity loss curves for nine OEM must-deploy crash tests versus the Subject Vehicle cumulative velocity loss curves 共derived via CDR兲 in order to determine whether Delta V in the subject vehicle was above or below the performance of the crash test exemplar must-deploy thresholds Note that the subject vehicle was a non deploy case, and that several comparative crash test vehicles showed deployments at equal or lower time magnitudes than the subject vehicle An example of this is shown in Fig 3.8.4 The first step in this process is to interrogate an exemplar EDR with a CDR and observe the hard copy acceleration curve and table-list acceleration values produced with the CDR report The second step is to scan and capture the derived integrated Delta V data from the hard copy report The third step is to integrate the table-list acceleration values and independently derive the integrated Delta V based on the CDR acceleration record The fourth step is to digitize the hard copy acceleration curve in the manner identified above and integrate that data to produce acceleration values and a Delta V curve using our digitization method Figure 3.8.4 shows an overlay of the Delta V curves produced by all three methods This verifies the accuracy, repeatability, and reproducibility of the digitization methodology 3.8.5 Derived Metrics and Data Interpretation for Evaluating a Subject System After the digitization methodology has been validated, we can now proceed with confidence to develop metrics from crash test hard copies and apply those results to the subject accident Additional metrics can be derived from the crash test data to comparatively evaluate air bag performance in the subject vehicle with respect to OEM specifications Figure 3.8.5.1 shows a 115-ms duration graphical overlay of Delta V data from nine must-deploy crash tests, for a similar vehicle, produced by the vehicle manufacturer Overlayed on those traces is the Delta V trace for the vehicle under analysis Figure 3.8.5.2 shows tabulation of data for additional metrics derived from the nine crash tests for evaluation of CHAPTER 䊏 EXAMPLES AND EXERCISES IN DATA TRANSLATION the subject vehicle These metrics include time to fire 共TTF兲 for the test crashes Figure 3.8.5.3 shows a 50-ms duration graphical interpretation of Delta V data from the same nine must-deploy crash tests, for a similar vehicle, produced by the vehicle manufacturer Overlayed on those traces is the Delta V trace for the vehicle under analysis Now observing the TTF timing range for the test crashes and observing the cumulative Delta V magnitudes of the test crashes versus the vehicle under analysis, we can quantitatively compare the response of the vehicle under analysis to the test crashes provided This kind of comparison could only be effectively defended when the accuracy, repeatability, and reliability of 89 the digitization methodology is first proven as it was here The following are Excel® equivalents for the QuatroPro® formulations shown in the discussion above Figure ref 3.8.3.1 3.8.3.1 3.8.3.1 3.8.3.1 3.8.3.1 Cell ref B10 I19 J19 K19 L19 Quattro Pro® ‘+G19+ ‘+H19 * $C$7 ‘+I19 * $C$11 ‘+K18+ J19 ‘+K19 * $C$9 / $C$10 Excel® ‘=G19+ ‘=H19 * C7 ‘=I19 * C11 ‘=K18+ J19 ‘=K19 * C9 / C10 MON05-EB/Sep 2009 EDR Data Considerations with/respect/to Evidentiary Reliability and Interrogation Procedures THE PRINCIPLES AND METHODS DISCUSSED IN Chapters and are good engineering science, but they are only of academic value unless they can be applied for a business purpose Analysis and improvement of system designs is one such purpose Another purpose may be to conduct a safety systems performance analysis for purposes of litigation issues A third purpose may be to document vehicle conditions at the time of a an AE1 event 共such as when the vehicle under examination has struck another vehicle2兲 There are several references to the parameter content of passenger vehicle EDRs and the reliability of such data and the availability of such data NHTSA has published a Final Rule response to various petitions 关4.1兴 This reference gives a global overview of EDR content for those manufacturers who choose to include EDRs on their vehicles Additionally, a discussion of EDR policies by manufacturer is found in Kowalick, Fatal Exit, The Automotive Black Box Debate 关4.2兴 With respect to EDR data retrieval, a list of the vehicles covered by the most common EDR data retrieval tool, the Bosch CDR® is available at http://www.cdr-system com/coverage/index.html 关4.3兴 Vehicles included in CDR coverage are most post-1990 G.M 共and G.M S.I.R based兲 vehicles, selected post-2000 Ford vehicles and selected post-2004 Chrysler vehicles However, certain additional vehicles 共i.e., non-CDR-covered vehicles兲 also have EDR data content Some references to vehicles with and without EDR information are shown below As identified in Auto Week 关4.4兴, Nissan has disclosed the inclusion of a Vehicle Status Data Recorder 共VSDR兲 on at least one of its latest higher end models Certain Volvo vehicles contain EDR information including, seatbelt buckle status, accelerator and/or the brake status, vehicle speed, and steering status However, to access Volvo EDR information, special 共proprietary兲 equipment must be used in a direct harness connector umbilical mode No commercial equipment is known to access Volvo EDR modules 关4.5兴 The Toyota EDR data content plan is listed in an official web site release which says that EDR data were first included in the 2001 model year 关4.6兴 Examples of Toyota EDR data for 2004 Camry, Solara, and Sienna vehicles are given in Evaluation of Event Data Recorders in Full Systems Crash Tests, Niehoff et al 关4.7兴 Examples of Toyota EDR data for 2004 Camry, 2003 Lexus ES300, and 2005 Corolla vehicles have been achieved by ASA 关4.8兴 Examples of Hyundai EDR data for the 2004 Elantra have been achieved by ASA 关4.9兴 Ford EDR data retrieval strategy is documented in Current Ford Event Data Recorders, Wheelock, Robert J 共Bob兲 关4.10兴 However, this does not include 2002–2005 Ford Explorer EDR data 10 Examples of 2002-2005 Ford Explorer EDR data 共nonCDR-covered vehicles兲 have been achieved by ASA 关4.11兴 11 A discussion of the pitfalls and considerations involved with acquiring and using EDR data is contained in a Trial Magazine article, How to Challenge Black Box Data, Van Gaasbeck 关4.12兴 and in certain correspondence, L Russell to S Van Gaasbeck 关4.13兴 12 A wide ranging discussion of the use of EDR data for highway crash data analysis is given in a 2004 report entitled Use of Event Data Recorder 共EDR兲 Technology for Highway Crash Data Analysis, Gabler et al 关4.14兴 This report also identifies the data content of then-current G.M and Ford EDR devices Whatever the purpose of the investigation, in most investigations, and especially in litigation issues, the investigator is often tested as to his/her methods and their reliability, repeatability, error rates, and usage/acceptance by industry peers Below is a discussion of some considerations regarding those tests and methods to assure that one can pass those tests AE is an acronym 共Algorithm Enable兲 which is variously called algorithm wakeup or G-trigger An “event” does not necessarily mean that the airbags deployed It means that the vehicle experienced a velocity change causing an acceleration so that the on-board EDR deployment algorithm calculation process was enabled 共AE兲 and thus recorded certain vehicle operating parameters For instance, in a rollover event, one may wish to know vehicle speed, brake status, throttle status, etc In other cases, even with non-deploy events, certain PCM event recorders keep a continuous multi-parameter record in a circular buffer—which must be read in a special fashion to avoid overwriting In such cases, the striking vehicle is often called the bullet vehicle and the impacted vehicle is often called the target vehicle 90 Copyright © 2009 by ASTM International www.astm.org CHAPTER Fig 4.2.1—Load boxes from two domestic manufacturers as used to interrogate EDRs in a forensically neutral bench top mode 4.1 The Role of Event Data and Data Translations Event data and data translations can be an important part of system development and functional performance investigations In such a role these data can be used to: Identify safety systems performance for a given crash event 共deploy timing, deploy/nondeploy decisions, seatbelt usage, occupant position, etc.兲 Identify vehicle parameters existing at a crash event 共speed, brake status, accelerator status, engine status, steer angle, trigger data, etc.兲 Identify EDR parameters with respect to accident stations 关for multistation accident events兴 共pre-event data, postevent data, trigger data, etc.兲 Identify pre-existing conditions for a given accident event 共DTCs, DPIDs, etc.兲 Confirm/corroborate other-expert analyses 共reconstruction, biomechanics, occupant kinematics, etc.兲 4.2 Veracity Requisites for EDR Data Retrieval A key requisite for the use and value of event data and data translations is that such data accurately represent the actual 䊏 EDR DATA CONSIDERATIONS 91 conditions at the time it was recorded Thus if a later interrogation process alters or erases data it is unacceptable, or if the data are shown to be inaccurate or corrupted, it is not generally usable In the litigation realm, a forensically neutral data retrieval process incorporates a method and a protocol to retrieve EDR data with the highest assurance of not changing or disturbing that data, either by erasure or overwriting With an in-vehicle installed EDR, a forensically neutral interrogation process is generally achieved by interrogating an ECU connected to its normal vehicle circuits With an EDR out of its installed vehicle condition, a forensically neutral interrogation can be accomplished with a load box/interface/test-bed containing proper electrical loads and interfaces, or by using an exemplar vehicle as a test bed or via a direct EEPROM read Generally, to be forensically neutral, an out of vehicle data retrieval fixture 共load box or data interface兲 must include provisions for actuator 共squib, solenoid, etc.兲 dummy loads, sensor detection loads, MIL loads,3 serial feedback or for seatbelt switch status, so that any ECU undergoing such test-via-umbilical-interrogation will see only a correct operating environment during power-on and continuous loop checks 共just as it would in a vehicle兲.4 In certain cases, where some partial data may be changed by the power on and/or interrogation process, such data changes should be identified before the interrogation process starts Certain data changes may be acceptable if they not change the data of interest, and all parties agree to that in advance.5 ASTM E2493-07, “Standard Guide for the Collection of Non-Volatile Memory Data in Evidentiary Vehicle Electronic Control Units” is a summary of such a procedure 关4.15兴 Several examples help illustrate the point Figure 4.2.1 shows load boxes from two domestic manufacturers as used to interrogate EDRs in a forensically neutral bench top mode Figure 4.2.2 shows a nonmanufacturer test fixture as used to interrogate an EDR in a forensically neutral bench top mode Figure 4.2.3 shows a time sequence of operation where the MIL is ON for power on bulb test and then OFF during continuous run operations 共including interrogation procedures兲 Since this is normal vehicle operation, a demonstration such as this, with appropriate other ECUs having known DTCs, can be used to confirm the veracity of a proper forensically neutral test fixture Figure 4.2.4.a shows two parameters as reported by the EDR under test to a commercial scanner 共Driver and Passen3 MIL= Malfunction Indicator Lamp, a generic term for visual system failure indicator Specific MIL’s can be called “Airbag Lamp,” “Engine Lamp,” “ABS Lamp,” etc The situation can get yet more complicated when modern anti-theft measures are incorporated into the ECU In such cases, the ECU non volatile data save all or part of the VIN, and specific manufacturer security procedures must be considered Certain data changes are unavoidable, e.g., incrementing an ignition cycle counter because of power on Certain data changes may be avoidable only at great effort or expense, e.g., providing a data bus driven MIL simulator So, if the MIL condition is not of interest, and the MIL error condition is identified prior to interrogation, such an interrogation generated error may be acceptable 92 BLACK BOX DATA FROM ACCIDENT VEHICLES 䊏 A converse example showing forensic non-neutrality is shown in multipart Fig 4.2.5 In these examples, a commonly used commercial interrogating tool 共CDR®兲 is not forensically neutral when used in a direct umbilical mode to interrogate the SRS ECU 共i.e., a direct connection to the SRS ECU兲 In that mode, certain external fault codes will be added 共or redetected兲 because there is no provision for proper dummy-load resistors in the tool cabling Depending on the data lock status of an event record, this may be an important consideration 4.3 Rules for the Application of EDR Enginering Data in Litigation Fig 4.2.2—A non-manufacturer test fixture as used to interrogate an EDR in a forensically neutral bench top mode ger Squib Resistances兲 and Fig 4.2.4.b shows the two 2.0 ⍀ resistors used to simulate the Driver and Passenger Squib Resistances.6 Resistor values are read using guides such as Resistor Color Codes 关4.16兴 Thus, since the EDR is reporting the values to be 2.0 ⍀ 共2.0 ⍀, Fig 4.2.4.a兲, and the load resistor values can be visually determined to be ⍀ ± % 共Fig 4.2.4.b兲, we know in this case that the EDR under test is correctly reporting electrical reality A demonstration such as this, with appropriate variations or multiple calibration points, can be used to confirm the reliability of a proper forensically neutral test fixture Note that although the figures in this book are monochrome, the reader is assured that the four color bands from the end toward the middle 共right to left in this case兲 are RED, BLACK, GOLD, GOLD These are interpreted as: RED 共1st digit, value= 2兲, BLACK 共2nd digit, value= 0兲, GOLD 共3rd digit, decimal multiplier, # 0s, except when a tolerance color 共e.g., gold兲, meaning preceding values are divided by 10兲, GOLD 共4th digit, tolerance, gold= %, silver= 10 %兲 Thus the value for the two resistors marked with RED, BLACK, GOLD, GOLD bands, is 2.0 ohms 共2.0 ⍀兲 with a % tolerance In litigation, an engineering opinion or conclusion, stated with reasonable engineering certainty, must meet certain clear tests These tests are summarized below, but a more complete discussion of these tests, and various judicial perceptions and interpretations are found in Refs 关4.17–4.21兴 It must be founded on an underlying methodology of scientific tests, and documentation of test results 共charts, graphs, etc.兲, which are reliable, as shown by publication of test methods and/or other by peer acceptance of test methods It must be repeatable by any peer scientist/engineer using the conditions and/or data recorded in the subject tests That is, allow other investigators to repeat the subject tests on the same ECU and derive the same test results It must have a reasonable error rate and that error rate must be accepted by other practitioners in that field of engineering testing and inquiry It must be consistent with industry or commonly accepted standards that define or direct the tests and results postulated It must pass a test of relevance and linkage to the facts at issue in the subject litigation Fig 4.2.3—A time sequence of operation for the non-manufacturer test fixture where the MIL is ON for power on bulb test and then OFF during continuous run operations 共including interrogation procedures兲 CHAPTER 䊏 EDR DATA CONSIDERATIONS 93 4.4 Protocols, Procedures, and Practices used in Interrogating EDR Data where Proprietary Data and/or Retrieval Methods are Involved Fig 4.2.4—Two parameters as reported by the EDR under test to a commercial scanner 共Driver and Passenger Squib Resistances兲 The two 2.0 ohm resistors used to simulate the Driver and Passenger squib resistances for the readings shown in Figure ??? Note that although the figures in this book are monochrome, the reader is assured that the four color bands from the end towards the middle 共right to left in this case兲 are RED, BLACK, GOLD, GOLD These are interpreted as: RED 共1st digit, value= 2兲, BLACK 共2nd digit, value= 0兲, GOLD 共3rd digit, decimal multiplier, # 0s, except when a tolerance color 兵e.g., gold其, meaning preceding values are divided by 10兲, GOLD 共4th digit, tolerance, gold= %, silver = 10 %兲 Thus the value for the two resistors marked with RED, BLACK, GOLD, GOLD bands, is 2.0 ohms 共2.0 Ω兲 with a % tolerance Where several parties have an interest in the data retrieved from an EDR, it is generally a wise idea to document the steps and equipment to be used for data retrieval in a written protocol for that data retrieval Such a protocol generally specifies the test bed, the interrogation hardware and software, and the distribution of the retrieved data Examples of the terms to be included is this type of protocol include: A description of the test bed/load box 共if not in the original vehicle兲 A description of the interrogation hardware and software to be used to interrogate the ECU and retrieve the data A demonstration of the test bed/load box and the interrogation hardware/software as used on a known exemplar ECU.7 This presubject-test proof of the data interrogation process is often called a baseline because it shows the operation of the hardware and software in a known normal and forensically neutral mode Identification of the data to be shared as a result of the data retrieval Such data can include: 共1兲 Photodocumentation of data on scan tools or laptops 共2兲 Hardcopies of raw data 共3兲 Computer files of such data 共4兲 Video documentation of data collection Identification of the translations/interpretations of the retrieved data Considerations here can include: 共1兲 Raw data 共hexadecimal formatted data兲 共2兲 Data translations/interpretations created by commercial tools An exemplar is an example ECU having the same design and functional response as the subject ECU Fig 4.2.5—In these examples, a commonly used commercial interrogating tool 共Vetronix CDR®兲 is not forensically neutral when used in a direct umbilical mode to interrogate the SRS ECU 共i.e., a direct connection to the SRS ECU兲 In that mode, certain external fault codes will be added 共or re-detected兲 because there is no provision for proper dummy-load resistors in the tool cabling 94 BLACK BOX DATA FROM ACCIDENT VEHICLES 共3兲 Data translations/interpretations created by proprietary tools 共4兲 Data files versus image files 共5兲 The time of data sharing 共i.e at the retrieval or later兲 共6兲 The media on which data may be shared 共hardcopy, data files on floppy disk, data files on USB mass storage device, data files on CDROM, etc.兲 共7兲 Data may be shared, by prior agreement, when the translations/interpretations become available 共8兲 In general, the methods and intelligence to derive the data 共e.g., translation source code兲 are considered to be confidential, protected, or trade secret data Thus, while the work product 共data charts and/or tables in common engineering units兲 may be shared, the methods and intelligence used to derive that work product 共e.g., translation source code兲 are generally not shared 4.5 An Example Protocol for Interrogating EDR Data where Proprietary Data and/or Retrieval Methods are Involved There can be many ways to reduce the above considerations to a working practice Below is an example of a protocol for EDR data retrieval using a proprietary data interface and software and using a forensically neutral test bed The protocol below includes considerations for serial data access via the EDR harness connector and considerations for data access via a direct EEPROM umbilical 共which may be accomplished via a serial or parallel protocol兲 EXAMPLE PROTOCOL A written protocol is established, distributed, and agreed to by the parties interested in the litigationrelated data retrieval Data to be shared at the data retrieval event are identified and agreed to such data may include: 共1兲 Raw interrogation data log with commands and responses 共2兲 Parsed hexadecimal data 共data stripped of commands and responses but not necessarily formatted兲 共3兲 Formatted hexadecimal data 共data in the formatted with address headers so that the contents of individual data cells can be identified by eye兲 共4兲 Calculated equivalents of the hexadecimal data 共not necessarily identified per engineering units兲 共5兲 Translated data in identified engineering units 共per SLOT factors兲 The involved parties should reach prior agreement regarding what data is to be provided at the time of the data collection, and what data is to be provided at a later time The test conductor will perform two test series, with two devices under test 共DUT兲 The first series should involve an exemplar device 共to provide a baseline verification of the test fixture兲 and the second series should involve the subject ECU The subject ECU may have been removed or may still be in the subject vehicle An exception to these dual DUT procedures would be the interrogation of on-vehicle ECUs, through the SAE J1962 port 共or Manufacturer diagnostic port兲 with all systems connected in the subject vehicle, and using standard 䊏 OEM or aftermarket scanners 共e.g., Tech-II, Mastertech, NGS, Consult, DRB-II, MT2500, CS2000, CDR, etc.兲 In either situation 共on-vehicle, off-vehicle, benchtop serial data via standard harness connector, or direct EEPROM read兲, where an nonstandard scanner is to be used 共e.g., proprietary investigator interface or proprietary mfr/supplier interface兲, an exemplar DUT 共ECU兲 will be first interrogated in a test fixture with the best achievable forensic neutrality 共benchtop fixture or exemplar vehicle, as agreed by the parties兲 using the same method and protocol as intended for the subject DUT This is done to prove the integrity of the test fixture, retrieval, and communication protocol and interrogation system In all cases the participating investigators shall agree on the feasibility of 共damaged兲 subject vehicle operational risk before electrical connections are made and/or power is applied Power supply through a fused jumper may be suggested Given the joint decision to proceed, the following procedures apply: Power off test fixture 共or subject/exemplar vehicle as test fixture兲 Select DUT Install DUT 10.1 For standard harness-connector interrogations using a forensically neutral test fixture, power on test fixture 共or subject/exemplar vehicle as test fixture兲 with DUT installed Observe MIL codes and other-indicator status Visually record 10.2 For direct-EEPROM data retrievals, connect device access probe head as appropriate 11.1 For standard harness-connector interrogations using a forensically neutral test fixture, as appropriate, interrogate DUT with standard scan tool to record scanner data 共service DTCs & service PIDs兲 Record serialized copy to master notes 11.2 For standard harness-connector interrogations using a forensically neutral test fixture, interrogate DUT with commercial or proprietary serial interrogation tool to retrieve extended DTC/PID information and/or nonvolatile memory information 共RAM, ROM, EAROM, EEPROM, etc.兲 Save data to file 11.3 For direct-EEPROM data retrievals, retrieve nonvolatile memory information 共RAM, ROM, EAROM, EEPROM, etc.兲 Save data to file 12 For standard harness-connector interrogations using a forensically neutral test fixture, reinterrogate with standard scan tool to record scanner data 共service DTCs & service PIDs兲 Record serialized copy to master notes 13 If no exceptions, power down, and remove DUT 14 If not in as-saved data, create hex table, and distribute to all parties attending 共serialized copy to master notes兲 15 All parties review 16 Select next DUT as applicable and repeat steps 5–15 共For subject DUT, repeat DTC/PID & EEPROM retrieval procedure twice.兲 17 End test operations 18 At the end of the tests, all parts are to be secured and repacked as received Subject DUTs, if removed, are to be packed in sealed signature-confirmed wrapping CHAPTER 䊏 EDR DATA CONSIDERATIONS 95 Fig 4.5.1—Showing a data retrieval process using a proprietary data interface and software, used with a combination of two forensically neutral test beds 关a benchtop fixture for an exemplar ECU and an accident vehicle with the subject ECU in situ兴 This process satisfied the requirements of ASTM E2493-07 An example of a process that followed the above protocol is shown in Figs 4.5.1 and 4.5.2 Figure 4.5.1 shows a proprietary data interface and software used with a combination of two forensically neutral test beds 共a benchtop fixture for an exemplar ECU and the subject accident vehicle with the subject ECU in situ兲 Figure 4.5.2 shows the data shared with the parties 共the formatted EEPROM data table and the resultant calculated acceleration/Delta V兲 Note that, by preagreement, no proprietary data were shared and the participants agreed to no photographs of proprietary data on computer screens This process satisfied the requirements of ASTM E2493-07 关4.15兴 A second example that followed this process is shown in Fig 4.5.3 Here we see the second EEPROM download of a subject SRS ECU 共the EDR in this case兲, done in accordance with the protocol above The first download was done by the component supplier 共TRW兲 and the second download was by an independent expert using verified forensically neutral equipment Both downloads produced identical data This download event is referenced by Van Gaasbeck 关4.12兴 in a section entitled “Unnecessary off-Vehicle Tests.” Van Gaasbeck 关4.13兴, Reference 关4.12兴, discusses this directly What is not said in 关4.13兴, Reference 关4.12兴, is that I was the expert who performed the independent download 4.6 An Example of Error Rate Calculations to Satisfy Litigation Tests Because download work product such as discussed in Section 4.5 was to be used in litigation, it was also necessary to characterize the possible error in that process For the examples shown in Figs 4.5.1 and 4.5.2, the work of developing and verifying a SLOT factor to translate raw EEPROM data to engineering units 共acceleration, gs兲 was extended to include an error analysis For that case, a test acceleration pulse was impinged on a previously undeployed exemplar ECU, causing a deploy command The error analysis was achieved by: 4.6.1 Plotting and comparing the resulting ECU record 共acceleration兲 with the external accelerometer This is shown in Fig 4.6.1 4.6.2 Plotting and comparing the cumulative time integrated acceleration product 共cumulative velocity, Delta V兲 for both the ECU acceleration with the external accelerometer This is shown in Fig 4.6.2 96 BLACK BOX DATA FROM ACCIDENT VEHICLES 䊏 Fig 4.6.1—Comparison of ECU acceleration with the external accelerometer Fig 4.5.2—Showing the data shared with the parties by preagreement 共the formatted EEPROM data table and the resultant calculated acceleration/Delta V兲 Note that, by pre-agreement, no proprietary data was shared This process satisfied the requirements of ASTM E2493-07 Formatted EEPROM data table Calculated Acceleration/Delta V Fig 4.5.3—Shows the second EEPROM download of a subject SRS ECU 共the EDR in this case兲, done in accordance with verified forensically neutral equipment The first download was done by the ECU supplier manufacturer and the second download was by an independent consultant 共the Author兲 using an AVT 716® for EEPROM download and a Snap-On MT-2500® for DTC confirmation 共both seen in the photos兲 The MT-2500 confirmed forensic neutrality and both the ECU supplier and the independent EEPROM downloaded data were identical Fig 4.6.2—Comparison of ECU acceleration generated Delta V with external accelerometer generated Delta V Fig 4.6.3—ECU acceleration generated Delta V with external accelerometer generated Delta V overlayed with deviation 共error, %兲 of the ECU generated Delta V versus the mean cumulative velocity 共Delta V兲, over the integration time CHAPTER 䊏 EDR DATA CONSIDERATIONS 97 Fig 4.6.4—An example of independently derived data, following the method of impinging a defined test acceleration pulse on a previously undeployed exemplar ECU, and then comparing the impinged pulse with the EDR internal record This example was derived using an exemplar 2003 Lexus ES300 ECU, and is one of the examples included in Ref 关4.8兴 Note that in this example, the impinged acceleration is shown as a line trace, the EDR acceleration is shown as a circle markers, the impinged-acceleration-derived DeltaគV is shown as a line trace and the EDR-derived DeltaគV is shown as triangle markers The EDR driver squib1 fire time is also shown 共as noted by the voltage difference across the Ω squib in the top half of the chart兲 4.6.3 Plotting the deviation of the ECU generated Delta V versus the mean cumulative velocity 共Delta V兲, over the integration time This is shown in Fig 4.6.3 This deviation 共error兲 plot shows the error rate to be less then ±3 % One reference to EDR data, Niehoff et al 关4.7兴, shows, for a sample of 28 test crashes, an average mean error of ±5.8 % for EDR-reported Delta V versus test-accelerometer-based Delta V8 Thus less then ±3 % the error rate derived in the tests here are well within accepted industry standards for EDR reporting accuracy 4.6.4 Another example of independently derived data is shown in Fig 4.6.4 In this example, using an exemplar 2003 Lexus ES300 ECU, and following the method of impinging a defined test acceleration pulse on a previously undeployed exemplar ECU, the impinged-pulsederived DeltaគV was compared DeltaគV derived from the with the EDR internal acceleration record It can be seen from this figure that the externalaccelerometer-derived DeltaគV is congruent with the EDR-derived DeltaគV, thus intuitively satisfying the acceptable error rate criteria This is one of the examples discussed in item of the introduction to this chapter Note that in this example, the driver squib1 fire pulse is also shown 共as recorded by the data acquisition system兲 so that the time to fire 共TTF兲 may also be discerned from the EEPROM data—if it is actually stored in that data Niehoff, et al., Table 2, average % error, frontal References 关4.1兴 NHTSA 49CFR Part 563, NHTSA Final Rule response to various petitions Full text found in The Federal Register, Vol 73, No 9, Monday, January 14, 2008/Rules and Regulations 关4.2兴 Kowalick, T., Fatal Exit, The Automotive Black Box Debate, Wiley, New York, 2004 关4.3兴 Bosch CDR coverage, Vehicles covered by the most common EDR data retrieval tool, the Bosch CDR® 共http://www.cdrsystem.com/coverage/index.html兲 关4.4兴 Auto Week, Black Box on Board, Nissan Vehicle Status Data Recorder 共VSDR兲 on GT-R, 24Sep08 共http://www.autoweek com/apps/pbcs.dll/article?AID⫽/20080924/FREE/ 809189970/1023/THISWEEKSISSUE兲 关4.5兴 Volvo Volvo Cars of Canada Corp Privacy Policy 共http:// www.volvocanada.com/Privacy.aspx?lng⫽2兲 关4.6兴 Toyota Event Data Recorders, Data Content and era of introduction 共http://www.toyota.com/about/our_business/opera tions/sales/EventDataRecorder.html兲 共September 26, 2008兲, 16Mar09 Proprietary EDR tool development 关4.7兴 Niehoff, P., Gabler, H., Brophy, J., Chidester, J., Hinch, J., Ragland, C., Evaluation of Event Data Recorders in Full Systems Crash Tests, National Highway Traffic Safety Administration Paper No 05-0271 共http://www.harristechnical.com/ downloads/05-0271-W.pdf兲 关4.8兴 ASA derivation of Toyota EDR data, March, 2009, ASA Inc., Reston, VA Proprietary EDR tool development 关4.9兴 ASA derivation of Hyundai EDR data, February, 2009, ASA Inc., Reston, VA Proprietary EDR tool development 关4.10兴 Wheelock, R J 共Bob兲, Ford Automotive Safety Office, Cur- 98 BLACK BOX DATA FROM ACCIDENT VEHICLES rent Ford Event Data Recorders, SAE Government / Industry Meeting, May 15, 2007 关4.11兴 ASA derivation of 2002–2005 Ford Explorer EDR data, January, 2009, ASA Inc, Reston, VA Proprietary EDR tool development 关4.12兴 Van Gaasbeck, S., How to Challenge Black Box Data, Trial Magazine, 01-FEB-07 关4.13兴 Van Gaasbeck, Reference 共12兲, Personal communication with Leon R Russell, Law Offices of Leon R Russell, P.C., Dallas, Tex Russell warns that “companies know far more about the airbag systems they manufacture than plaintiff’s counsel ever will Documentation describing air bag systems is usually cryptic and difficult to decipher It is essential then that plaintiff’s counsel retain an expert on black boxes not only to help counsel understand the air bag system specifications but also to test the critical components of the system to assure that they functioned as designed.” 关4.14兴 Gabler, H C Gabauer, D J and Newell, H L., Rowan University, Glassboro, New Jersey, Michael E O’Neill, George Mason Law School, Arlington, Virginia, Use of Event Data Recorder 共EDR兲 Technology for Highway Crash Data Analysis, December 2004 关4.15兴 ASTM E2493-07, “Standard Guide for the Collection of Non- 䊏 Volatile Memory Data in Evidentiary Vehicle Electronic Control Units,” 2007 Published by ASTM International, West Conshocken, PA, 19428–2959 关4.16兴 Capgo, Inc., Resistor Color Codes 共http://www.capgo.com/ Resources/Measurement/MeasHome/Resistors/ Resistors.html兲 关4.17兴 Graham, S J., Abandoning New York’s “General Acceptance” Requirement: Redesigning Proposed Rule of Evidence 702共b兲 After Daubert v Merrell Dow Pharmaceuticals, Inc Volume 43, Number of the Buffalo Law Review, Spring 1995, pages 229–61 关4.18兴 Oppenheim, E B., Scientific Evidence in Medical Negligence Litigation Part II 关4.19兴 Kesan, J P., Ph.D., A Critical Examination of the Post-Daubert Scientific Evidence Landscape 关4.20兴 Samelman, T R., Junk Science in Federal Courts: Judicial Understanding of Scientific Principles, Daubert v Merrell Dow Pharmaceuticals, Inc., 509 U.S 579 共1993兲 关4.21兴 Sanders, Joseph, University of Houston Law School, Shari S Diamond, Northwestern University Law School and American Bar Foundation, Neil Vidmar, Duke University Law School, Legal Perceptions of Science and Expert Knowledge, Psychology, Public Policy, and Law, 2002, Vol 8, No 2, 139– 153, Copyright 2002 by the American Psychological Association, Inc MON05-EB/Sep 2009 Subject Index A E EDR accelerometer analysis, 62–67 EDR data evidentiary reliability and, 90–98 interrogation procedures and, 90–98 litigation rules, 92–93 veracity requisites for, 91–92 EDR performance versus EDR family performance, 83–89 EEPROM data, 12–17 electronic control unit 共ECU兲, electronic data in air bag ECUs, 3–5 engineering units from raw data Inside ECU/EDR, 5–9 equipment and instrumentation, heavy duty truck wheel rotation tests, 70–71 error rate calculations for litigation, 95–97 event data and data translations, 91 event data recorders 共EDRs兲, evidentiary reliability and EDR data considerations and, 90–98 example protocol, interrogating EDR data, 94–95 Excel, 33–40, 60–61, 67, 81–82 ABS operation, 68–69 potential failure modes, 69–70 wheel speed sensor, 68–82 WSS signal processing by, 74–76 acceleration reporting, 22 accident investigation, 83 algebraic acceleration using count values, 19 analysis, EDR accelerometer, 62–67 azimuth notation, 62–63 azimuth derivation, vector magnitude and, 63–67 B base 10 system, 16 bitmap encoding, hexadecimal data and, 60 C F calculate cumulative velocity change, Delta V, 19 complex numeric translations, 15–16 forensically neutral data retrieval process, 91 formatting, spreadsheet data quantization and, 83–88 formula, 11 frontal air bag systems, deployment metrics, 2–3 D data import methods, 42–54 data interpretation derived metrics and, 88–89 for evaluating subject system, 88–89 data mining, 42–54, 46–47 data templates using hexadecimal data stream input, 47–49 data translations, 11 event data and, 91 deduction, 18 deductive analysis, source data for, 26–33 Delta V cumulative velocity change, 19 validation of acceleration digitization capability, 88 deployment metrics, frontal air bag systems, 2–3 derived engineering units, to calculate cumulative velocity change, 19 derived metrics, and data interpretation for evaluating subject system, 88–89 deriving scaling and transfer relationship from data, 9–10 diagnostic trouble codes 共DTCs兲, 60 multibyte and bitmap translation as, 55–61 digitization, hard copy data, 83 G geometric conventions for crash event, H handling data array that is not quite usual, 49–54 handshakes, hard copy data digitization, 83 hardcopy crash test data, EDR performance, 83–89 heavy duty truck wheel rotation tests, 70–71 hex data blocks, linear data list from, 12–17 hexadecimal arithmetic, 16–17 hexadecimal data bitmap encoding, 60 data templates using, 47–49 hexadecimal representation of signed binary integer value, 59–60 hypothesis, 18 99 Copyright © 2009 by ASTM International www.astm.org 100 I importing and mapping data, using Vetronix CDR retrieval tool, 44–46 inductive engineering method to document and confirm an ECU/EDR SLOT factor, 18–19 inductive reasoning, 18 interpretation, interrogating EDR data example protocol, 94–95 protocols, 93–94 interrogation, EDR data considerations and, 90–98 inverted octal coding for single positive integer value, 58–59 L lateral components, vector magnitude, 63–67 linear data list from hex data blocks, 12–17 litigation EDR engineering data rules, 92–93 error rate calculations for, 95–97 longitudinal components, vector magnitude and, 63–67 M mapping templates, 42–54 matching real data to standardized axis conventions, 62 multibyte and bitmap translation as applied to diagnostic trouble codes 共DTCs兲, 55–61 multibyte hexadecimal quantities for single positive integer value, 55 N noise immunity, 76–81 nonvolatile memory, nonvolatile memory data, O octal coding for single positive value, 55–58 operator equivalence, 33–40 P parsing, potential failure modes, ABS, 69–70 process outcome, 18 protocols, interrogating EDR data, 93–94 Q Quattro Pro, 33–40, 60–61, 67, 81–82 R repeatable method, 18 retrieving crash-related data, 5–9 retrieval of EEPROM data, 25–26 S signed binary integer value, hexadecimal representation of, 59–60 single positive value inverted octal coding for, 58–59 multibyte hexadecimal quantities for, 55 octal coding for, 55–58 source data for deductive analysis, 26–33 spreadsheet data quantization and formatting, 83–88 T time to fire 共TTF兲, 23 translation, 7–9 V validation acceleration digitization capability using Delta V analysis, 88 empirically derived SLOT constants, 22–23 vector magnitude, 63–67 and azimuth derivation, 63–67 lateral components, 63–67 and longitudinal components, 63–67 vehicle black box data, veracity requisites for EDR data retrieval, 91–92 verification, 18 Vetronix CDR retrieval tool, 44–46 W wheel rotation tests, 71–73 wheel speed sensor See WSS WSS ABS, 68–82 data from wheel rotation tests, 71–73 signal processing by ABS ECU, 74–76 signal review, 73–74 William Rosenbluth has 48 years of professional experience with complex electro-mechanical, electronic and computer components and systems He was employed by the IBM Corporation for 21 years, and for the past 23 years he has been principal engineer for Automotive Systems Analysis, Inc (ASA), in Reston, Virginia At ASA, he specializes in the analysis and diagnosis of computerrelated vehicle control systems and in the retrieval and analysis of electronic crash-event data in accident vehicles (black box data) Mr Rosenbluth is a Diplomate of the International Institute of Forensic Engineering Sciences (D-IIFES), a Fellow of the American Academy of Forensic Sciences (AAFS), a member of the Society of Automotive Engineers (SAE), ASTM International, and a life member of the Institute of Electrical and Electronics Engineers (IEEE) and the IEEE Computer Society At IEEE and AAFS, he has co-authored and/or presented over 60 papers dealing with automotive engineering investigations, co-instructed a continuing education short course, and organized engineering technical sessions His engineering achievements were recognized by the AAFS in 1999 when he was presented with the Andrew H Payne, Jr Special Achievement Award for Pioneering New Procedures, Outstanding Professional Performance and Outstanding Forensic Engineering Leadership He holds three U.S Patents, including one for a device to measure air bag static deployment throw and velocity using digital data acquisition His publications include a prior book, Investigation and Interpretation of Black Box Data in Automobiles, co-published by ASTM and SAE in June 2001, a Chapter on air bag systems data and diagnosis in Forensic Accident Investigation, Motor Vehicles-2, published by Lexis Law Publishing and papers in the Journal of Forensic Sciences and Sensors Magazine Mr Rosenbluth was chairman of the ASTM E30.05/WK 4150 Standards Development Group that produced E2493-07: Standard Guide for the Collection of Non-Volatile Memory Data in Evidentiary Vehicle Electronic Control Units That Standard Guide, developed with participants from industry, government and private sectors, was approved and published by ASTM in April 2007 He lives with his wife Jean in Reston, Virginia www.astm.org ISBN: 978-0-8031-7003-2 Stock #: MONO5