(19) United States
`(12) Patent Application Publication (10) Pub. No.: US 2006/0092043 A1
`Lagassey
`(43) Pub. Date:
`May 4, 2006
`
`US 20060092043A1
`
`(54) ADVANCED AUTOMOBILE ACCIDENT
`DETECTION, DATA RECORDATION AND
`REPORTING SYSTEM
`
`(76) Inventor: Paul J. Lagassey, Vero Beach, FL (US)
`Correspondence Address:
`MILDE & HOFFBERG, LLP
`1O BANK STREET
`SUTE 460
`WHITE PLAINS, NY 10606 (US)
`(21) Appl. No.:
`11/267,732
`
`(22) Filed:
`
`Nov. 3, 2005
`Related U.S. Application Data
`(60) Provisional application No. 60/522,749, filed on Nov.
`3, 2004.
`
`Publication Classification
`
`(51) Int. Cl.
`G08G I/095
`
`(2006.01)
`
`(52) U.S. Cl. .............................................................. 340/907
`
`(57)
`
`ABSTRACT
`
`A system for monitoring a location to detect and report a
`vehicular incident, comprising a transducer for detecting
`acoustic waves at the location, and having an audio output;
`a processor for determining a probable occurrence or
`impending occurrence of a vehicular incident, based at least
`upon said audio output; an imaging system for capturing
`images of the location, and having an image output; a buffer,
`receiving said image output, and storing at least a portion of
`said images commencing at or before said determination;
`and a communication link, for selectively communicating
`said portion of said images stored in said buffer with a
`remote location and at least information identifying the
`location, wherein information stored in said buffer is pre
`served at least until an acknowledgement of receipt is
`received representing Successful transmission through said
`communication link with the remote location.
`
`100 N
`
`40
`
`
`
`45
`
`
`
`SEE : B
`
`
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`Exhibit 1011
`Page 01 of 29
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`Patent Application Publication May 4, 2006 Sheet 1 of 6
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`US 2006/0092043 A1
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`S.
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`f
`s
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`Exhibit 1011
`Page 02 of 29
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`Patent Application Publication May 4, 2006 Sheet 2 of 6
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`US 2006/0092043 A1
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`(N
`S. N
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`O
`O
`ve
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`Exhibit 1011
`Page 03 of 29
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`Patent Application Publication May 4, 2006 Sheet 3 of 6
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`US 2006/0092043 A1
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`
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`Control Unit Active and Receiving incoming Audio and Video Signals 51
`
`Store. At Least Video Data and Other Desired Accident Related Data in a
`Circular Buffer White 52
`
`NO
`
`
`
`incoming Audio Signals for Match to
`Stored Acoustic Signature of Qualifying Sound to Determine if
`Qualifying Sound is Present?
`
`Yes
`Stop Overwriting and Preserve Data in Circular Buffer 54
`
`Continue to Save at Least Subsequent Video Data and Other Desired
`ACCident-Related Data 55
`
`Initiate Contact With Monitoring Center 75
`
`Contact Established 76
`
`
`
`No
`
`Yes
`Transmit Location Data and At Least One image to Monitoring Center 77
`
`Continue Saving Desired Accident Related Data 78
`Yes
`
`ime Limit Passed,
`emory Limit Been Reached, or Termination Signal Received
`79
`
`Yes
`Stop Saving Accident-Related Data 80
`
`
`
`Transmit Accident-Related Data 81
`
`Uccessful Transmission
`Verified 82
`Yes
`End Transmission 85
`
`Flush and Reuse allocated memory 90
`
`Fig. 3
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`
`
`
`
`O
`
`No
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`No
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`Exhibit 1011
`Page 04 of 29
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`Patent Application Publication May 4, 2006 Sheet 4 of 6
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`US 2006/0092043 A1
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`START
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`NO
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`Receive Audio And Video Signals, Time And
`LOCation Data 50
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`NO
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`Audio Signals For Match
`Stored Acoustic Signature
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`YES
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`Continue Saving And Analyze
`Subsequent Audio Signals For
`Match To Qualifying Sound 61
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`Sound Detected Within Fif
`Predetermined Time?
`
`NO
`
`Transmit Location Data And At
`Least One Image To Monitoring
`Center 77
`
`econd Predetermined
`Time Passed, Storage
`Capacity Been Reached, Or
`Termination Signal
`
`Stop Saving Accident-Related Data
`80
`
`Transmit Or Upload Accident
`Related Data 81
`
`Successful
`Transmission Or Upload
`Verified 82
`
`YES
`Flush Buffer 90
`
`Reinitialize (Optional) 99
`Fig. 4
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`Exhibit 1011
`Page 05 of 29
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`Patent Application Publication May 4, 2006 Sheet 5 of 6
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`US 2006/0092043 A1
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`27 A/S
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`A /
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`Exhibit 1011
`Page 06 of 29
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`Patent Application Publication May 4, 2006 Sheet 6 of 6
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`US 2006/0092043 A1
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`Detect acoustic waves at the location 301
`
`Analyze conditions at location 302
`Determine a likely occurrence or an imminent occurrence of a vehicular accident or
`Other incident 303
`y
`Optionally determine compliance with traffic control regulations 304
`Capture initial images of the location along with audio, timecode, state of traffic signal,
`GPS code, optionally polling a plurality of cameras 305
`- Y - -
`Store data, starting no later than the determination of likely occurrence 306
`v
`Optionally Communicate Location and At Least One image 307
`Continue to capture stream of images of the location along with audio, timecode, state
`of traffic signal, GPS code until cease condition 308
`Optionally use sensor data to model location 309
`Optionally communicate to or from traffic signal control device 310
`
`-
`
`Establish communication pathway and communicate the stored
`images and incident related data to a remote location 311
`
`
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`
`
`erification of Successfu
`Communication?
`312
`
`
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`
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`Retry and/or try
`alternate Communication
`pathway 313
`
`Yes
`Preserve stored information at least until verification of successful Communication, and
`then delete 314
`
`Receive and display information from a plurality of locations at a remote monitoring
`center on a map 315
`
`Route information to an available live agent at a remote monitoring Center,
`coordinate multiple communications 316
`Preserve information in a forensically reliable record 317
`
`
`
`Communicate from remote monitoring center to location with audio Communications,
`to control and program traffic signal control device, control and program the
`components of the system, and to activate visual alert 318
`Fig. 6
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`Exhibit 1011
`Page 07 of 29
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`

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`US 2006/0092043 A1
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`May 4, 2006
`
`ADVANCED AUTOMOBILE ACCIDENT
`DETECTION, DATA RECORDATION AND
`REPORTING SYSTEM
`
`CROSS REFERENCE TO RELATED
`APPLICATION
`0001. The present application claims benefit of priority
`from U.S. Provisional Patent Application 60/522,749 filed
`Nov. 3, 2004.
`
`BACKGROUND OF THE INVENTION
`0002 The invention generally relates to an automobile
`accident detection and data recordation and reporting sys
`tem, and in particular to a system which detects accidents
`based on a set of characteristic sounds or other cues.
`0003 Traffic accidents cause significant costs in terms of
`direct loss, consequential loss, and Societal loss due to
`obstruction of the roadway in the aftermath of an accident.
`Another issue is the allocation of direct costs, for example
`when more than one vehicle is involved, the vehicle at fault
`is generally held liable for the damages.
`0004. It is possible to monitor locations that are likely
`places for accidents to occur, however, without intelligence,
`this process may be inefficient and unproductive. Likewise,
`without immediate and efficient communication of the infor
`mation obtained, benefits of the monitoring are quite limited.
`0005 Since cellular telephone technology has become so
`widely adopted, the most common means by which motor
`vehicle accidents are reported to agencies in the U.S. is
`through cellular telephones. However, this is not always
`reliable or immediate if the victims are unable to use their
`cellular phones or if there are no witnesses with cellular
`phones to report the accident, and it fails to record an actual
`record of the accident which can later be used as evidence.
`0006 Automobile accident detection systems are com
`mon in the art. Upon the occurrence of an automobile
`accident, it may be desirable to obtain video images and
`sounds of the accident and to record the time of the accident
`and the status of the traffic lights at the time the accident
`occurred. This information can then be sent to a remote
`location where emergency crews can be dispatched and the
`information further examined and forwarded to authorities
`in order to determine fault and liability.
`0007. A number of prior art techniques are available for
`predicting the occurrence of an accident. Some of these
`require an extended period of time for an automated system
`to analyze the data, and thus any report generated is Sub
`stantially delayed. In others, the accuracy of the system
`depends on environmental conditions, such as lighting or
`time of day. Therefore, in order to provide an immediate and
`reliable response to a predicted occurrence of an accident,
`Such techniques are Suboptimal.
`0008 For example, Japanese Patent Application No.
`8-162911 entitled “Motor Vehicle Accident Monitoring
`Device” (“the Japanese reference”), expressly incorporated
`herein by reference in its entirety, discloses a system for
`monitoring traffic accidents including a plurality of micro
`phones and video cameras disposed at an intersection.
`Collision sounds are chosen from among the typical Sounds
`at an intersection. The Source of the collision Sounds is
`
`determined by comparing the time differences of the sounds
`received by each of the microphones. Image data from the
`cameras is recorded upon the occurrence of the collision.
`However, the Japanese reference discloses a system that is
`constantly photographing the accident scene thereby wast
`ing video resources.
`0009 U.S. Pat. No. 6,141,611 issued to Mackey et al.
`entitled “Mobile Vehicle Accident Data System” (“the
`Mackey reference”), expressly incorporated herein by ref
`erence in its entirety, discloses an on-board vehicle accident
`detection system including one or more video cameras that
`continuously record events occurring at a given scene.
`Camera images of the scene are digitally stored after com
`pression. An accident detector on-board the vehicle deter
`mines if an accident has occurred, and if so, the stored
`images are transmitted to a remote site for observation.
`However, the Mackey reference includes video cameras
`on-board the vehicles themselves, increasing the likelihood
`that the cameras would become damaged during an accident
`thereby rendering them impractical for accident-recording
`systems. Further, the on-board cameras image-capturing
`ability is severely limited due to the constraints of the
`vehicle themselves. Additionally, the Mackey reference dis
`closes a system that determines if an accident is present by
`the sudden acceleration or deceleration of the vehicle, with
`out the use of fixed microphones. The invention claimed by
`Mackey is on board the vehicle, it does nothing to solve the
`problem or record an accident in two vehicles which are not
`so equipped. Equipping every vehicle with this system is
`impractical and therefore not feasible.
`0010 U.S. Pat. No. 6,111,523 issued to Mee entitled
`“Method and Apparatus for Photographing Traffic in an
`Intersection', expressly incorporated herein by reference in
`its entirety, describes a system for taking photographs of
`vehicles at a traffic intersection by triggering a video camera
`to capture images wherein the triggering mechanism of the
`Video camera is based upon certain vehicle parameters
`including the speed of the vehicle prior to its entrance into
`the traffic intersection.
`0011 U.S. Pat. No. 6,088,635 issued to Cox et al. entitled
`“Railroad Vehicle Accident Video Recorder, expressly
`incorporated herein by reference in its entirety, discloses a
`system for monitoring the status of a railroad vehicle prior
`to a potential accident. The system employs a video camera
`mounted within the railroad car that continuously views the
`status of a given scene, and continuously stores the images
`of the scene. Like Mackey, it is impractical and therefore not
`feasible to equip every vehicle with this system.
`0012 U.S. Pat. No. 5,717.391 issued to Rodriguez
`entitled “Traffic Event Recording Method and Apparatus',
`expressly incorporated herein by reference in its entirety,
`describes a system for determining the condition of a traffic
`light and includes an audio sensor which monitors sound at
`all times. Sound detected above a certain decibel level
`triggers the recordation of Sounds, the time of day and the
`status of the traffic lights. However, Rodriguez fails to
`disclose video cameras or any image-capturing means.
`0013 U.S. Pat. No. 5,677,684 issued to McArthur
`entitled “Emergency Vehicle Sound-Actuated Traffic Con
`troller, expressly incorporated herein by reference in its
`entirety, describes a traffic controller system utilizing Sound
`detection means connected to a control box which contains
`
`Exhibit 1011
`Page 08 of 29
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`

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`US 2006/0092043 A1
`
`May 4, 2006
`
`a Switching mechanism that, in a first orientation, allows
`normal operation of traffic light control and a second ori
`entation that, upon the detection of an approaching siren,
`sets all traffic signals at an intersection to red to prohibit the
`entrance into the intersection of additional vehicles.
`0014 U.S. Pat. No. 5,539,398 issued to Halletal. entitled
`“GPS-based Traffic Control Preemption System”, expressly
`incorporated herein by reference in its entirety, discloses a
`system for determining if a vehicle issuing a preemption
`request to an emergency vehicle or police car is within an
`allowed approach of a traffic intersection, utilizing a GPS
`system.
`0015 U.S. Pat. No. 6,690,294 issued to Zierden entitled
`“System and method for detecting and identifying traffic law
`violators and issuing citations', expressly incorporated
`herein by reference, discloses a mobile or stationary traffic
`monitoring system for detecting violations of speed limits or
`other traffic laws by vehicle operators and issuing citations
`to an operator and/or vehicle owner Suspected of a violation
`using a digital camera to capture images of the operator
`and/or the vehicle, transmitting the captured images and
`other relevant data to an analysis center where the images
`and data are analyzed to determine whether to issue a
`citation and, if so, to issue the citation or take other appro
`priate law enforcement measures. The system captures
`images of a vehicle and/or vehicle operator Suspected of a
`traffic violation, determines the time and geographic loca
`tion of the Suspected violation, transmits the images and
`other data to an analysis center, issues citations to violators
`and derives revenue therefrom.
`0016 U.S. Pat. No. 5,938,717 to Dunne et al., expressly
`incorporated herein by reference, discloses a traffic control
`system that automatically captures an image of a vehicle and
`speed information associated with the vehicle and stores the
`image and information on a hard disk drive. The system uses
`a laser gun to determine whether a vehicle is speeding. The
`hard drive is later connected to a base station computer
`which is, in turn, connected to a LAN at which the infor
`mation from the hard drive is compared with databases
`containing data such as vehicle registration information and
`the like. The system automatically prints a speeding citation
`and an envelope for mailing to the registered owner of the
`vehicle
`0017 U.S. Pat. No. 5,734,337 to Kupersmit, expressly
`incorporated herein by reference, discloses a stationary
`traffic control method and system for determining the speed
`of a vehicle by generating two images of a moving vehicle
`and calculating the vehicle speed by determining the dis
`tance traveled by the vehicle and the time interval between
`the two images. The system is capable of automatically
`looking up vehicle ownership information and issuing cita
`tions to the owner of a vehicle determined to be speeding.
`0018 U.S. Pat. No. 5,948,038 to Daly et al., expressly
`incorporated herein by reference, discloses a method for
`processing traffic violation citations. The method includes
`the steps of determining whether a vehicle is violating a
`traffic law, recording an image of the vehicle committing the
`violation, recording deployment data corresponding to the
`violation, matching the vehicle information with vehicle
`registration information to identify the owner, and providing
`a traffic violation citation with an image of the vehicle, and
`the identity of the registered owner of the vehicle.
`
`0019. The I-95 Corridor Coalition, Surveillance Require
`ments/Technology, Ch. 4. Technology Assessment,
`expressly incorporated herein by reference, describes a
`number of different technologies suitable for incident detec
`tion. For example, AutoAlert: Automated Acoustic Detec
`tion of Traffic Incidents, was an IVHS-IDEA project which
`uses military acoustic sensor technologies, e.g., AT&T IVHS
`NET-2000TM. The AutoAlert system monitors background
`traffic noise and compares it with the acoustic signatures of
`previously recorded accidents and incidents for detection.
`See, David A. Whitney and Joseph J. Pisano (TASC, Inc.,
`Reading, Mass.), “AutoAlert: Automated Acoustic Detec
`tion of Incidents'. IDEA Project Final Report, Contract
`ITS-19, IDEA Program, Transportation Research Board,
`National Research Council, Dec. 26, 1995, expressly incor
`porated herein by reference. The AutoAlert system employs
`algorithms which provide rapid incident detection and high
`reliability by applying statistical models, including Hidden
`Markov Models (HMM) and Canonical Variates Analysis
`(CVA). These are used to analyze both short-term and
`time-varying signals that characterize incidents.
`0020. The Smart Call Box project (in San Diego, Calif.)
`evaluated the use of the existing motorist aid call box system
`for other traffic management strategies. The system tests the
`conversion of existing cellular-based call boxes to multi
`functional IVHS system components, to transmit the data
`necessary for traffic monitoring, incident detection, hazard
`ous weather detection, changeable message sign control, and
`CCTV control.
`0021. In 1992 the French Toll Motorway Companies
`Union initiated testing an Automatic Incident Detection
`(AID) technique proposed by the French National Institute
`for Research on Transportation and Security (INRETS). The
`technique consists of utilizing computers to analyze video
`images received by television cameras placed along the
`roadway. A “mask’ frames the significant part of the image,
`which typically is a three or four-lane roadway and the
`emergency shoulder. The computer processes five pictures a
`second, compares them two at a time, and analyzes them
`looking for points that have moved between two Successive
`pictures. These points are treated as objects moving along
`the roadway. If a moving object stops and remains stopped
`within the mask for over 15 seconds, the computer considers
`this an anomaly and sets off an alarm. In 1993, as part of the
`European MELYSSA project, the AREA Company con
`ducted a full scale test over an urban section of the A43
`motorway located east of Lyons. The roadway was equipped
`with 16 cameras on 10 meter masts or bridges with focal
`distances varying from 16 to 100 km, and fields of detection
`oscillating between 150 and 600 meters. Image Processing
`and Automatic Computer Traffic Surveillance (IMPACTS) is
`a computer system for automatic traffic Surveillance and
`incident detection using output from CCTV cameras. The
`algorithm utilized by the IMPACTS system takes a different
`approach from most other image processing techniques that
`have been applied to traffic monitoring. Road space and how
`it is being utilized by traffic is considered instead of iden
`tifying individual vehicles. This leads to a qualitative
`description of how the road, within a CCTV image, is
`occupied in terms of regions of empty road or moving or
`stationary traffic. The Paris London Evaluation of Integrated
`ATT and DRIVE Experimental Systems (PLEIADES) is
`part of the DRIVE Research Programme. The Automatic
`Traffic Surveillance (ATS) system has been installed into
`
`Exhibit 1011
`Page 09 of 29
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`US 2006/0092043 A1
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`May 4, 2006
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`Maidstone Traffic Control Center and provides information
`on four separate CCTV images. This information will be
`used both in the Control Center and passed onto the Traffic
`Information Center via the PLEIADES Information Con
`troller (PIC) and data communications link. Instead of
`remote PCs there is a duplicate display of the Engineers
`workstation that is shown in the Control Office on a single
`computer monitor. The ATS system communicates data at
`regular intervals to the PIC. Any alarms that get raised or
`cleared during normal processing will get communicated to
`the PIC as they occur. The PIC uses the information received
`to display a concise picture of a variety of information about
`the highway region. The ATS system uses video from CCTV
`cameras taken from the existing Control Office Camera
`Multiplex matrix, while not interfering with its normal
`operation. When a camera is taken under manual control, the
`processing of the data for that image is Suspended until the
`camera is returned to its preset position.
`0022 Navaneethakrishnan Balraj, “Automated Accident
`Detection. In Intersections Via Digital Audio Signal Process
`ing' (Thesis, Mississippi State University, December 2003),
`expressly incorporated herein by reference, discusses, inter
`alia, feature extraction from audio signals for accident
`detection. The basic idea of feature extraction is to represent
`the important and unique characteristics of each signal in the
`form of a feature vector, which can be further classified as
`crash or non-crash using a statistical classifier or a neural
`network. Others have tried using wavelet and cepstral trans
`forms to extract features from audio signals such as speech
`signals. S. Kadambe, G. F. Boudreaux-Bartels, “Application
`of the wavelet transform for pitch detection of speech
`signals.” IEEE Trans. on Information Theory, vol. 38, no. 2,
`part 2, pp. 917-924, 1992: C. Harlow and Y. Wang, “Auto
`mated Accident Detection.” Proc. Transportation Research
`Board 80th Annual Meeting, pp. 90-93, 2001. Kadambe etal
`developed a pitch detector using a wavelet transform. One of
`the main properties of the dyadic wavelet transform is that
`it is linear and shift-variant. Another important property of
`the dyadic wavelet transform is that its coefficients have
`local maxima at a particular time when the signal has sharp
`changes or discontinuities. These two important properties
`of the dyadic wavelet transform help to extract the unique
`features of a particular audio signal. Kadambe et al made a
`comparison of the results obtained from using dyadic wave
`let transforms, autocorrelation, and cepstral transforms. The
`investigation showed that the dyadic wavelet transform pitch
`detector gave 100% accurate results. One reason for the
`difference in the results was that the other two methods
`assume stationarity within the signal and measure the aver
`age period, where as the dyadic wavelet transform takes into
`account the non-stationarities in the signal. Hence, the
`dyadic wavelet transform method would be the best to
`extract feature when the signals are non-stationary. Harlow
`et al developed an algorithm to detect traffic accidents at
`intersections, using an audio signal as the input to the
`system. The algorithm uses the Real Cepstral Transform
`(RCT) as a method to extract features. The signals recorded
`at intersections include brake, pile drive, construction and
`normal traffic Sounds. These signals are segmented into
`three-second sections. Each of these three second segmented
`signals is analyzed using RCT. RCT is a method where the
`signal is windowed for every 100 msec using a hamming
`window with an overlap of 50 msec. Thus, for a given
`three-second signal, there will be almost 60 segments of 100
`
`msec duration each. RCT is applied to each of these seg
`ments, and the first 12 coefficients are used as the features.
`The features obtained using the RCT are then classified as
`“crash” or “non-crash” using a neural network.
`0023 Balraj's experimental results showed that among
`the three different statistical classifiers investigated, maxi
`mum likelihood and nearest neighbor performed best,
`although this had high computational costs. Haar,
`Daubechies, and Coiflets provided the best classification
`accuracies for a two-class system. Among the five different
`feature extraction methods analyzed on the basis of the
`overall accuracy, RCT performed best. The second-genera
`tion wavelet method, the lifting scheme, was also investi
`gated. It proved computationally efficient when compared to
`DWT. Thus, it was concluded that the optimum design for
`an automated system would be a wavelet-based feature
`extractor with a maximum likelihood classifier. Thus the
`choice of DWT or the lifting scheme would be preferred for
`a real-time system.
`0024. In any and/or all of the embodiments described
`herein, the systems, equipment systems, Subsystems,
`devices, components, and/or appliances, of and/or utilized in
`any of the respective embodiments, can include and/or can
`utilize the teachings and/or the subject matter of the follow
`ing U.S. Patents, the subject matter and teachings of which
`are hereby incorporated by reference herein and form a part
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`Exhibit 1011
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`
`SUMMARY OF THE INVENTION
`0045. Many of the known vehicle accident detection
`systems are limited in their ability to capture or process
`accurate data or to accurately and timely send the processed
`data to the proper location to enable authorities to properly
`assess accident damage and liability. Further, these systems
`generally do not incorporate advance