US0073 89178B2
`
`(12)
`
`United States Patent
`Raz et a].
`
`(10) Patent N0.:
`(45) Date of Patent:
`
`US 7,389,178 B2
`Jun. 17, 2008
`
`(54) SYSTEM AND METHOD FOR VEHICLE
`DRIVER BEHAVIOR ANALYSIS AND
`EVALUATION
`
`(75) Inventors: Ofer Raz, Moshav Bna a IL ; Hod
`y
`Fleishman, Jerusalem (IL); Itamar
`Mulchadsky> Tel'Avl" (IL)
`
`6/1987 Lemelson
`4,671,111 A
`5,270,708 A 12/1993 Kamishima et a1.
`5,546,305 A
`8/1996 Kondo
`5,570,087 A 10/1996 Lemelson
`5,805,079 A
`9/1998 Lemelson
`6,060,989 A
`5/2000 Gehlot
`6,438,472 B1
`8/2002 Tano et a1.
`
`.
`_
`.
`.
`.
`(73) Asslgnee' grrizlhrggségvmg Technologles Ltd"
`
`2002/0128751 A1 *
`9/2002 Engstrom et a1. ............ .. 701/1
`2007/0001831 A1* 1/2007 Raz e161. ................. .. 340/439
`
`( * ) Notice:
`
`Subject to any disclaimer, the term of this
`patent is extended or adjusted under 35
`U.S.C. 154(1)) by 698 days.
`
`(21) APP1~ NOJ 10/894-11345
`
`(22) Filed:
`
`Jul. 20, 2004
`
`(65)
`
`Prior Publication Data
`Us 2005/0131597 A1
`Jun 16, 2005
`
`Related US, Application Data
`_
`_
`_
`_
`(60) 3021330311211 apphcanon NO‘ 60/528522’ ?led on Dec‘
`’
`'
`
`(51) Int. Cl.
`
`(2006.01)
`G01C 21/00
`701/200_ 70109 70184
`52 U 5 Cl
`701/36_’701/56_’340/903’
`(
`)
`'
`'
`' """""""""""" "
`’
`’
`_
`_
`_
`(58) Field of Classi?cation Search .................. .. 701/ 1,
`70109, 31’ 33’34’ 36> 56> 301; 340/438’
`_
`349/439’ 903
`_
`See apphcanon ?le for Complete Search hlstory'
`References Cited
`
`(56)
`
`U.S. PATENT DOCUMENTS
`
`* Cited b examiner
`y
`Primar Examiner4Gertrude A. Jean laude
`y
`g
`(74) Attorney, Agent, or F irmiBroWdy and Neimark
`
`(57)
`
`ABSTRACT
`
`A system and method for analyzing and evaluating the
`performance and attitude of a motor vehicle driver. A raW
`data stream from a set of vehicle sensors is ?ltered to
`eliminate extraneous noise, and then parsed to convert the
`stream into a string of driving event primitives. The string of
`driving events is then processed by a pattern-recognition
`system to derive a sequence of higher-level driving maneu
`.
`.
`.
`.
`.
`vers. Dr1v1ng maneuvers mclude such fam1l1ar procedures as
`.
`.
`.
`.
`lane changmg, passmg, and turn and brake. Dr1v1ng events
`and maneuvers are quanti?ed by parameters developed from
`the sensor data. The parameters and timing of the maneuvers
`can be analyzed to determine Skin and attitude factors for
`evaluating the driver’s abilities and safety ratings. The
`rendering of the data into common driving-related concepts
`alloWs more accurate and meaningful analysis and evalua
`tion1 than is possible With ordinary statistical threshold-based
`ana ys1s.
`
`4,500,868 A
`
`2/1985 Tokitsu et a1.
`
`15 Claims, 14 Drawing Sheets
`
`Sensors
`103
`
`05
`
`@ Q
`
`K10]
`
`07
`
`109
`
`111
`Other
`sensors
`
`RAW DATA STREAM
`[101 (101
`Event Handler 2M “1 Low-Pass Filter
`Event Library |—-| Event Detector
`+
`I
`
`Events Stack and
`Event Extractor
`\
`DRN'IG EVENT STRIG 105
`
`20:
`
`208 213
`
`l
`
`i
`
`+
`1
`Pattern Recognition Unit
`aneuver Library
`Detector
`pi Maneuver Classi?er HSkillAssessorHittitude Assegl
`
`219
`Km
`
`:15
`
`\
`
`ATTl'l'lDE ‘
`SKILL
`“RMNG
`. . . EMT“ ASSESSMENT ASSESSMENT
`II
`
`235
`
`1:7
`
`239
`
`OWNER Ex. 2025, page 1
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 1 0f 14
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`US 7,389,178 B2
`
`Sensors
`(105
`(103
`RPM Speed
`
`/101
`(107
`Acceleration
`
`(109
`Location
`
`K111
`other
`sensors
`
`113/\
`
`Analysis and Evaluation Unit
`115
`Threshold \_/
`Settings
`
`+
`
`RAW DATA STREAM
`
`Continuous Processing Unit
`
`121
`
`‘
`
`>
`
`Statistical
`
`Unit
`
`119
`J
`
`‘
`
`.
`
`.
`
`.
`
`A
`117 Dlscrimlnator
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`125
`
`STATISTICALLY-PROCESSED DATA
`
`REPORT /
`
`k
`
`f
`
`0 0 0
`
`SESSION
`n
`
`SESSION
`AVERAGE
`
`ROAD-SPECIFI
`SESSION
`
`127
`
`129
`
`131
`
`no. 1 (PRIOR ART)
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`OWNER Ex. 2025, page 2
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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 2 0f 14
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`US 7,389,178 B2
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`Sensors
`(105
`(103
`RPM Speed
`
`[101
`(107
`Acceleration
`
`(109
`Location
`
`/"111
`other
`sensors
`
`[201 {Z02
`
`RAWDATA STREAM
`
`Event Handler 207
`
`Low-Pass Filter
`
`Events Stack and
`
`Event Library
`A
`
`- Event Detector
`)
`203/
`
`Event Extractor
`\
`DRMNG EVENT STRlNG\205
`
`209
`
`208
`
`213
`l
`
`Maneuver\
`Detector
`
`‘
`_
`Maneuver Library
`
`219
`211
`/'2l5
`/ f
`i
`i
`_
`_
`Pattern Recognition Unlt
`
`/ Maneuver Classifier
`Skill Assessor Attitude Assessor
`\
`L217 % DRIVING MANEUVER SEQUENCE {E \221
`223
`Anomaly Detector v/ 225/‘
`ANALYZER
`
`DRMNG
`INCONSISTENCIES
`
`227
`
`ANALYSISAND
`EVALUATIONS
`
`REPORT!
`NOTIFICATION!
`ALARM
`
`229
`
`ATTITUDE
`SKILL
`DRMNG
`, , , SrrUAT'o ASSESSMENT ASSESSMENT
`n
`
`231
`
`233
`
`235
`
`237
`
`239
`
`FIG. 2
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`OWNER Ex. 2025, page 3
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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 3 0f 14
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`US 7,389,178 B2
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`307
`
`\
`
`301
`\
`
`303 \
`
`305
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`309
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`FIG. 3
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`OWNER Ex. 2025, page 4
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 4 0f 14
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`US 7,389,178 B2
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`401
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`403
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`FIG. 4
`
`Filtered Data
`
`as
`N
`25
`II
`15
`IO
`5
`0
`6
`40
`45
`an
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`OWNER Ex. 2025, page 5
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 5 0f 14
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`US 7,389,178 B2
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`403
`
`525
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`527 529 533 535
`
`539
`
`543
`
`i
`i
`‘2
`.3
`
`521
`
`523
`
`531
`
`537
`
`541
`
`“R,
`‘w
`Start: s)
`
`' F'“
`)1
`End.- 8 > Max: M
`
`Min: L
`
`Cross: C
`
`A ,1’
`I‘
`L. Max: j 1.. Flat:
`
`Flat: P
`
`501
`
`503
`
`505
`
`507
`
`509
`
`511
`
`513
`
`515
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`FIG. 5
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`OWNER Ex. 2025, page 6
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 6 0f 14
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`US 7,389,178 B2
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`i
`
`X
`
`Y
`
`601
`
`/
`
`Time
`
`603
`
`FIG. 6
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`OWNER Ex. 2025, page 7
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 7 0f 14
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`US 7,389,178 B2
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`701
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`703
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`707
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`705
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`FIG. 7
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`OWNER Ex. 2025, page 8
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 8 0f 14
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`US 7,389,178 B2
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`801
`
`807
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`809
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`803
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`/
`
`813
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`FIG. 8
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`OWNER Ex. 2025, page 9
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 9 0f 14
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`US 7,389,178 B2
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`901
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`909 913
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`FIG. 9
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`OWNER Ex. 2025, page 10
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 10 0f 14
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`US 7,389,178 B2
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`Begin
`/——®
`1001
`
`1005
`\
`
`—-> Sx —> M): -> Ex - Accelerate
`
`LX+EX - Braking
`
`1003
`
`Done
`b
`
`A
`
`1007
`
`—1009
`
`l—>Sx->Mx—>-My—>-D<+Ey - Turn and Accelerate
`
`\1011
`
`FIG. 10
`
`OWNER Ex. 2025, page 11
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 11 0f 14
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`7,389,178 B2
`US
`
`1101
`
`1103
`
`V Raw Sensor 7 \_
`
`Data Stream
`
`1107
`
`\7/
`
`Event String /
`
`I
`START
`+
`Filter
`Sensor Data Stream
`1105
`+
`\_ Detect Events in
`1109
`Filtered Data Strean
`\ *
`Generate Event Strir
`1111
`+
`\_ Match Event String
`Patterns to Maneuve "
`1115
`+
`\/ Maneuver / K Generate Maneuve
`1117
`Sequence
`1119
`Sequence
`\ *
`Assess Driver Skill
`\7/
`1123
`+
`1121
`\_/ Attitude Rating / \\/ Assess Driver Attitud
`1125
`1127
`+
`\_/ Inconsistency Set / p Anomalies
`Detect Driving
`
`1113
`
`Skill Rating
`
`-
`
`1131
`\_,
`
`Initiate Alert
`
`Analyze and Evaluat
`—————>
`+
`1135
`\_,
`Issue Reports
`
`1139
`\_,
`
`Initiate Alert
`
`DONE
`
`4
`
`FIG. 11
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`OWNER Ex. 2025, page 12
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 12 0f 14
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`US 7,389,178 B2
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`1201
`\ Maneuver
`
`1213
`
`1207
`
`1203 1205
`
`1209
`
`Poorly-Skilled ) K Highly-Skilled
`
`Maneuver
`
`Maneuver
`
`213
`\ Maneuver Library
`
`FIG. 12
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`OWNER Ex. 2025, page 13
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 13 0f 14
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`US 7,389,178 B2
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`1201
`\ Maneuver
`
`1313
`
`1307
`
`1303 1305
`
`Maneuver
`
`Safely-Executed ) KDangerously- Executed
`T
`T
`
`Maneuver
`
`213
`\ Maneuver Library
`
`FIG. 13
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`OWNER Ex. 2025, page 14
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`

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`U.S. Patent
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`Jun. 17, 2008
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`Sheet 14 0f 14
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`US 7,389,178 B2
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`1407
`
`Threshold
`
`1409
`
`1405
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`1401 1403
`
`DRIVING
`INCONSISTENCIES
`
`Maneuver
`
`Characterlstlc Maneuver
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`209
`
`Database
`
`FIG. 14
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`OWNER Ex. 2025, page 15
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`

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`US 7,389,178 B2
`
`1
`SYSTEM AND METHOD FOR VEHICLE
`DRIVER BEHAVIOR ANALYSIS AND
`EVALUATION
`
`The present application claims bene?t of US. Provisional
`Patent Application No. 60/528,522 ?led Dec. 11, 2003.
`
`FIELD OF THE INVENTION
`
`The present invention relates to vehicle monitoring sys
`tems, and methods, and, more particularly, to systems and
`methods for monitoring and evaluating vehicle driver behav
`ior.
`
`BACKGROUND OF THE INVENTION
`
`There are recognized bene?ts in having systems and
`methods to monitor the operation of vehicles, for capturing
`real-time data pertaining to driving activity and patterns
`thereof. Such systems and methods facilitate the collection
`of qualitative and quantitative information related to the
`contributing causes of vehicle incidents, such as accidents;
`and alloW objective driver evaluation to determine the
`quality of driving practices. The potential bene?ts include
`preventing or reducing vehicle accidents and vehicle abuse;
`and reducing vehicle operating, maintenance, and replace
`ment costs. The social value of such devices and systems is
`universal, in reducing the impact of vehicle accidents. The
`economic value is especially signi?cant for commercial and
`institutional vehicle ?eets, as Well as for general insurance
`and risk management.
`There exists a large and growing market for vehicle
`monitoring systems that take advantage of neW technologi
`cal advances. These systems vary in features and function
`ality and exhibit considerable scope in their approach to the
`overall problem. Some focus on location and logistics,
`others on engine diagnostics and fuel consumption, Whereas
`others concentrate on safety management.
`For example, US. Pat. No. 4,500,868 to Tokitsu et al.
`(herein denoted as “Tokitsu) is intended as an adjunct in
`driving instruction. By monitoring a variety of sensors (such
`as engine speed, vehicle velocity, selected transmission gear,
`and so forth), a system according to Tokitsu is able to
`determine if certain predetermined condition thresholds are
`exceeded, and, if so, to signal an alarm to alert the driver.
`Alarms are also recorded for later revieW and analysis. In
`some cases, a simple system such as Tokitsu can be valuable.
`For example, if the driver Were to strongly depress the
`accelerator pedal, the resulting acceleration could exceed a
`predetermined threshold and sound an alarm, cautioning the
`driver to reduce the acceleration. If the driver Were prone to
`such behavior, the records created by Tokitsu’s system
`Would indicate this. On the other hand, Tokitsu’s system is
`of limited value under other conditions. For example, if the
`driver Were to suddenly apply the vehicle brakes With great
`force, the resulting deceleration could exceed a predeter
`mined threshold, and thereby signal an alarm and be
`recorded. Although the records of such behavior could be
`valuable, such strong braking is usually done under emer
`gency conditions Where the driver is already aWare of the
`emergency, and Where an alarm Would be super?uous (and
`hence of little or no value), or perhaps distracting (and hence
`of dubious value or even detrimental).
`US. Pat. No. 4,671,111 to Lemelson (herein denoted as
`“Lemelson 111”) teaches the use of accelerometers and data
`recording/transmitting equipment for obtaining and analyZ
`ing vehicle acceleration and deceleration. Although Lemel
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`son 111 presents this in the context of analyZing vehicle
`performance, hoWever, there is no detailed discussion of
`precisely hoW an analysis of the resulting data Would be
`done, nor hoW meaningful information could be obtained
`thereby. In related US. Pat. No. 5,570,087 also to Lemelson
`(herein denoted as “Lemelson 087”) the analyZed vehicular
`motion is expressed in coded representations Which are
`stored in computer memory. As With Lemelson 111, Which
`does not describe hoW raW data is analyZed to determine
`driving behavior parameters, Lemelson 087 does not
`describe hoW coded representations of raW data or driving
`behavior parameters Would be created or utiliZed. It is
`further noted that US. Pat. No. 5,805,079 to Lemelson
`(herein denoted as “Lemelson 079”) is a continuation of
`Lemelson 087 and contains no neW or additional descriptive
`material.
`US. Pat. No. 5,270,708 to Kamishima (herein denoted as
`“Kamishima”) discloses a system that detects a vehicle’s
`position and orientation, turning, and speed, and coupled
`With a database of past accidents at the present location,
`determines Whether the present vehicle’s driving conditions
`are similar to those of a past accident, and if so, alerts the
`driver. If, for example, the current vehicle speed on a
`particular road exceeds the (stored) speed limit at that point
`of the road, the driver could be alerted. Moreover, if exces
`sive speed on that particular area is knoWn to have been
`responsible for many accidents, the system could notify the
`driver of this. The usefulness of such a system, hoWever,
`depends critically on having a base of previous data and
`being able to associate the present driving conditions With
`the stored information. The Kamishima system, in particu
`lar, does not analyZe driving behavior in general, nor draW
`any general conclusions about the driver’s patterns in a
`location-independent manner.
`US. Pat. No. 5,546,305 to Kondo (herein denoted as
`“Kondo”) performs an analysis on raW vehicle speed and
`acceleration, engine rotation, and braking data by time
`dilferentiating the raW data and applying threshold tests.
`Although such an analysis can often distinguish betWeen
`good driving behavior and erratic or dangerous driving
`behavior (via a driving “roughness” analysis), time-differ
`entiation and threshold detection cannot by itself classify
`raW data streams into the familiar patterns that are normally
`associated With driving. Providing a count of the number of
`times a driver exceeded a speed threshold, for example, may
`be indicative of unsafe driving, but such a count results in
`only a vague sense of the driver’s patterns. On the other
`hand, a context-sensitive report that indicates the driver
`repeatedly applies the brake during turns Would be far more
`revealing of a potentially-dangerous driving pattern. Unfor
`tunately, hoWever, the analysis performed by Kondo, Which
`is typical of the prior art analysis techniques, is incapable of
`providing such context-sensitive information. (See “Limita
`tions of the Prior Art” beloW.)
`US. Pat. No. 6,060,989 to Gehlot (herein denoted as
`“Gehlot”) describes a system of sensors Within a vehicle for
`determining physical impairments that Would interfere With
`a driver’s ability to safely control a vehicle. Speci?c physi
`cal impairments illustrated include intoxication, fatigue and
`droWsiness, or medicinal side-effects. In Gehlot’s system,
`sensors monitor the person of the driver directly, rather than
`the vehicle. Although this is a useful approach in the case of
`physical impairments (such as those listed above), Gehlot’s
`system is ineffective in the case of a driver Who is simply
`unskilled or Who is driving recklessly, and is moreover
`incapable of evaluating a driver’s normal driving patterns.
`
`OWNER Ex. 2025, page 16
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`US 7,389,178 B2
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`3
`Us. Pat. No. 6,438,472 to Tano, et al. (herein denoted as
`“Tano”) describes a system Which analyzes raW driving data
`(such as speed and acceleration data) in a statistical fashion
`to obtain statistical aggregates that can be used to evaluate
`driver performance. Unsatisfactory driver behavior is deter
`mined When certain prede?ned threshold values are
`exceeded. A driver Whose behavior exceeds a statistical
`threshold from What is considered “safe” driving, can be
`deemed a “dangerous” driver. Thresholds can be applied to
`various statistical measures, such as standard deviation.
`Because Tano relies on statistical aggregates and thresholds
`Which are acknowledged to vary according to road location
`and characteristics, hoWever, a system according to Tano has
`limited ability to evaluate driver performance independent
`of the statistical pro?les and thresholds. In particular, the
`statistical characterization of a driver’s performance is gen
`erally not expressible in terms of familiar driving patterns.
`For example, a driver may have a statistical pro?le that
`exceeds a particular lateral acceleration threshold, and the
`driver may therefore be classi?ed as a “dangerous” driver.
`But What driving pattern is responsible for excessive lateral
`acceleration? Is it because this driver tends to take curves too
`fast? Or is it because he tends to change lanes rapidly While
`Weaving in and out of traf?c? Both are possibly “dangerous”
`patterns, but a purely threshold-oriented statistical analysis,
`such as presented in Tano, may be incapable of discrimi
`nating betWeen these, and therefore cannot attribute the
`resulting statistical pro?le to speci?c patterns of driving. As
`noted for Kondo’s analysis (above), Tano’s statistical analy
`sis is also incapable of providing information in terms of
`familiar driving patterns.
`In addition to the above issued patents, there are several
`commercial products currently available for monitoring
`vehicle driving behavior. The “Mastertrak” system by
`Vetronix Corporation of Santa Barbara, Calif. is intended as
`a ?eet management system Which provides an optional
`“safety module”. This feature, hoWever, addresses only
`vehicle speed and safety belt use, and is not capable of
`analyZing driver behavior patterns. The system manufac
`tured by SmartDriver of Houston, Tex. monitors vehicle
`speed, accelerator throttle position, engine RPM, and can
`detect, count, and report on the exceeding of thresholds for
`these variables. Unfortunately, hoWever, there are various
`driving patterns Which cannot be classi?ed on the basis of
`thresholds, and Which are nevertheless pertinent to detecting
`questionable or unsafe driving behavior. For example, it is
`generally acknowledged that driving too sloWly on certain
`roads can be haZardous, and for this reason there are often
`minimum speed limits. Driving beloW a minimum speed,
`hoWever, is not readily detectable by a system such as
`SmartDriver, because introducing a loW-speed threshold
`results in such a large number of false reports (When the
`vehicle is driven sloWly in an appropriate location) that
`collecting such data is not normally meaningful.
`
`Limitations of the Prior Art
`Collecting raW physical data on vehicle operation through
`a multiplicity of sensors usually results in a very large
`quantity of data Which is cumbersome to store and handle,
`and impractical to analyZe and evaluate. For this reason, any
`automated system or method of driver behavior analysis and
`evaluation must employ some abstraction mechanism to
`reduce the data to a manageable siZe and in a meaningful
`Way.
`For the prior art, as exempli?ed by the speci?c instances
`cited above, this is done through statistical processing and
`the use of predetermined thresholds, supplemented in some
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`cases by limited continuous pre-processing (e.g., time-dif
`ferentiation), optionally correlated in some cases With avail
`able history or other data on the location Where the driving
`is being done. As a result, prior art systems and methods are
`generally limited to providing aggregate and statistically
`processed overvieWs of driver performance. This is
`expressed succinctly in Lemelson 111: “The computer
`analysis may determine the manner in Which the vehicle is
`driven, either during a speci?c time interval or a number of
`time intervals or over a longer period of time Wherein
`averaging is employed to determine the general performance
`or use of the vehicle”(column 1 lines 21-26). That is, prior
`art analysis and evaluation is based on overall performance
`during a particular driving session, or is based on statistical
`averages over a number of different sessions. In limited
`cases, the analysis and evaluation can be made With regard
`to a particular road or road segment, through the application
`of GPS locating.
`FIG. 1 illustrates the general prior art analysis and evalu
`ation approach. Atypical set of sensors 101 has a tachometer
`103, a speedometer 105, one or more accelerometers 107, a
`GPS receiver 109, and optional additional sensors 111. In the
`case of accelerometers, it is understood that an accelerom
`eter is typically operative to monitoring the acceleration
`along one particular speci?ed vehicle axis, and outputs a raW
`data stream corresponding to the vehicle’s acceleration
`along that axis. Typically, the tWo main axes of vehicle
`acceleration that are of interest are the longitudinal vehicle
`axisithe axis substantially in the direction of the vehicle’s
`principal motion (“forWard” and “reverse”); and the trans
`verse (lateral) vehicle axisithe substantially horiZontal axis
`substantially orthogonal to the vehicle’s principal motion
`(“side-to-side”). An accelerometer Which is capable of
`monitoring multiple independent vector accelerations along
`more than a single axis (a “multi-axis” accelerometer) is
`herein considered as, and is denoted as, a plurality of
`accelerometers, Wherein each accelerometer of the plurality
`is capable of monitoring the acceleration along only a single
`axis. Additional sensors can include sensors for driver
`braking pressure, accelerator pressure, steering Wheel con
`trol, handbrake, turn signals, and transmission or gearbox
`control, clutch (if any), and the like. Some of the sensors,
`such as tachometer 103 and speedometer 105 may simply
`have an analog signal output Which represents the magnitude
`of the quantity. Other sensors, such as a transmission or
`gearbox control sensor may have a digital output Which
`indicates Which gear has been selected. More complex
`output Would come from GPS receiver 109, according to the
`formatting standards of the manufacturer or industry. Other
`sensors can include a real-time clock, a directional device
`such as a compass, one or more inclinometers, temperature
`sensors, precipitation sensors, available light sensors, and so
`forth, to gauge actual road conditions and other driving
`factors. Digital sensor output is also possible, Where sup
`ported. The output of sensor set 101 is a stream of raW data,
`in analog and/or digital form.
`Sensor outputs are input into an analysis and evaluation
`unit 113, Which has threshold settings 115 and a threshold
`discriminator 117. A statistical unit 119 provides report
`summaries, and an optional continuous processing unit 121
`may be included to preprocess the raW data. The output of
`analysis and evaluation unit 113 is statistically-processed
`data.
`A report/noti?cation/alarm 123 is output With the results
`of the statistical analysis, and may contain analysis and
`evaluations of one or more of the folloWing: an emergency
`alert 125, a driving session 1 statistics report 127, a driving
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`US 7,389,l78 B2
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`5
`session 2 statistics report 129, etc., and a driving session n
`statistics report 131, a driving session average statistics
`report 133, and a road-speci?c driving session statistics
`report 135.
`These reports may be useful in analyzing and evaluating
`driver behavior, skill, and attitude, but the use of statistics
`based predominantly on thresholds or on localiZation of the
`driving, and the aggregation over entire driving sessions or
`groups of driving sessions also result in the loss of much
`meaningful information.
`In particular, the details of the driver’s behavior in speci?c
`driving situations are not available. Familiar driving situa
`tions, such as passing, lane changing, tra?ic blending, mak
`ing turns, handling intersections, handling olf- and on
`ramps, driving in heavy stop- and-go traf?c, and so forth,
`introduce important driving considerations. It is evident that
`the aggregate statistics for a given driver in a given driving
`session depend critically on the distribution and mix of these
`situations during that given session.
`For example, the same driver, driving in a consistent
`manner but handling different driving situations may exhibit
`completely different driving statistics. One of the key ben
`e?ts of monitoring driving behavior is the ability to deter
`mine a driver’s consistency, because this is an important
`indicator of that driver’s predictability, and therefore of the
`safety of that driver’s performance. If the driver begins to
`deviate signi?cantly from an established driving pro?le, this
`can be a valuable advance warning of an unsafe condition.
`Perhaps the driver is fatigued, distracted, or upset, and
`thereby poses a haZard Which consistency analysis can
`detect. It is also possible that the driver has been misiden
`ti?ed and is not the person thought to be driving the vehicle.
`Unfortunately, hoWever, statistically aggregating data, as is
`done in the prior art, does not permit a meaningful consis
`tency analysis, because such an analysis depends on the
`particular driving situations Which are encountered, and
`prior art analysis completely ignores the speci?cs of those
`driving situations.
`Atypical prior art report presents information such as: the
`number of times a set speed limit Was exceeded; the maxi
`mum speed; the number of times a set RPM limit Was
`exceeded; the maximum lateral acceleration or braking
`deceleration; and so forth. Such information may be char
`acteristic of the driver’s habits, but it Would be much better
`to have a report that is based on familiar driving situations,
`maneuvers, and patternsifor example, by revealing that the
`driver has a habit of accelerating during turns, or makes
`frequent and rapid high-speed lane changes.
`As another example of the limitations of the prior art, a
`neW and relatively inexperienced driver might drive very
`cautiously and thereby have very “safe” overall statistics,
`but might lack skills for handling certain common but more
`challenging driving situations. An experienced driver, hoW
`ever, might exhibit What appear to be more “dangerous”
`overall statistics, but might be able to handle those chal
`lenging driving situations much better and more safely than
`the neW driver. Prior art analysis systems and methods,
`hoWever, might erroneously deduce that the more experi
`enced driver poses the greater haZard, Whereas in reality it
`is the apparently “safer” driver Who should be scrutinized
`more carefully.
`There is thus a need for, and it Would be highly advan
`tageous to have, a method and system for analyZing a raW
`vehicle data stream to determine the corresponding sequence
`of behavior and characteristics of the vehicle’s driver in the
`context of familiar driving situations, and for expressing
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`driving behavior and characteristics in terms of familiar
`driving patterns and maneuvers. This goal is met by the
`present invention.
`
`SUMMARY OF THE INVENTION
`
`The present invention is of a system and method for
`analyZing and evaluating a raW data stream related to the
`operation of a vehicle for the purpose of classifying and
`rating the performance of the vehicle’s driver. Unlike prior
`art systems and methods, embodiments of the present inven
`tion are not restricted to performing statistical and threshold
`analysis and evaluation of the driver’s skills and behavior,
`but can express an analysis and evaluation in terms of
`familiar driving patterns and maneuvers, thereby yielding
`analyses and evaluations Which contain more information
`and Which are more readily put to use.
`According to embodiments of the present invention, the
`raW data stream from the vehicle sensors is progressively
`analyZed to obtain descriptors of the driving operations
`Which are less and less “data” and more and more expressive
`of familiar driving operations and situations. An objective of
`the present invention is to identify the context in Which each
`event takes place. For example, braking suddenly is de?ned
`as an event, and the context of such an event may be the
`making of a turn Which Was entered at too high a speed.
`It is thus an objective of the present invention to identify
`events in the context of driving situations. Whereas prior art
`solutions perform statistical analysis according to threshold
`levels of measurable variables (such as counting the number
`of times a driver exceeds a particular speed), it is a goal of
`the present invention to recogniZe common patterns of
`driving situations (such as lane changing) and to associate
`other events With a context (such as increasing speed during
`a lane change) Which may represent unsafe driving behavior.
`It is an objective of the present invention to facilitate the
`classi?cation of a driver’s skill on the basis of sensor
`measurements of the driven vehicle. It is also an objective of
`the present invention to facilitate the classi?cation of a
`driver’s attitude on the basis of sensor measurements of the
`driven vehicle, Where the term “attitude” herein denotes the
`driver’s approach toWard driving and the tendency of the
`driver to take risks. Categories include, but are not limited
`to: “safe” (or “normal”); “aggressive” (or “risky”); “thrill
`seeking”; “abusive”; and “dangerous”. In an embodiment of
`the present invention, aggressive or dangerous behavior is
`logged as an event.
`It is moreover an objective of the present invention to
`enable the making of quantitative and qualitative compari
`sons betWeen a current driver’s behavior and a previous
`pro?le of the same driver, independent of the particular
`details of the driving sessions involved, by qualifying and
`quantifying the driver’s behavior When performing common
`driving maneuvers in common driving situations.
`Therefore, according to the present invention there is
`provided a system for analyZing and evaluating the perfor
`mance and behavior of the driver of a vehicle, the system
`including: (a) a vehicle sensor operative to monitor the state
`of the vehicle and to output a raW data stream corresponding
`thereto; (b) a driving event handler operative to detect
`driving events based on the raW data stream and to output a
`driving event string corresponding thereto, the driving event
`string containing at least one driving event symbol corre
`sponding to a driving event; and (c) a maneuver detector
`operative to recogniZe patterns of driving maneuvers and to
`construct and output a driving maneuver sequence corre
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`US 7,389,178 B2
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`7
`sponding thereto, the driving maneuver sequence containing
`at least one driving maneuver.
`In addition, according to the present invention there is
`also provided a method for analyzing and evaluating the
`performance and behavior of the driver of a vehicle, based
`on a raW data stream from a set of sensors operative to
`monitor the state of the vehicle, the method including: (a)
`detecting driving events represented by the raW data stream,
`and generating a driving event string therefrom; and (b)
`matching patterns in the driving event string to detect
`driving maneuvers therein, and generating a driving maneu
`ver sequence therefrom.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`The invention is herein described, by Way of example
`only, With reference to the accompanying draWings,
`Wherein:
`FIG. 1 conceptually illustrates prior art analysis and
`evaluation of vehicle driving data.
`FIG. 2 is a block diagram of a system according to an
`embodiment of the present invention.
`FIG. 3 is an example of a graph of a raW data stream from
`multiple vehicle accelerometers.
`FIG. 4 is an example of the ?ltering of the raW data stream
`to remove noise, according to the present invention.
`FIG. 5 is an example of parsing a ?ltered data stream to
`derive a string of driving events, according to the present
`invention.
`FIG. 6 shoWs the data and event string analysis for a “lane
`change” driving maneuver, according to the present inven
`tion.
`FIG. 7 shoWs the data and event string analysis for a
`“tum” driving maneuver, according to the present invention.
`FIG. 8 shoWs the data and event string analysis for a
`“braking Within turn” driving maneuver, according to the
`present invention.
`FIG. 9 shoWs the data and event string analysis for an
`“accelerate Within turn” driving maneuver, according to the
`present invention.
`FIG. 10 shoWs a non-limiting illustrative example of
`transitions of a ?nite state machine for identifying driving
`maneuvers, according to an embodiment of the present
`invention.
`FIG. 11 is a ?owchart of a method for analyZing and
`evaluating vehicle driver performance according to an
`embodiment of the present invention.
`FIG. 12 is a conceptual block diagram of an arrangement
`for assessing driver skill according to an embodiment of the
`present invention.
`FIG. 13 is a conceptual block diagram of an arrangement
`for assessing driver attitude according to an embodiment of
`the present invention.
`FIG. 14 is a conceptual block diagram of an arrangement
`for determining Whether there is a signi?cant anomaly in the
`current driver’s behav