VWGoA - Ex. 1002
`Volkswagen Group of America, Inc., Petitioner
`
`1
`
`

`

`of side detection radar,” and seeks to improve the zone of coverage by delaying a
`
`signal turn-off, or applying a longer sustain time to hold the signal on. The ’927
`
`patent, col. 2, 11. 10—34. For example, according to the ”927 patent, sustaining an alert
`
`signal for a sustain time “improves the zone of coverage as perceived by the vehicle
`
`driver.” The "927 patent, col. 2,. ll. 15—34; col. 4, ll. 8—211.
`
`4.
`
`The radar system described by the ’927 patent includes side detection system
`
`16, which has side detecnon radar antenna 14 and signal processor 18. The detection
`
`system detects an object in the adjoining lane, and alerts the driver when an Object is
`
`detected. The ’927 patent, col. 2, l. 66—col- 3, l. 27. The signal processor 18 discerns
`
`valid targets using various information such as the relative speed of the vehicle and
`
`the target, and the range rate of the target, and ignores these targets of little interest to
`
`the driver. The ’927 patent, col. 3, 1L 27—51.
`
`5.
`
`Figures 3a—3d illustrate the reflected radar signal strength, to show how weak,
`
`return signals cause gaps in the alert command. To remove these gaps, the ’927 patent
`
`describes “judiciously sustaining” the alert signals, “thereby extending the zone of
`
`coverage as perceived by the driver.” The ’927 patent, col. 3, 1. 52—co]. 4,1. 7. The alert
`
`signals fill in any gaps in the signal, and further add a period 48 to the end of the alert
`
`signal. As shown in Figure 4, the extended period 48 results in zone extensions 56 and
`
`64, so that the driver “has greater assurance that the blind spot is free of an object.”
`
`The ’927 patent, col. 4, 11. 8—21.
`
`2
`
`

`

`-
`
`----—--1-+--—e+ ——————
`
`.I x
`
`Vii” 91‘ !~
`_._______!__I
`1
`J
`
`!
`
`C” ”Cw-4°
`(
`
`38
`
`
`TIME
`‘2
`44
`.2
`Kr
`W 1-
`mam ,
`.
`
`
`TIME
`
`FIG-3b
`RENEW
`SlGNALFEELD
`STRENGTH
`
`FIG- 3c
`
`SUSTAINED
`
`1-
`
`F“ —+[
`
`
`
`FIG -3d m ,l ii”TILE
`
`
`
`6.
`
`Sustaining the alert signal follows the algorithm outlined at column 4, lines 22
`
`to 49. Specifically, if the alert is active for at least a threshold time, a Variable sustain
`
`time, selected as a function of vehicle speed, delays the nun—off of the alert The ’927
`
`patent, col. 4, 11. 41—44.
`
`7.
`
`In a Notice of Allowance dated July 22, 1997, the Examiner acknowledged the
`
`pertinence of several prior art documents, including Bernhard, "to the method claimed
`
`-3-
`
`3
`
`

`

`in the ’927 patent, stating, for example, that “Bernhard discloses a method for
`
`providing guiding assistance for a vehicle in changing lane,” but stating that
`
`The prior art cited herein fails to disclose a method of improving the
`
`perceived zone of coverage response of automotive radar comprising the
`
`steps of selecting a variable sustain time as a function of relative vehicle
`
`speed, and sustaining an alert signal for the variable sustain time if the
`
`alert signal was active for a threshold time.
`
`8.
`
`Therefore, according to my understanding of the prosecution history of the
`
`’927 patent, it was allowed because it claims “selecting a variable sustain time as a
`
`function of relatiVe vehicle speech” and “if the alert signal was active for the threshold
`
`time, sustaining the alert signal for the variable sustain time.”
`
`The Combination of Bernhard, Pakett, and Fujilci — Claims '1, 2, and 6
`
`'9.
`
`Bernhard describes a computer-assisted guidance system for a motor vehicle
`
`that includes a number of radar devices including a rear—mounted radar device HR, at
`
`disrance radar device AR , a blind spot radar device TWR1 and forward-directed radar
`
`device VR. Bernhard, col. 3, ll. 34—40. “These devices detect the presence of objects
`
`in the respective area covered by them, and also permit the distance from the object
`
`to be determined.” Bernhard, col. 3, 11. 40-43.
`
`10.
`
`As described by Bernhard, radar devices measure the relative speed of objects 1
`
`to 4, compared to the driver’s vehicle 0. The driver’s vehicle speed v0 is measured by
`
`4
`
`

`

`a speedorneter. Bernhard, col. 4, 11. 35—40. Therefore, Bernhard describes determining
`
`the relative speed of host and target vehicles.
`
`11.
`
`Referdng to the radar devices, Bernhard states that “[t]hese‘ devices detect the
`
`presence of objects in the respective area covered by them, and also permit the
`
`distance £er the object to be determined.” Bernhard, col. 3, ll 40—43. The raw data
`
`from these radar devices are processed, including filtering out faults, and tested for
`
`sufficient plausibility. Bemhard, col. 4,
`
`ll. 40—44. Bernhard therefore detects the
`
`presence of a mget vehicle, and produces an alert command.
`
`12.
`
`The distances 801.
`
`to 304, as detected by the radar devices, are used to
`
`determine whether a lane change is possible. If a lane change is not possible, an
`
`instruction to stay in lane is issued to the driver. Bernhard, co]. 5, l. 44—c'01. 6, 1. 22.
`
`Therefore, Bernhard describes activating an alert signal in response to the alert
`
`command.
`
`13.
`
`Pakett describes a smart blind spot detection system. Using radar, the blind
`
`spot system detects the presence of an obstacle, measures the relative speed between
`
`the vehicle and the obstacle indicated by the Doppler shift in the radar signals, and
`
`generates an alarm if the obstacle is traveling at a similar speed and direction as the
`
`vehicle. Pakett, col. 2, 11. 8—13. To prevent unnecessary alarms, the smart blind spot
`
`system only warns the driver if the object persists for the “persistence period,” which
`
`is the time it takes for the vehicle to travel 15 feet, or if the presence of an object is
`
`indicated within two seconds of the previous warning. Pake'tt, col. 6, II. 43—56. Then
`
`-5-
`
`5
`
`

`

`the alarm is sustained for at least one second after the object is no longer detected. If
`
`the alarm has been on for more than one second without reactivation, the alarm is
`
`stopped. Pakett, col. 7, L 64—col. 8, l. 5; Fig. 3A.
`
`14.
`
`Pakett also describes a low pass filter 27, which serves to eliminate signals for
`
`objects that only briefly appear in the vehicle’s blind spot, as these objects are not of
`
`interest to the driver. Pakett, col. 5, ll. 11—31. The low pass filter 27 passes signals of
`
`lower frequency, i.e., those signals that appear for longer duration. Therefore, the low
`
`pass filter 27 distinguishes signals that are maintained for at least a threshold amount
`
`of time from signals that only briefly appear. Only those signals that are greater than
`
`the threshold time are passed through the filter and to be utilized to activate an alarm
`
`signal. These signals, which are longer than the threshold time, may be sustained by
`
`the system described in Pakett, for a sustain time of one Second. Pakett, col. 7, l. 31—
`
`col. 8, l. 10.
`
`15.
`
`Therefore, in the system described by Pakett, signals that are active. for a
`
`threshold time are sustained, and the zone of coverage would appear, to the driver, to
`
`increase according to the sustain time.
`
`16.
`
`Fujiki describes a vehicle system including a radar device that emits a radar
`
`signal in order to detect obstacles relative to the vehicle. The system prevents “stop
`
`starting braiding due to momentary ‘safe’ signals.” Abstract. The system maintains a
`
`safe distance between vehicles by comparing the relative speed and distance betWeen
`
`the vehicles to a setpoint curve, and applying the brake when the vehicles are Within
`
`-6-
`
`6
`
`

`

`the safe distance for the measured relative speed. Fujiki, col. 2, ll. 1—13gFig. 313. If the
`
`system determines that braking is not required to maintain a safe distance between the
`
`vehicles, the system checks whether the brake was just previously 0n. Funki, col. 5, 11.
`
`46—57. If the brake was just previously on, the system applies additional braking, i.e.,
`
`sustains the brake, for a period of time. Fujild, col. 5, IL 59~61.
`
`17.
`
`Fujiki describes three preferable periods of time that
`
`the brake will be
`
`sustained: 1‘1, 2‘2, or :3. One of these time periods, 2;, is expressly described as a fiancn'on
`
`of the relative velocity of the vehicles. Fujiki, col. 5, ll. 59—67 (“:3 is a function of the
`
`pr'e—select'ed distance D and the relative velocity dR/dt just prior [sic]
`
`the. danger
`
`signal disappearing, for which the additional braking will take place”). Fujiki therefore
`
`describes selecting a variable sustain time as a function of relative vehicle speed.
`
`18.
`
`The purpose of this system, according to Fujil'ti, is to sustain the braldng system
`
`in an activated state for a predetermined distance after a danger signal disappears.
`
`Fujiki, col. 1, ll. 53—58. Thus, Fujiki is improving the perceived zone of coverage of
`
`the detection system (after the signal has been persistent for a threshold time).
`
`19.
`
`Regarding claim 2 of the ’927 patent, as described above, the variable sustain
`
`time of Fujiki is a function of the relative vehicle speed. Fujiki, col. 5, ll. 59—67. Fujild
`
`states that the sustain time is a function of a predetermined distance and the relative
`
`velocity. This time must be the product of the distance and the inverse of the relative
`
`velocity. Therefore, Fujilsi describes that the sustain time is an inverse function of the
`
`relative vehicle speed.
`
`7
`
`

`

`20.
`
`Regarding claim 6 of the ’927 patent, as stated above, Bernhard, Pakett, and
`
`Fujiki each describe determining the host vehicle speed. Bernhard, col. 4, 1]. 35—40;
`
`Pakett, col. 7, 1], 31—32; Fujiki, col. 2, 11. 28—31. As further described above, Pakett
`
`includes a persistence period of the time it would take for the vehicle to travel 15 feet.
`
`Pakett, col. 6, ll 43-46, col. 7, ll. 32—36. The determined time, i.e. the threshold time,
`
`is a function of vehicle speed, as the speed of the vehicle will dictate how long it will
`
`take the vehicle to travel 15 feet
`
`I declare that all statements made herein of my own knowledge are true and
`
`that all statements made on information and belief are believed to be true, and filrther
`
`that these statements were made with the knowledge that willfill false statements and
`
`the like so made are punishable by 'fine or imprisonment, or both, under §1001 of
`
`Title 18 of the United States Code.
`
`Dated: Mgrok 16, 201$? 2M34/
`
`Dr. David M. Bevly
`
`8
`
`

`

`Exhibit
`
`A
`
`9
`
`

`

`David M. Bevly
`
`Phone: (334) 844-3446
`Fax: (334) 844-3307
`Email: dmbevly@eng.auburn.edu
`
`Department of Mechanical Engineering
`Auburn University
`Auburn, AL 36849-5341
`
`Current
`Position
`
`Education
`
`Auburn, AL
`Auburn University
`20.1 0-20} 5
`Aibert Smith ifndan-ed Professorship
`2th tJ-Presctit
`Professor. Department af'Mecnanicai Engineering
`200 7-201 0
`Associate Professor, Department of'Meehanicai Engineering
`MOI-2007
`Assistant Professor. Department af'Mecnanieat Engineering
`In charge ofteaching mechanical engineering courses and developing a strong externally
`funded research program in the area of dynamics, controls, and transportation systems.
`
`Stanford, CA
`Stanford University
`Pitt), Mechanical Engineering. September 2001. Thesis directed by Professor Bradford
`Parkinson entitled “High Speed, Dead Reckoning,
`and Towed Implement Control
`for
`Automatically Steered Farm Tractors Using GPS.”
`Major Area: Automatic Control, Minor Area: Mechatronic Systems
`
`Cambridge, MA
`Massachusetts Institute of Technology
`Master of Science, Mechanical Engineering, September 1997, Thesis directed by Professor
`Steven Dubowsky. entitled “Action Module Planning and Cartesian Based Control of an
`Experimental Climbing Robot.”
`
`College Station, TX
`Texas A&M University
`Bacheior' c/‘Setence, Mechanical Engineering, Summa Cum Laude, May 1995, Broad eurn'culum
`in mechanical engineering with emphasis in design, dynamics, and control.
`Completed
`undergraduate research units directed by Professor Christian Burger.
`
`Awards
`
`2007 SAE Ralph R. Teetor Educational Award
`2005, 2008, 2010 Outstanding Mechanical Engineering Faculty Member Award
`2010 Walker Teaching Award
`2006 Office ofNaval Research Young Investigator Award
`2006 Army Research Office Young Investigator Award
`2000 SAE Myers Award for Outstanding Student Paper
`Best Paper of Session: ION CPS 2000
`Best Presentation of Session: ACC 2000
`
`Research
`
`Background
`
`GPS Lab, Stanford
`Graduate Researcher
`
`Stanford, CA
`1998-2001
`
`Performed research and implementation of hardware for automated control of a farm tractor
`using GPS. Developed accurate vehicle models for high-speed control and towed implement
`control,
`Responsible for programming data acquisition equipment
`for Lynx Real Time
`Operating System and integration of several analog sensors. Developed method for integrating
`multiple inertial type sensors with GPS, through an EKF, for estimating multiple biases and dead
`reckoning control ofthe tractor. Created computer simulations and models to verify control and
`estimation techniques performed on the tractor.
`DYNAMIC Design Lab, Stanford
`Graduate Researcher
`
`Stanford, CA
`1 999-200]
`
`Initiated the ideas and performed research on the use of GPS velocity measurements for
`estimation of vehicle states and developed a method for measuring wheel slip and side-slip
`angle. Developed vehicle simulation models and performed experiments on a test vehicle to
`verify methodology.
`
`
`
`David M. Bevly — 2015 Dossier
`
`10
`
`10
`
`

`

`Teaching
`Experience
`
`Auburn, AL
`Auburn University
`2002-prexettt
`MIL'C 'H 4420 - Vehicie Dynamics
`Developed this new course as part of the College of Engineering’s Automotive Certificate
`Program Emphasized the importance of computational numerical methods to simulate and
`analyze vehicle systems. Was able to secure an Infiniti G35 test vehicle from Nissan which was
`instrumented by graduate students in the laboratory and used as part of the class. The students
`test drive the vehicle, collect data, and provide lab reports analyzing the experiments and data.
`Mi-ICH 3 .140 - System Dynamics and Controls
`2001-pt-esettt
`Have taught this traditional undergraduate course most Fall and Spring Semesters since arriving
`at Auburn University. Assigned homework and team design projects requiring simulation,
`analysis, and design ofa control system using MATLAB.
`2002-pt‘cs'ent
`Mia'( '15" 771’ 0 - Optimal Estimation and Control
`Developed this new graduate course. The class consists of assignments which utilize data from
`experimental research platforms and culminates in a team research design project.
`2005-pmsem
`MIX '1‘! 6970 - Fundamentals of UPS
`Developed this new undergraduatetgraduate course on the fundamentals of GPS with graduate
`student Matthew Lashley.
`The class consists of assignments which utilize data from
`experimental research platforms in the laboratory and the use of GPS equipment from the
`laboratory.
`
`HONORS AND AWARDS
`
`camawewwr
`
`While at Auburn, Dr. Bevly has received the following awards:
`201 1 Auburn University Graduate School Outstanding Faculty Member
`Albert Smith Professorship, 2010-2015
`Philpott-Westpoint Stevens Professorship, 2008-2010
`2010 William Walker Teaching Award
`2010 Auburn Alumni Engineering Council Outstanding Faculty Award
`2008 Auburn Alumni Engineering Council Outstanding Faculty Award
`2007’ SAE Ralph R. Tector Educational Award
`2006 Office of Naval Research Young Investigator Proposal (ONR YlP) Recipient
`2006 Army Research Office Young Investigator Proposal Recipient
`10.2005 Auburn Alumni Engineering Council Outstanding Faculty Award
`1 l. 2003 Auburn Alumni Engineering Council Junior Faculty Research Award
`
`
`
`David M. Bevly — 2015 Dossier
`
`11
`
`11
`
`

`

`SCHOLARLY CONTRIBUTIONS
`
`The following section outlines Dr. Bevly’s scholarly contributions and is divided into three
`areas:
`teaching, research, and outreach.
`
`A. Teaching
`
`1. Courses Taught—Table 2 indicates the courses that Dr. Bevly has taught over the past
`three years. Lecturet’lab hours and enrollment are also noted. MECH 7990 Research and
`Thesis and MECH 8990 Research and Dissertation courses have been omitted.
`
`Table 2. Actual Course Taught from Spring 2012-2015
`
`Semester
`
`Course
`
`Hours
`
`Enrollment
`
`Fall 2012
`
`MECH 3140 System Dynamics and Control
`
`3 lee.
`
`Spring 2013
`
`Fall 2013
`
`Spring 2014
`
`Fall 2014
`
`Spring 2015
`
`
`
`MECH 4420 Vehicle Dynamics
`MECH 7710 Optimal Estimation and Control
`
`MECH 3|40 System Dynamics and Control
`\AECH 5970 Fundamentals of GPS
`
`VIECH 3140 System Dynamics and Control
`VIECH 4420 Vehicle Dynamics
`
`VlECH 3140 System Dynamics and Control
`
`VIECH 4420 Vehicle Dynamics
`VIECH 7710 Optimal Estimation and Control
`
`3 Ice.
`3 lee.
`
`3 Ice.
`3 leg
`
`3 lec.
`3 lee.
`
`3 lee.
`
`3 Ice.
`3 Ice.
`
`41
`
`20
`12
`
`60
`22
`
`33
`18
`
`50
`
`23
`18
`
`2. Graduate Students (Graduated)
`As outlined below, Dr. Bevly has served as the major professor adviser of 10 PhD
`students and 37 MS students. The names of the students research topic, department, degree,
`and graduation year are provided below.
`
`
`
`
`7
`
`Student
`
`..
`
`Research Topic
`
`
`__- MSEPhD__
`
`
`
`CS
`
`EE
`
`PhD
`
`PhD
`
`I
`
`;
`
`2009
`
`2009
`
`
`
`
`
`
`
`
`
`Winnard Britt
`
`.
`
`Matthew Lashley
`
`William Travis
`
`A software and hardware system for the
`autonomous control and navigation ofa
`trained canine
`Modehng and Performance Analyms ofGPS
`Vector Trackm_Alor1thms
`Nawgatlon Accuracy of Various Sensors for
`_
`Glound Vehicles
`— Oil-line Vehicle Estimation and Navigation
`‘
`.
`A Maximum Effort Control System for the
`
`2010
`. Jetfrey Mlllel‘
`T1‘ackin_ and Control ofa Guided Canine
`
`
`
`
`
`
`David M. Bevly — 2015 Dossier
`
`12
`
`ME
`
`PhD
`
`2010
`
`2010
`
`_
`
`
`
`
`
`12
`
`

`

`
`
`Be“ Clark
`
`ME
`
`PhD
`
`- Dnynai Guassial P'ocess Muels for Model
`.
`-
`
`Dav1d BrOdean
`Predictive Control of Vehicle Roi]
`Fault Detection and Exclusion in Deeply
`1m irated orsans
`Terrain and Road Characterization and
`Roughness Estimation for Simulation and
`Control of Unmanned Ground Vehicles
`Integrated sensor fuslon for vehicle control
`applications
`Modeling the Variations of Tractor Dynamics
`for Tyoical Farm A lications
`f
`N '
`I
`'
`A
`fV ‘A
`avrgation Iccuracyo
`at tous Sensors or
`Ground Vehicles
`
`ME
`
`ME
`
`VIE
`VIE
`
`:
`
`PhD
`
`201 l
`
`PhD
`
`MS
`
`MS
`
`2014
`
`_
`'
`2007 _
`2006
`
`Navigation in GPS Denied Areas
`
`2007
`2007 ;
`2008
`Development and Analysis of Deeply ---
`Interated GPSIINS
`EE
`2006 .
`2006 .
`2006
`
`GPS Applications tor Advanced Driver
`Assrstance S stems
`.
`Direct Adaptive Control of Farm Tractors
`2008 _
`Using GPS for Model Based Estimation of
`I
`Critical Vehicle States and Parameters - 2004 ;
`
`ME
`
`MS
`
`
`
`I
`
`.
`
`:
`
`Jeremyr Dawkins
`
`Ya“ Wang
`
`; Pall] Pearson
`-
`'
`-
`-
`William Trams
`
`. John Wall
`Kennytamben
`Dustin Edwards
`
`Matt Lashley
`Michael Newlin
`Josh Clanton
`
`5
`_ Benton Derrick
`I
`RUSty Anderson
`
`John Plumlee
`
`.
`
`Christopher
`Hamm
`; Evan Gartley
`I
`.
`
`. Randy Whltehead
`Warren Henmken
`
`:
`
`David H0do
`
`Harold Henderson
`
`.
`
`Kenneth Lambert
`
`E
`
`:
`
`I
`
`:
`
`.
`
`_ Dustin Edwards
`I
`Ben Clark
`
`Novel Control Allocation and Quadratic
`Programming Algorithms for Control of Aero
`and Ground Vehicles
`Comparison of GPSIINS Integration
`Techniques for High Dynamic Environments
`Adaptive Steering Control of Farm Tractors
`Determination of Vehicle Parameters that
`
`ME
`
`MS
`
`EE
`
`MS
`
`ME
`
`MS
`
`MS
`
`MS
`
`MS
`
`EE
`
`ME
`
`BE
`ME
`
`Influence Vehicle Rollover Pro-ensit -- 2005
`low level GPS INS Sensor Fuston for
`ME
`MS
`2005
`Imuroved Vehicle Navration
`Development of an autonomous mobile robot-
`trailer 5 stern for UXO detection
`Delauve poSIttomng ofvunmanned ground
`\ehicles usm_ ultrasonic sensors
`Registration and tracking ofobjects with
`
`2004
`
`_
`
`2005
`
`_
`2005 _
`I
`
`2007
`
`2003
`
`_
`
`_
`
`.
`
`2009 i
`
`A study ofvehicle properties that influence
`rollover and their effect on electronics stability
`controllers
`Parameter estimation techniques for
`
`determinin safe vehicle seeds in UGVs - 2008 .
`GPSIINS Operation in shadowed
`Environments
`Tire Force Estimation in OFF-Road Vehicles
`
`ME
`
`MS
`
`.
`I
`
`MS
`
`200'?
`
`Ryan Hill
`
`using Suspension Strain and Deflection
`
`
`
`
`David M. Bevly — 2015 Dossier
`
`13
`
`13
`
`

`

`Lane detection and vehicle attitude using Lidar
`Use ot‘Vision Sensors and Lane Maps to Aid
`CPS-INS Naviation
`Low-Bandwidth Three Dimensional Mapping
`2012
`and Latency Reducing Model Prediction to
`
`Improve Teleoperation of Robotic Vehicles
`Non-Collocated Control of an Autonomous
`EE
`Ms
`2012 :
`
`
`
`
`
`Roll & Bank Estimation UsingGPSKINS and
`Lowell Brown
`
`Suspension Deflections
`Closely Coupled GPSJINS Relative
`Positionin for Automated Vehicle Convo s
`
`Scott Martin
`
`Jonathan Ryan
`
`A Fully Integrated Sensor Fusion Method
`Combining a Single Antenna GPS Unit with
`Electronic Stabilit Control Sensors
`
`MS
`
`'
`
`-E:-:-
`
`Robust lane detection using VisionflMU
`'55
`2010
`Chris Rose
`
`measurements
`
`EE
`
`Ms
`
`2010 :
`
`Lon er Combination Vehicles cs
`
`: Jordan Britt
`
`John Allen
`
`William Woodall
`
`' Mike Payne
`
`I Michael Wooten
`
`‘ Robert Williams
`
`I Thomas Bitner
`
`Eric Broshears
`
`MS
`
`EE
`
`MS
`
`2013
`
`ME
`
`MS
`
`Vehicle-Trailer S stem Usin State Estimation
`High-Dynamic Range Coliision Detection
`using Piezoelectric Polymer Films for Planar
`and Non- lanar A 1 ulications
`Evaluation of Beam Load Cell Use for Base
`Reaction Force Collision Detection on
`Industrial Robots
`Guidance of an Off-Road Tractor-Trailer
`S stern Usin_ Mode] Predictive Control
`Detection and Removal of Erroneous GPS
`
`Si_nals Usin_ Au_le of Arrival
`Ultra-wideband Radio Aided Carrier Phase
`
`Ambiguity Resolution in
`Real-Time Kinematic GPS Relative
`Positionin
`
`ME
`
`MS
`
`I Jameson Colbert
`
`Development ofa Custom Data Acquisition
`System for the Study of Vehicle Dynamics in
`
`ME
`
`MS
`
`2013
`
`2014
`
`Andrew Hennigar
`
`Analysis of Record and Playback Errors of
`GPS Si_nals Caused b the USRP
`
`ME
`
`MS
`
`2014
`
`3. Graduate Students (Current)
`Dr. Bevly is currently serving as the major professor advisor for 6 PhD students and [7 MS
`students. Listed below is each student, graduate level, department, and topic of study.
`
`'
`
`'
`
`:
`
`'
`
`'
`
`.
`
`Lowell Brown
`
`,
`
`Optical flow for UGV navigation usmg plenopt1c
`camera
`
`ME
`
`PhD
`
`
`— Research Tepic _—
`
`
`_cottMartin
` Vector tracking aided carrier phase for GPS attitude
`
`Model parameter uncertainty and sensitivity for
`
`control erformance uarantees
`
`
`
`
`
`
`ME
`
`PhD
`
`
`
`
`
`
`Simultaneous localization, mapping and sen50r
`calibratlon
`:
`Vehicle dynamic consttaints to aid navigation _—
`_Jonathan Ryan
`Network based navigation
`_—
`David HOdO
`Vehicle based models and sensors to improve
`M F
`Danie] Salmon
`navi gation
`
`
`Jordan Britt
`
`David M. Bevly— 2015 Dossier
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`

`
`f (:1131111111503 0111(:115
`Netwn'orkia—asenyvectawuiiion
`_
`I Sostenez Perez
`Sensitivity and accuracy of friction and mass
`ME
`_
`estimation in heav vehicles
`Sensor fusion for pedestrian navigation
`
`John Dan Pierce
`
`William Apperson
`
`GPS guidance ofa tractor implement
`
`Brian Keyger
`
`Real-time implementation ol‘a vector tracking
`receiver on an FPGA
`
`S to
`
`I Sarah Preston
`I Nate Carson
`
`Use of clock bias for detecting false signals
`GPS Spoofing detection and mitigation using
`networked receivers
`
`: Robert Cofield
`I Scott Smith
`
`Map based navigation
`DRTKIRadar fiJsion for robust vehicle following
`
`: Joshua Starling
`_ Gabriel Morales
`: Velislav Stemcnov
`
`Multi-antenna signal processing for GPS
`Magnetometer based vehicle navigation
`Lidar based terrain perception
`
` U1mmmmmmmm(/3U)0'}com(I)03‘
`5mmmrm['1']rm{'1'rnU1U1[Tl
`
`: Jacoby Golden
`
`Image tracking for UAWUGV collaboration
`
`.
`
`E
`GNSS Vector Tracking analysis
`. Trip Richert
`EE
`Multi-antenna signal processing for GPS
`I Joseph Hamilton
`ME
`MPC‘ control for vehicle model uncertainty in CAC‘C
`: Xialong C30
`
`3 stems
`
`4. Committee Members
`
`Dr. Bevly has served as a committee member or outside reader for 12 students.
`
`I. Abby Anderson, M.S., May 2006, “Design, Testing, and Simulation ofa Low-Cost, Light-
`Weight, Low-G, IMU for the Navigation of an Indoor Blimp.”
`2. Darrel Krueger, MS, December, 2003’, “Investigation of Lateral Performance on and ATV
`Tire on Natural, Deformable Surfaces.”
`3. Desheng Ma, PhD, May 2010, “Design and Implementation of RF Receiver Front-end and
`Tunable Filter.”
`
`4. Yuan Yao, PhD, May 2010, “Design and Implementation of High-Speed Low-Power Data
`Converters.”
`
`5. Guangli Ma, PhD, December 2010, “Modeling, Machine Vision Sensing and Position Control
`of Braiding Point based on Braiding Process”
`6. Robert Jantz, MS, May 201 1, “Controlling the Speed ofa Magnetically-Suspended Rotor
`with Compressed Air.”
`7, Russell Green, MS, August 201 l, A Non-contact Method for Sensing Tire Contact Patch
`Deformation Using a Monocular Vision System and Speckled Image Tracking
`8. Robert Jantz, MS, May 201 I, “Controlling the Speed ofa Magnetically-Suspended Rotor
`with Compressed Air.”
`James Jantz, MS, May 2014, Development ofa Multi-mode Adaptive Controller and
`Investigation of Gain Variations with Speed and Balance Changes
`10. Robert Thetford, Jr., MEE, “Visual Position and Angle Recognition using a Neural Network”
`August 2012.
`l l. Siwei Wang MEE “Design and Analysis of Trailer System for the Metalmapper Sensor”,
`May 2013.
`12. Brian Reitz, PhD, AE, Dec 2014, “Control System Development for Autonomous Aerobatic
`Maneuvering with a Fixed-Wing Aircraft.”
`
`9.
`
`
`
`David M. Bevly — 2015 Dossier
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`

`

`5. Courses and Curricula Developed
`
`While at Auburn, Dr. Bevly has developed three new courses, each of which is described
`below.
`
`MECH 4420 Vehicle Dynamics
`This course developed by Dr. Bevly as part of the Automotive Certificate in Engineering
`program is a three hour undergraduate technical elective. The course introduces students to
`the basic mechanics governing vehicle performance, analytical methods, and terminology.
`The students are given assignments that require the development of mathematical models in
`order to simulate and analyze the various components of vehicle dynamics. The class also
`contains an un-official lab in which the students drive instrumented test-vehicles donated to
`
`Dr. Bevly's research program in order to collect data. This provides the students additional
`exposure to sensors and instrumentation as well as simulation and model validation, and it is
`an example of Dr. Bevly's commitment to integrate his research with the undergraduate
`education at Auburn. The class also contains a final project in which students must present
`model development, simulation and experimental results to the class and answer questions
`from members of Dr. Bevly's research lab.
`
`MECH 77“) Optimal Estimation and Control
`This course is a graduate class in optimal control and estimation and is intended to be a
`follow-up class to ELECTSOO. The class covers statistical modeling of disturbances and
`measurement noises and develops methods to design sensor fusion and estimation algorithms
`to account for the statistical uncertainties. The class consists of several assignments that
`utilize GPS and [MU data from Dr. Bevly‘s research projects and a final project in which
`students can apply the material from the class to a problem related to their area of research.
`
`MECH 5970 Fundamentals ofGPS
`Developed this new undergraduatefgraduate course on the fundamentals of GPS with
`graduate student Matthew Lashley. The class consists ofassignments which utilize data from
`experimental research platforms in the laboratory and the use of GPS equipment from the
`laboratory.
`
`6. Grants Related to Teaching — None.
`
`7. Publications Related to Teaching - None.
`
`8. Other Contributions to Teaching - None.
`
`9. Statement of Teaching Philosophy and Self-evaluation
`
`a. Philosophy—As a professor of mechanical engineering, my goal is to give students a
`practical yet theoretical foundation that will prepare them for jobs in industry or further work
`in academia. Because I believe practical examples are the best way to motivate students, i
`combine theory and practice by giving examples from past work and research. Test
`platforms used in my GPS and Vehicle Dynamics Laboratory are used to provide data,
`examples, and experience to students in a variety of classes taught by Dr. Bevly. This
`exposure allows students the Opportunity to get hands-on experience applying theories taught
`
`
`David M. Bevly — 2015 Dossier
`
`16
`
`16
`
`

`

`I strive to give the students 1 interact with, both in my
`in classes to cutting-edge platforms.
`research group and in the classroom, the invaluable experiences gained from building and
`working with experimental systems.
`Along with using real-world examples to give students the basic theoretical skills to
`advance in engineering,
`1
`seek to create a professional atmosphere that cultivates
`responsibility and reSpect. My assignments stress the importance of self-motivation as they
`challenge students to extend their knowledge beyond class discussions and lecture notes.
`Furthermore, these assignments teach students that they are ultimately responsible for their
`success, a lesson that
`1 hope students take with them outside the classroom. Another
`component of my classes is team projects. Not only do these projects teach students how to
`work together and respect each other,
`they also provide opportunities for professional
`deveIOpment as students are required to present their projects before their peers. By relying
`on real-world examples and creating a professional environment that stimulates learning and
`growth,
`I believe my classroom prepares students to become successful engineers in the
`twenty-first century.
`
`[7. Seifevaluation—My level of effectiveness as a teacher can be demonstrated by my student
`evaluations given at the end of the semester. The final question on the survey asks students
`to rank the instructor‘s effectiveness, with 6 being the highest score Table 3 illustrates the
`responses from all the classes I have taught at Auburn, with the exception of MECH 69?0
`Special Topics: Fundamentals of GPS. The effectiveness scores of all classes result in an
`average score of 5.4, indicating that the majority of students find my teaching effective.
`
`Table 3. Summary of Recent Student Evaluations.
`
`Semester
`
`Course
`
`Res 011593
`
`E ectiveness
`
`Fall 2012
`
`MECH 3140
`
`Spring 2013 MECH 7710
`MECH 4420
`
`Fall 2013
`
`MECH 3140
`
`Spring 2014 MECH 3140
`MECH 4420
`
`Fall 2014
`
`MECH 3140
`
`20
`
`11
`7
`
`31
`
`18
`8
`
`21
`
`5.]
`
`5.5
`5.7
`
`4.9
`
`5.2
`5.9
`
`5.3
`
`
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`David M. Bevly — 2015 Dossier
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`

`B. Researcthreative Work
`
`1.
`
`Books
`
`1. David M. Bevly and Steward Cobb, GNSS For Vehicle Control, published by Artech,
`2010.
`
`2.
`
`Article-length Publications
`
`Dr. Bevly has written 3 book chapters, 37 journal articles, 35 of which are in print and 2 of
`which are currently under review. Dr. Bevly also has published 3 magazine articles and 10?
`conference papers. Dr. Bevly’s Google Scholar H-index is 26.
`
`a.
`
`1.
`
`Book Chapters
`David M. Bevly, Demoz Gebre-Egziabher, Mark Petovello, “Integration of GNSS and
`INS: Part 1” in GNSS Applications and Methods, published by Artech, 2009
`
`David M. Bevly, Demoz Gebre-Egziabher, Mark Petovello, “Integration of GNSS and
`INS: Part 2” in GNSS Applications and Methods, published by Artech, 2009
`
`Bevly, D. M., Gebre—Egziabher, D., and Parkinson, B. W., "Error Analysis of a Dead
`Reckoning Navigator for Ground Vehicle Guidance and Control," selected to be
`published in the new GPS Red Book.
`
`Refereed Journal Publications
`
`In Print
`
`. Wooten, J. M., Bevly, D.M., Hung, J., “Piezoelectric Polymer-Based Collision Detection
`Sensor for Robotic Applications,” Electronics, Vol. 4, No. I, 2015, pp. 204-220.
`Brown, Lowell S., Bevly, D.M., “Roll and Bank Estimation Using GPSlINS and
`Suspension Deflections,” Electronics, Vol. 4, No. 1, 2015, pp.
`1
`I 8-149.
`Rose, C.; Britt, J .; Allen, 5.; Bevly, D., "An Integrated Vehicle Navigation System
`Utilizing Lane-Detection and Lateral Position Estimation Systems in Difficult
`Environments for GPS," Intelligent Transportation Systems, IEEE Transactions on ,
`Vol.15, No.6, March, 2014. pp. 2615 -— 2629.
`Ryan, Jonathan, and Bevly, D.M., “On the Observability of Loosely Coupled Global
`Positioning Systemllnertial Navigation System Integrations With Five Degree of
`Freedom and Four Degree of Freedom Inertial Measurement Units”, Journal of
`Dynamics, Systems, Measurement, and Control, Vol. 136, No. 2, March 2014, pp.
`Martin. S.
`, Beviy, D.M., " Comparison of GPS-based autonomous vehicle
`following using global and relative positioning,” international Journal of
`Autonomous Vehicle Systems, Vol. 10, No. 3, 2012 pp. 229-255.
`Miller, J., Bevly, D.M., “A system for autonomous canine guidance,” International
`Journal of Modelling, identification and Control, Vol. 20, No. 1, 2013, pp. 33-46.
`Dawkins, J.J., Bevly, D.M, and Jackson, R.L., “Evaluation of fractal terrain model for
`vehicle dynamic simulations,” Journal of Terramechanics, Vol. 49, No. 6, 2012, pp. 299—
`30?.
`
`Miller, J., Bevly D.M., Flowers, G., “A System for Tracking an Autonomously
`Controlled Canine, The Journal ofNavigation, Vol. 64, No. 3, July 2012, pp 422-444.
`
`
`
`David M. Bevly — 2015 Dossier
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`

`

`10.
`
`ll.
`
`12.
`
`13.
`
`14.
`
`15.
`
`16.
`
`1?.
`
`18.
`
`19.
`
`20.
`
`21.
`
`22.
`
`23.
`
`Huang, W. and Bevly, D.M. “Set terrain based optimal speed limits for heavy trucks
`energy saving”, International Journal a]? Powertrains, Vol. I, No. 4, 2012, pp. 335—350.
`Davvkins, II, Bevly, D.M, and Ja