Traffic surveillance and simulation apparatus
First Claim
Patent Images
1. A traffic surveillance system comprising:
- a first sensor positioned to sense vehicular traffic in a predetermined first field;
a first local traffic processor coupled to the first sensor for identifying vehicles within the field of the first sensor by periodically sampling the first sensor and extracting vehicle locations and identification information;
a second sensor positioned to sense vehicular traffic in a second predetermined field where said second predetermined field is separated from said first predetermined field;
a second local traffic processor coupled to the second sensor for identifying vehicles within the field of the second sensor by periodically sampling the second sensor and extracting vehicle locations and identification information; and
a wide area traffic flow processor coupled to each local traffic processor for receiving the vehicle location and identification information from each local traffic processor and tracking the identified vehicles;
wherein the wide area traffic flow processor utilizes a predictor algorithm to predict each identified vehicle'"'"'s location, and utilizes the vehicle location and identification information to correct the predictor model thereby functioning to monitor traffic consisting of all the identified vehicles.
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Abstract
A wide area surveillance system for application to large road networks is described. The system employs smart sensors to identify plural individual vehicles in the network. These vehicles are tracked on an individual basis, and the system derives the behavior of the vehicle. Furthermore, the system derives traffic behavior on a local basis, across roadway links, and in sections of the network. Processing in the system is divided into multiple processing layers, with geographical separation of tasks.
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Citations
29 Claims
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1. A traffic surveillance system comprising:
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a first sensor positioned to sense vehicular traffic in a predetermined first field; a first local traffic processor coupled to the first sensor for identifying vehicles within the field of the first sensor by periodically sampling the first sensor and extracting vehicle locations and identification information; a second sensor positioned to sense vehicular traffic in a second predetermined field where said second predetermined field is separated from said first predetermined field; a second local traffic processor coupled to the second sensor for identifying vehicles within the field of the second sensor by periodically sampling the second sensor and extracting vehicle locations and identification information; and a wide area traffic flow processor coupled to each local traffic processor for receiving the vehicle location and identification information from each local traffic processor and tracking the identified vehicles; wherein the wide area traffic flow processor utilizes a predictor algorithm to predict each identified vehicle'"'"'s location, and utilizes the vehicle location and identification information to correct the predictor model thereby functioning to monitor traffic consisting of all the identified vehicles. - View Dependent Claims (2)
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3. A traffic surveillance system comprising:
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a first sensor positioned to sense vehicular traffic in a predetermined first field; a first local traffic processor for identifying vehicles within the first field and periodically generating vehicle locations and identification information concerning the vehicles in the first field; a second sensor Dositioned to sense vehicular traffic in a predetermined second field; a second local traffic processor for identifying vehicles within the second field and periodically generating vehicle locations and identification information concerning the vehicles in the second field; and a wide area traffic flow processor coupled to the first and second local traffic processors for receiving the vehicle location and identification information from the local traffic processors and tracking the identified vehicles, the wide area traffic flow processor reporting predictions of where the vehicles will be at the next period; wherein the wide area traffic flow processor models the kinematics of the vehicles individually, and utilizes new vehicle location and identification information to correct the prediction mechanism. - View Dependent Claims (4, 5, 6, 7, 8, 9, 10, 11)
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12. An apparatus for tracking vehicles in a roadway comprising a programmed computer system, the roadway including first and second lanes, wherein the first lane includes a segment which is substantially parallel to a segment of the second lane such that a vehicle could move directly therebetween, the beginning of the substantially parallel segments defining a hypothesis generation point, and the end of the substantially parallel segments defining a decision hold off point in each lane, there being further defined a first decision point in the first lane beyond the decision hold off point and a second decision point in the second lane beyond the decision hold off point, the program being selectively operable to effect the process of:
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obtaining a vehicle'"'"'s location on the first lane segment within a predetermined distance of the hypothesis generation point in a first period; generating a hypothetical track corresponding to the vehicle continuing on the first lane and a hypothetical track corresponding to the vehicle moving to the second lane; generating a probability of correctness for the hypotheses; obtaining the vehicle'"'"'s location in a second period after the first period; updating the probabilities based upon the vehicle'"'"'s location in the second period; determining if the vehicle'"'"'s location is past the decision hold off point, and if so, then determining whether one of the probabilities has reached a threshold, and if so, then eliminating the track associated with the hypothesis which did not reach the threshold, and otherwise updating the tracks with the vehicle'"'"'s location in the second period and continuing with the method at the updating probabilities step. - View Dependent Claims (13, 14)
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15. A method of tracking vehicles comprising:
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obtaining current vehicle location and generating a vehicle track from the location of the vehicle; extending the vehicle track based upon the current location of the vehicle; associating and updating all vehicle tracks; for each vehicle track; performing a first test of whether the vehicle track has reached a hypothesis generation point, and if not then continuing with the method at the vehicle location obtaining step, and otherwise; extending all vehicle tracks; performing associations between vehicle tracks; updating all vehicle tracks; updating hypothesis probabilities for the vehicle tracks; updating vehicle track types; testing if the vehicle tracks have passed a hold off point, and if not then continuing with the method at the extending all vehicle tracks step, and otherwise testing if a stopping rule has been satisfied, and if not then continuing with the method at the extending all vehicle tracks step, and otherwise continuing with the method at the first test step. - View Dependent Claims (16)
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17. A method of tracking a first vehicle on a roadway, the roadway comprising a first lane segment contiguous with a second lane segment, and a plurality of geometric events having predetermined locations, the method comprising:
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(a) obtaining the first vehicle'"'"'s location on the first lane segment in a first time period, and the first vehicle'"'"'s location on the first lane segment in a second time period; (b) predicting a track of the first vehicle as an extension of the first vehicle'"'"'s locations in the first and second time periods; (c) determining if geometric events are in the first vehicle'"'"'s track, and if present, identifying the geometric events; (d) determining if a second vehicle is preceding the first vehicle, and, if present, obtaining the second vehicle'"'"'s kinematics; (e) determining the lane gap, the car gap, the lane influence point and the headway influence point for the first vehicle; (f) if the lane gap is greater than the lane influence point and the car gap is greater than the headway influence point, then extending the first vehicle'"'"'s track without taking into account the geometric events or the second vehicle'"'"'s kinematics; (g) if the lane gap is greater than the lane influence point and the car gap is not greater than the headway influence point, then extending the first vehicle'"'"'s track without taking into account the geometric events while taking into account the second vehicle'"'"'s kinematics; (h) if the lane gap is not greater than the lane influence point and the car gap is greater than the headway influence point, then extending the first vehicle'"'"'s track taking into account the geometric events and not taking into account the second vehicle'"'"'s kinematics; (i) if the lane gap is not greater than the lane influence point and the car gap is not greater than the headway influence point, then extending the first vehicle'"'"'s track taking into account the geometric events and the second vehicle'"'"'s kinematics. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24, 25, 26, 27)
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28. A traffic surveillance system comprising:
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plural local traffic processors each for identifying vehicles within a field and periodically generating vehicle locations and identification information, the fields including first and second lanes having at least one segment each which are substantially parallel such that a vehicle could move directly therebetween, the beginning of the substantially parallel segments defining a hypothesis generation point, and the end of the substantially parallel segments defining a decision hold off point in each lane, there being further defined a first decision point in the first lane beyond the decision hold off point and a second decision point in the second lane beyond the decision hold off point; and a wide area traffic flow processor coupled to the local traffic processors for receiving the vehicle location and identification information from the local traffic processors and tracking the identified vehicles, the wide area traffic flow processor reporting predictions of where the vehicles will be at the next period, and being selectively operable to effect the process of; obtaining a vehicle'"'"'s location on the first lane segment within a predetermined distance of the hypothesis generation point in a first period; generating a hypothetical track corresponding to the vehicle continuing on the first lane and a hypothetical track corresponding to the vehicle moving to the second lane; generating a probability of correctness for the hypotheses; obtaining the vehicle'"'"'s location in a second period after the first period; updating the probabilities based upon the vehicle'"'"'s location in the second period; determining if the vehicle'"'"'s location is past the decision hold off point, and if so, then determining whether one of the probabilities has reached a threshold, and if so, then eliminating the hypothetical track associated with the hypothesis which did not reach the threshold, and otherwise updating the hypothetical tracks with the vehicle'"'"'s location in the second period and continuing with the method at the updating probabilities step; wherein the wide area traffic flow processor models the kinematics of the vehicles individually, and utilizes new vehicle location and identification information to correct the prediction mechanism.
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29. A traffic surveillance system comprising:
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plural local traffic processors each for identifying vehicles within a field and periodically generating vehicle locations and identification information, the fields including first and second contiguous lane segments and plural geometric events; and a wide area traffic flow processor coupled to the local traffic processor for receiving the vehicle location and identification information from the local traffic processor and tracking the identified vehicles, the wide area traffic flow processor reporting predictions of where the vehicles will be at the next period and being selectively operable to effect the process of; obtaining a first vehicle'"'"'s location on the first lane segment in a first time period, and the first vehicle'"'"'s location on the first lane segment in a second time period; predicting a track of the first vehicle as an extension of the first vehicle'"'"'s locations in the first and second time periods; determining if geometric events are in the first vehicle'"'"'s track, and if present, identifying the geometric events; determining if a second vehicle is preceding the first vehicle, and, if present, obtaining the second vehicle'"'"'s kinematics; determining the lane gap, the car gap, the lane influence point and the headway influence point for the first vehicle; if the lane gap is greater than the lane influence point and the car gap is greater than the headway influence point, then extending the first vehicle'"'"'s track without taking into account the geometric events or the second vehicle'"'"'s kinematics; if the lane gap is greater than the lane influence point and the car gap is not greater than the headway influence point, then extending the first vehicle'"'"'s track without taking into account the geometric events and taking into account the second vehicle'"'"'s kinematics; if the lane gap is not greater than the lane influence point and the car gap is greater than the headway influence point, then extending the first vehicle'"'"'s track taking into account the geometric events and not taking into account the second vehicle'"'"'s kinematics; if the lane gap is not greater than the lane influence point and the car gap is not greater than the headway influence point, then extending the first vehicle'"'"'s track taking into account the geometric events and the second vehicle'"'"'s kinematics; wherein the wide area traffic flow processor models the kinematics of the vehicles individually, and utilizes new vehicle location and identification information to correct the prediction mechanism.
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Specification