Traffic accident detection device and method of detecting traffic accident

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First Claim
1. A traffic accident detection apparatus comprising:
 a sensor section that detects a vehicle and obtains speed observation values of the vehicle;
a timeseries/reversetimeseries combined estimation section that computes timeseries speed estimate values estimated by reading out the speed observation values in a timeseries fashion and applying a predetermined filter to the speed observation values read in a timeseries fashion, computes reversetimeseries speed estimate values estimated by reading out the speed observation values in a reversetimeseries fashion and applying the predetermined filter to the speed observation values in a reversetimeseries fashion, extracts a time at which the difference between the timeseries speed estimate values and the reversetimeseries speed estimate values becomes greatest, and computes speed estimate values by connecting the timeseries speed estimate values preceding the extracted time and the reversetimeseries speed estimate values following the extracted time at the extracted time;
an acceleration computation section that computes acceleration values in a timeseries fashion based on an amount of change in the speed estimate values per unit time; and
a sudden braking determination section that compares the acceleration values and a predefined determination threshold in a timeseries fashion, and determines a time at which the acceleration values are less than the determination threshold to be a sudden braking time of the vehicle.
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Abstract
The present invention provides a traffic accident detection device of detecting traffic accident that estimates a precise speed variation for a vehicle, and detects dangerous events similar to traffic accidents. A time series estimation unit (202) chronologically estimates a speed from a detected speed of a vehicle that a sensor unit (102) has detected, and acquires a first estimated value; and a reverse time series estimation unit (204) reverse chronologically estimates a speed from the detected speed, and acquires a second estimated value. An integration estimation unit (206) estimates the speed and the speed shift of the vehicle by defining the first estimated value as an integrated estimated value until a time when the distance between the first estimated value and the second estimated value is at maximum, and defining the second estimated value as the integrated estimated value at the time the distance is at maximum and thereafter.
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Current Assignee
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General Electric Company

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Current Assignee
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Current Assignee
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Sponsoring Entity
IMA  Industria Macchine Automatiche SPA

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Patent #
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Current Assignee
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Sponsoring Entity
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9 Claims
 1. A traffic accident detection apparatus comprising:
 a sensor section that detects a vehicle and obtains speed observation values of the vehicle;
a timeseries/reversetimeseries combined estimation section that computes timeseries speed estimate values estimated by reading out the speed observation values in a timeseries fashion and applying a predetermined filter to the speed observation values read in a timeseries fashion, computes reversetimeseries speed estimate values estimated by reading out the speed observation values in a reversetimeseries fashion and applying the predetermined filter to the speed observation values in a reversetimeseries fashion, extracts a time at which the difference between the timeseries speed estimate values and the reversetimeseries speed estimate values becomes greatest, and computes speed estimate values by connecting the timeseries speed estimate values preceding the extracted time and the reversetimeseries speed estimate values following the extracted time at the extracted time;
an acceleration computation section that computes acceleration values in a timeseries fashion based on an amount of change in the speed estimate values per unit time; and
a sudden braking determination section that compares the acceleration values and a predefined determination threshold in a timeseries fashion, and determines a time at which the acceleration values are less than the determination threshold to be a sudden braking time of the vehicle.  View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
 a sensor section that detects a vehicle and obtains speed observation values of the vehicle;
 9. A traffic accident detection method, comprising the steps of:
 detecting a vehicle and obtaining speed observation values of the vehicle;
computing timeseries speed estimate values estimated by reading out the speed observation values in a timeseries fashion and applying a predetermined filter to the speed observation values read in a timeseries fashion, reversetimeseries speed estimate values estimated by reading out the speed observation values in a reversetimeseries fashion and applying the predetermined filter to the speed observation values in a reversetimeseriesfashion;
extracting a time at which the difference between the timeseries speed estimate values and the reversetimeseries speed estimate values becomes greatest;
computing speed estimate values by connecting the timeseries speed estimate values preceding the extracted time and the reversetimeseries speed estimate values following the extracted time at the extracted time;
computing acceleration values in a timeseries fashion based on an amount of change in the speed estimate values per unit time; and
comparing the acceleration values and a predefined determination threshold in a timeseries fashion, and determines a time at which the acceleration values are less than the determination threshold to be a sudden braking time of the vehicle.
 detecting a vehicle and obtaining speed observation values of the vehicle;
1 Specification
The present invention relates to a traffic accident detection apparatus and traffic accident detection method where a vehicle is observed with a sensor.
Accident prediction information and statistical/analytical information on accidents are useful in preventing vehicle accidents. Such information are provided to, for example, drivers, road administrators who are responsible for road safety design or for considering improvement measures, police who inspect traffic accidents and organize traffic safety campaigns, accident appraisers and insurers that conduct accident analyses, and so forth.
One known method for collecting such information is the drive recorder, for example. A drive recorder records images/video and sensor information of the few seconds before and after a sudden braking event detected by a vehiclemounted sensor. The information recorded on the drive recorder is visualized, presented to the driver by a business operator that manages the vehicle, and thus utilized to raise awareness regarding traffic safety. The “HiyariHatto Database” compiled by the Society of Automotive Engineers of Japan, which is a database comprised of image/videos and sensor information from drive recorders, enables causal analyses of accidents based on large volumes of hiyarihatto data, and is used by auto manufacturers in developing traffic safety assistance apparatuses, and/or the like. The term “hiyarihatto” refers to a state where, although a collision did not take place, one was close to happening.
Although such drive recorders are gradually becoming common place on business vehicles, e.g., taxis, buses, etc., it is unrealistic to expect drive recorders to be mounted on all vehicles on public roads, including ordinary vehicles. On the other hand, since 60% of traffic accidents take place at intersections, it is desired that accidents and hiyarihattos be detected based on changes in vehicle speed observed by roadside sensors installed at intersections.
To that end, Patent Literature 1, for example, discloses a traffic accident detection apparatus that uses a vehicle detection sensor installed at an intersection.
Imaging device 11 constantly captures the traffic conditions in its observation area. The image data thus captured is temporarily recorded (cached) in data recording section 13. Vehicle detection sensor 12 detects all vehicles within the observation area, monitoring, as well as outputting to data analysis section 14, changes in the position and speed of each vehicle over time.
Data analysis section 14 analyses the data outputted from vehicle detection sensor 12. By way of example, data analysis section 14 determines if an accident or a dangerous situation has occurred by detecting sudden acceleration changes of a vehicle, abnormal proximity of positional data between a plurality of vehicles, and/or the like, and notifies recording control section 15 of the determination result.
If the determination result received from data analysis section 14 indicates that an accident or a dangerous situation has occurred, recording control section 15 has data recording section 13 record the imaged data of a given duration preceding and following that occurrence.
As a filter for correcting errors contained in observation values, the Kalman filter is widely known. As an application example of the Kalman filter, Patent Literature 2, for example, discloses a current position detection apparatus for vehicles which detects the current position of a vehicle based on the vehicle's orientation and traveled distance.
By having computations (deadreckoning computations) carried out at relative path computation section 24 and absolute position computation section 25 based on signals from vehicle speed sensor 21 and gyro 22, vehicle speed, absolute orientation, relative path, and absolute position are outputted. Further, outputs of position, orientation, and vehicle speed are obtained from GPS 23. Based on the vehicle speed, absolute orientation, and absolute position information obtained through deadreckoning, as well as the vehicle speed, orientation, and position information from GPS 23, Kalman filter 26 performs vehicle speed sensor distance coefficient correction, gyro offset correction, absolute orientation correction, and absolute position correction.
PTL 1
Japanese Patent Application LaidOpen No. 2000207676
PTL 2
Japanese Patent Application LaidOpen No. HEI 868654
However, due to the fact that vehicles are not uniformly oriented at intersections, for example, the emitted wave from the sensor for detecting vehicles and pedestrians is reflected by unexpected parts of other vehicles and one's own vehicle, thereby causing noise. With the technique disclosed in Patent Literature 1 mentioned above, even if one were to employ the technique disclosed in Patent Literature 2, there would still be unpredictable errors in the observation values by the vehicle detection sensor, as a result of which it would be impossible to correctly determine speed changes. In other words, it would be difficult to accurately determine when sudden braking occurred.
An object of the present invention is to provide a traffic accident detection apparatus and traffic accident detection method that estimate an accurate speed change of a vehicle, and detect risk events comparable to traffic accidents.
A traffic accident detection apparatus of the present invention may be configured to include: a sensor section that observes a vehicle and obtains speed observation values of the vehicle; a timeseries/reversetimeseries combined estimation section that obtains the speed observation values from the sensor section, computes timeseries speed estimate values and reversetimeseries speed estimate values, and computes speed estimate values based on the timeseries speed estimate values and the reversetimeseries speed estimate values, the timeseries speed estimate values being estimated in a timeseries fashion based on the speed observation values, the reversetimeseries speed estimate values being estimated in a reversetimeseries fashion based on the speed observation values; an acceleration computation section that computes acceleration values in a timeseries fashion based on an amount of change in the speed estimate values per unit time; and a sudden braking determination section that compares the acceleration values and a predefined determination threshold in a timeseries fashion, and determines a time at which the acceleration values are less than the determination threshold to be a sudden braking time of the vehicle.
A traffic accident detection method of the present invention may be so arranged that: a sensor section observes a vehicle and obtains speed observation values of the vehicle; a timeseries/reversetimeseries combined estimation section obtains the speed observation values from the sensor section, computes timeseries speed estimate values and reversetimeseries speed estimate values, and computes speed estimate values based on the timeseries speed estimate values and the reversetimeseries speed estimate values, the timeseries speed estimate values being estimated in a timeseries fashion based on the speed observation values, the reversetimeseries speed estimate values being estimated in a reversetimeseries fashion based on the speed observation values; an acceleration computation section computes acceleration values in a timeseries fashion based on an amount of change in the speed estimate values per unit time; and a sudden braking determination section compares the acceleration values and a predefined determination threshold in a timeseries fashion, and determines a time at which the acceleration values are less than the determination threshold to be a sudden braking time of the vehicle.
With the present invention, it is possible to provide a traffic accident detection apparatus and traffic accident detection method that estimate an accurate speed change of a vehicle, and detect risk events comparable to traffic accidents.
Embodiments of the present invention are described in detail below with reference to the drawings.
Sensor section 102 detects all vehicles present within an observation area, and obtains and outputs timeseries speed observation values for each vehicle.
Timeseries/reversetimeseries combined estimation section 104 included in data analysis section 103 obtains timeseries speed observation values from sensor section 102, and computes timeseries speed estimate values and reversetimeseries speed estimate values based on the speed observation values. Since speed observation values include noise caused by surrounding vehicles, the vehicle speed of the vehicle of interest must be estimated based on speed observation values. Noise is caused by scattered reflection in the case of radarbased sensing, or by occlusion in the case of camerabased sensing.
Timeseries speed estimate values are estimated using the Kalman filter, and/or the like, and by reading speed observation values in a time series fashion. Specifically, timeseries speed estimate values are estimated based on speed observation values read in a timeseries fashion, their observed times, and the Kalman gain value derived from the Kalman filter. Reversetimeseries speed estimate values are estimated using the Kalman filter, and/or the like, and by reading speed observation values in a reversetimeseries fashion. Specifically, reversetimeseries speed estimate values are estimated based on speed observation values read in a reversetimeseries fashion, their observed times, and the Kalman gain value.
Timeseries/reversetimeseries combined estimation section 104 computes speed estimate values based on the timeseries speed estimate values and the reversetimeseries speed estimate values. Specifically, speed estimate values are computed by determining the time of observation at which the difference between the timeseries speed estimate values and the reversetimeseries speed estimate values becomes greatest (hereinafter referred to as combination time in some cases), and then combining the timeseries speed estimate values preceding the combination time and the reversetimeseries speed estimate values following the combination time.
Acceleration computation section 105 obtains, in a time series fashion, the speed estimate values computed at timeseries/reversetimeseries combined estimation section 104, and, based on the amount of change in the speed estimate values per unit time, computes acceleration values in a timeseries fashion.
Sudden braking determination section 106 obtains, in a timeseries fashion, the acceleration values computed at acceleration computation section 105, and makes a comparative determination between the timeseries acceleration values and a predefined determination threshold. An observation time at which the acceleration value is less than the determination threshold is determined as being a sudden braking time of the vehicle. Also, if the acceleration values are greater than the determination threshold at all times, the sudden braking determination section determines that no sudden braking took place.
Thus, traffic accident detection apparatus 800 according to the present embodiment estimates vehicle speed in a timeseries fashion and a reversetimeseries fashion based on speed observation values, and detects the time at which the vehicle made a sudden brake through a comparative determination between acceleration values of speed estimate values, which are computed based on timeseries and reversetimeseries speed estimate values, and a determination threshold. Thus, traffic accident detection apparatus 800 is able to accurately detect correct speed changes (the time at which sudden braking occurred) even when unpredictable errors occur in the values observed by the vehicle detection sensor.
In the description above, sudden braking determination section 106 uses a predefined determination threshold to determine if sudden braking has occurred. However, the determination threshold may also be made variable based on speed estimate values. Specifically, as in the table shown in
A configuration of traffic accident detection apparatus 100 is described below with reference to
Sensor section 102 detects all vehicles present within an observation area, obtains speed observation values of each vehicle in a timeseries fashion, and outputs them to timeseries/reversetimeseries combined estimation section 104 of data analysis section 103.
Data analysis section 103 includes timeseries/reversetimeseries combined estimation section 104, acceleration computation section 105, and sudden braking determination section 106.
Timeseries/reversetimeseries combined estimation section 104 obtains, in a timeseries fashion, the speed observation values outputted from sensor section 102, and, based on the timeseries speed observation values, estimates the speed of the vehicle in a timeseries fashion and a reversetimeseries fashion. Based on the vehicle speeds estimated in a timeseries fashion and the vehicle speeds estimated in a reversetimeseries fashion. timeseries/reversetimeseries combined estimation section 104 computes speed estimate values and outputs them to acceleration computation section 105.
Specifically, timeseries/reversetimeseries combined estimation section 104 computes timeseries speed estimate values and reversetimeseries speed estimate values based on timeseries speed observation values. Timeseries/reversetimeseries combined estimation section 104 computes a combination time, which is the observation time at which the difference between the timeseries speed estimate values and the reversetimeseries speed estimate values becomes greatest, and computes speed estimate values by combining the timeseries speed estimate values preceding the combination time with the reversetimeseries speed estimate values following the combination time.
Acceleration computation section 105 obtains, in a time series fashion, the speed estimate values outputted from timeseries/reversetimeseries combined estimation section 104, and, based on timeseries changes in the obtained speed estimate values, computes acceleration values of the vehicle. The computed acceleration values of the vehicle are outputted to sudden braking determination section 106. The speed of the vehicle is hereinafter simply referred to as “speed value,” and the acceleration of the vehicle as “acceleration value.”
Sudden braking determination section 106 makes a comparative determination between the acceleration values obtained from acceleration computation section 105 and a predefined determination threshold. If the acceleration value is less than the determination threshold, it is determined that the vehicle has made a sudden brake.
In cases where traffic accident detection apparatus 100 is operated as a system, sudden braking determination section 106 outputs the determination result and the sudden braking time to recording control section 107 and data recording section 108. The sudden braking of a vehicle is hereinafter referred to simply as “sudden braking.”
If the analysis result outputted from data analysis section 103 indicates sudden braking, recording control section 107 obtains the time at which the sudden braking took place (sudden braking time), and computes a record start time and a record end time based on the obtained sudden braking time. Recording control section 107 sets the computed record start time and record end time in data recording section 108.
Data recording section 108 records, from the cache onto a recording medium, the image data of from the record start time to the record end time set by recording control section 107 and the analysis data of data analysis section 103. Once recording on the recording medium is completed, data recording section 108 deletes the image data and analytical data that were temporarily recorded before a given point in time that goes back a predetermined period of time from the current time.
Imaging device 101, recording control section 107, and data recording section 108 are not key features of traffic accident detection apparatus 100. Even if they are omitted, the present invention still produces an advantageous effect where the time at which sudden braking took place is determined accurately. By providing imaging device 101, recording control section 107, and data recording section 108, a system that detects traffic accidents is constructed.
Observation value buffer 201 stores speed observation values outputted from sensor section 102. The stored speed observation values are read out by timeseries estimation section 202 and reversetimeseries estimation section 204.
Timeseries estimation section 202 reads out, in a timeseries fashion, the speed observation values stored in observation value buffer 201, and estimates speed in a timeseries fashion. The estimated timeseries speed estimate values are outputted to first estimate value buffer 203 as first estimate values.
Reversetimeseries estimation section 204 reads out, in a reversetimeseries fashion, the speed observation values stored in observation value buffer 201, and estimates speed in a reversetimeseries fashion. The estimated reversetimeseries speed estimate values are outputted to second estimate value buffer 205 as second estimate values.
With respect to reversetimeseries estimation section 204, estimate value computation section 301 reads out speed observation values from observation value buffer 201 in a reversetimeseries fashion starting with the speed observation value observed most recently. Based on speed observation values read out in a reversetimeseries fashion along with their observation times, and on the Kalman gain value derived from Kalman filter 303, estimate value computation section 301 computes reversetimeseries speed estimate values (i.e., second estimate values). Computation value buffer 302 holds the speed estimate value from one time unit later (e.g., 100 milliseconds later), while also outputting it to second estimate value buffer 205. Kalman filter 303 forms an error distribution based on speed estimate values from one time unit later, derives the Kalman gain value, and feeds it back to estimate value computation section 301.
First estimate value buffer 203 stores the first estimate values outputted by timeseries estimation section 202. The stored first estimate values are read out by combined estimation section 206. Second estimate value buffer 205 stores the second estimate values outputted by reversetimeseries estimation section 204. The stored second estimate values are read out by combined estimation section 206.
Combined estimation section 206 outputs, to acceleration computation section 105 and sudden braking determination section 106 as combined estimate values, the first estimate values up until the time at which the difference between each first estimate value (timeseries speed estimate value) read out from first estimate value buffer 203 and each second estimate value (reversetimeseries estimate value) read out from second estimate value buffer 205 at the same time becomes greatest (i.e., before the combination time), and it outputs the second estimate values after the time at which the difference becomes greatest (i.e., after the combination time).
In other words, combined estimation section 206 computes speed estimate values by determining the observation time at which the difference between the first estimate values (the timeseries speed estimate values) and the second estimate values (the reversetimeseries speed estimate values) becomes greatest (i.e., the combination time), and by combining the first estimate values preceding the combination time and the second speed estimate values following the combination time.
In step S401, timeseries/reversetimeseries combined estimation section 104 sets a search start time and a search range.
By way of example, if an observation value is inputted every 100 milliseconds, timeseries/reversetimeseries combined estimation section 104 is configured to perform processing by shifting a threesecond search range by 100 milliseconds at a time. Specifically, the search start time is successively shifted from 0 seconds to 3000 milliseconds 100 milliseconds at a time. First, in step S401, in order to determine the “timeseries speed estimate values” and the “reversetimeseries speed estimate values” based on speed observation values from 0 seconds to 3000 milliseconds, the search start time is set to 0 milliseconds, and the search range is set to be from 0 milliseconds to 3000 milliseconds (first run). Next, in step S401, in order to determine the “timeseries speed estimate values” and the “reversetimeseries speed estimate values” based on speed observation values from 100 milliseconds to 3100 milliseconds, the search start time is set to 100 milliseconds, and the search range is set to be from 100 milliseconds to 3100 milliseconds (second run). Search ranges are subsequently set in a similar fashion.
Sufficient memory to buffer threeseconds' worth of speed observation values sampled at 100 milliseconds is allocated to observation value buffer 201, first estimate value buffer 203, and second estimate value buffer 205.
With respect to the search range, timeseries/reversetimeseries combined estimation section 104 carries out timeseries estimation for each estimation time by successively setting estimation times from the earliest time to the latest time, and carries out reversetimeseries estimation for each estimation time by setting estimation times in reverse from the latest time to the earliest time.
In step S402, timeseries estimation section 202 sets an estimation time within the search range. It estimates speed in a timeseries fashion in step S403. In step S404, it temporarily stores a speed estimate value (a first estimate value) in first estimate value buffer 203. In step S405, timeseries estimation section 202 determines whether or not the search range has been completed, and if not, repeats step S402 through step S404 until it is completed, proceeding to step S406 once the search range has been completed. In step S402 of the second and subsequent runs, the estimation time is set to an observation time that follows by 100 milliseconds. By way of example, in a case where processing is carried out with observation times of 0 to 3000 milliseconds as the search range, timeseries estimation section 202 estimates speed by reading out observation values from observation value buffer 201 while successively shifting the estimation time, as in from 0 milliseconds to 100 milliseconds, and then to 200 milliseconds, and so forth.
Next, in step S406, reversetimeseries estimation section 204 sets an estimation time within the search range. It estimates speed in a reversetimeseries fashion in step S407. In step S408, it temporarily stores a speed estimate value (a second estimate value) in second estimate value buffer 205. In step S409, reversetimeseries estimation section 204 determines whether or not the search range has been completed, and if not, repeats step S406 through step S408 until it is completed, proceeding to step S410 once the search range has been completed. In step S406 of the second and subsequent runs, the estimation time is set to an observation time that precedes by 100 milliseconds. By way of example, in a case where processing is carried out with observation times of 0 to 3000 milliseconds as the search range, reversetimeseries estimation section 204 estimates speed by reading out observation values from observation value buffer 201 while successively going through the estimation times backwards, as in from 2900 milliseconds to 2800 milliseconds, and then to 2700 milliseconds, and so forth.
Next, in step S410, with respect to the search range, combined estimation section 206 sets computation times from the earliest time to the latest time. In step S411, it reads out a first estimate value and a second estimate value corresponding to the same computation time from first estimate value buffer 203 and second estimate value buffer 205, respectively, and computes the distance between the first estimate value and the second estimate value. In step S412, combined estimation section 206 determines whether or not the search range has been completed, and if not, repeats step S410 and step S411 until it is completed, proceeding to step S413 once the search range has been completed.
Next, in step S413, combined estimation section 206 holds, as a switch time (a combination time), the time at which the difference computed in step S411 becomes greatest. In step S414, combined estimation section 206 sets an output time within the search range, and determines, in step S415, whether or not the output time precedes the switch time. If the output time precedes the switch time (i.e., YES), the first estimate value is outputted in step S416, whereas if the output time follows the switch time (i.e., NO), it outputs the second estimate value in step S417. In step S418, combined estimation section 206 determines whether or not the search range has been completed, and if not, repeats step S414 through step S417 until it is completed, proceeding to step S419 once the search range has been completed.
Finally, in step S419, timeseries/reversetimeseries combined estimation section 104 specifies the next search start time and returns to step S401.
The description above assumes that, in cases where an observation value is inputted every 100 milliseconds, timeseries/reversetimeseries combined estimation section 104 is configured to perform processing by shifting a threesecond search range by 100 milliseconds at a time. However, this is by no means limiting.
By way of example, in a case where an observation value is inputted every 100 milliseconds, timeseries/reversetimeseries combined estimation section 104 may also be configured to perform processing by shifting a twosecond search range by 100 milliseconds at a time.
In other words, the range of speed observation values used to compute timeseries speed estimate values and reversetimeseries speed estimate values at the timeseries/reversetimeseries combined estimation section is broader than the range of timeseries speed estimate values and reversetimeseries speed estimate values of the computed results.
By way of example, in a ease where an observation value is inputted every 100 milliseconds, timeseries/reversetimeseries combined estimation section 104 may also be configured to perform processing by shifting a twosecond search range by an integer multiple of 100 milliseconds at a time, e.g., by one second at a time.
In this case, a property of Kalman filter 303, which is suited for linear estimation, is utilized, namely that it is incapable of following sudden changes in speed corresponding to sudden braking observed in accidents and hiyarihattos. Specifically, since the quantity that cannot be followed by timeseries estimation and the quantity that cannot be followed by reversetimeseries estimation expand in opposite directions, the time at which the distance between the timeseries speed estimate values (the first estimate values) and the reversetimeseries speed estimate values (the second estimate values) becomes greatest is taken to be the switch time. This is depicted in
As shown in
As shown in
Thus, with Embodiment 2, the first estimate values are obtained by estimating speed in a timeseries fashion based on the speed observation values of the vehicle observed by sensor section 102, and the second estimate values are obtained by estimating speed in a reversetimeseries fashion based on the speed observation values. Up to the time at which the distance between the first estimate values and the second estimate values becomes greatest, the first estimate values, which follow the vehicle speed, are adopted as combined estimate values, and following the time at which the distance becomes greatest, the second estimate values, which follow the vehicle speed, are adopted as the combined estimate values. The above are taken to be the actual speed of the vehicle. It is thus made possible to detect sudden braking. In addition, even if an unpredictable error were to occur in the speed observation values, it would be possible to detect an accurate speed change of the vehicle, that is, the time at which sudden braking took place.
For the present embodiment, with respect to Kalman filter 303, it is preferable that the initial value and system noise parameters be configured to derive a Kalman gain that is suited for linear estimation so as to render Kalman filter 303 incapable of following sudden changes. However, other initial values and system noise parameters may also be used.
With respect to combined estimation section 206, when extracting the time at which the difference between the first estimate values and the second estimate values becomes greatest (i.e., the combination time), it may be so arranged that a comparative determination with respect to the determination threshold is rendered only when that difference is greater than a predetermined threshold, and that a determination of no sudden braking is made when it is less than the predetermined threshold. In this case, the predetermined threshold may also be set dynamically based on the error distribution computed by the Kalman filter, on the S/N obtained by sensor section 102, or on changes in vehicle count, vehicle crowdedness, and/or the like, as observed by sensor section 102.
For the present embodiment, sudden braking determination section 106 may be equipped with a table such as that shown in
For the present embodiment, various radars, such as laser, millimeter wave, and/or the like, may be used as sensor section 102, or a camera involving image processing, or some combination thereof may be used.
For the present embodiment, although changes in the observation values in the forward/rearward direction relative to the travel direction of the vehicle under observation are addressed, they may also be changes in the observation values in the left/right direction as viewed in the travel direction of the vehicle, changes in the observation values in the up/down direction, or some combination of the above. Furthermore, for the present embodiment, speed was used for the observation values. However, the present invention is by no means limited as such, and the distance between sensor section 102 and a vehicle, or the position of a vehicle may also be used. When the distance between sensor section 102 and a vehicle is used, speed may be determined based on the difference between timeseries distance values and the time interval between the distance measurements. When the position of a vehicle is used, speed may be determined based on the difference between timeseries vehicle positions and the time interval between the position measurements.
For the present embodiment, the Kalman filter is used. However, the present invention is by no means limited as such, and other linear filters may be used, as well as nonlinear filters such as the extended Kalman filter, the unscented Kalman filter, etc.
For the present embodiment, in the process of deriving the Kalman gain of the Kalman filter, a speed estimate value from one time unit before or from one time unit after is used. However, it is also possible to use a value from a given number of time units before or after, as well as an integrated value, average value, etc., of up to a given number of time units before or after. Furthermore, the given number of time units may be set dynamically based on the range of variation of the Kalman gain, the range of variation of the estimate values, and/or the like.
For the present embodiment, in order to detect sudden braking as observed in accidents and hiyarihattos, it is determined whether or not there exists a time at which the distance between timeseries speed estimate values and reversetimeseries speed estimate values reaches a global maximum equal to or greater than a specified threshold. However, in order to accommodate cases where several sudden braking events occur, e.g., double collisions, and/or the like, it may also be detected by determining whether or not the distance between timeseries speed estimate values and reversetimeseries speed estimate values reaches a local maximum equal to or greater than a specified threshold. When detecting sudden braking based on local maxima, those preceding the smallest local maximum are taken to be first estimate values, and those following the greatest local maximum are taken to be second estimate values. The combined estimate values between the local maxima may be either of the first estimate values and the second estimate values.
The disclosure of the specification, drawings, and abstract included in Japanese Patent Application No. 2010241982, filed on Oct. 28, 2010, is incorporated herein by reference in its entirety.
A traffic accident detection apparatus and a traffic accident detection method according to the present invention may be applied to the Traffic Accident Automatic Memory System (TAAMS), prevention/safety systems, as well as drive assist systems, and particularly to traffic accident prevention systems, traffic accident cause analysis systems, traffic accident prediction systems, and/or the like, for intersections.
 100 Traffic accident detection apparatus
 101 Imaging device
 102 Sensor section
 103 Data analysis section
 104 Timeseries/reversetimeseries estimation section
 105 Acceleration computation section
 106 Sudden braking determination section
 107 Recording control section
 108 Data recording section
 201 Observation value buffer
 202 Timeseries estimation section
 203 First estimate value buffer
 204 Reversetimeseries estimation section
 205 Second estimate value buffer
 206 Combined estimation section
 301 Estimate value computation section
 302 Computation value buffer
 303 Kalman filter
 800 Traffic accident detection apparatus