Abstract: A vehicle detection system 200 comprises: a receiver 202 arranged to receive data transmissions from a vehicle 210; a camera 204 arranged to capture an image of the vehicle 210; and a processing subsystem 206 arranged to implement a trained model for detecting and/or classifying vehicles in an image. The system 200 is arranged to operate in a first mode wherein: the camera 204 captures an image of a vehicle 210 and the receiver 202 receives a data transmission from the vehicle 210; and the processing subsystem 206 is arranged to use information resulting from the data transmission received and the image to train the model further. The system 200 is also arranged to operate in a second mode wherein: the camera 204 captures an image of a vehicle 210 and the receiver 202 does not receive a data transmission; and the processing subsystem 206 is arranged to use the image and the trained model to detect and/or classify the vehicle 210 in the image.

Abstract: A vehicle detection system 200 comprises: a receiver 202 arranged to receive data transmissions from a vehicle 210; a camera 204 arranged to capture an image of the vehicle 210; and a processing subsystem 206 arranged to implement a trained model for detecting and/or classifying vehicles in an image. The system 200 is arranged to operate in a first mode wherein: the camera 204 captures an image of a vehicle 210 and the receiver 202 receives a data transmission from the vehicle 210; and the processing subsystem 206 is arranged to use information resulting from the data transmission received and the image to train the model further. The system 200 is also arranged to operate in a second mode wherein: the camera 204 captures an image of a vehicle 210 and the receiver 202 does not receive a data transmission; and the processing subsystem 206 is arranged to use the image and the trained model to detect and/or classify the vehicle 210 in the image.

Abstract: A vehicle detection system 200 comprises: a receiver 202 arranged to receive data transmissions from a vehicle 210; a camera 204 arranged to capture an image of the vehicle 210; and a processing subsystem 206 arranged to implement a trained model for detecting and/or classifying vehicles in an image. The system 200 is arranged to operate in a first mode wherein: the camera 204 captures an image of a vehicle 210 and the receiver 202 receives a data transmission from the vehicle 210; and the processing subsystem 206 is arranged to use information resulting from the data transmission received and the image to train the model further. The system 200 is also arranged to operate in a second mode wherein: the camera 204 captures an image of a vehicle 210 and the receiver 202 does not receive a data transmission; and the processing subsystem 206 is arranged to use the image and the trained model to detect and/or classify the vehicle 210 in the image.

Abstract: A method and system for determining a path taken by a vehicle are disclosed. A path portion is determined from a plurality of position estimates of the vehicle using a satellite positioning system. From the position estimates a determination is made that the path includes a poor satellite signal zone and the path portion is combined with a predetermined path portion corresponding to the poor satellite signal zone to provide the determined path.

Abstract: A vehicle detection system 200 comprises: a receiver 202 arranged to receive data transmissions from a vehicle 210; a camera 204 arranged to capture an image of the vehicle 210; and a processing subsystem 206 arranged to implement a trained model for detecting and/or classifying vehicles in an image. The system 200 is arranged to operate in a first mode wherein: the camera 204 captures an image of a vehicle 210 and the receiver 202 receives a data transmission from the vehicle 210; and the processing subsystem 206 is arranged to use information resulting from the data transmission received and the image to train the model further. The system 200 is also arranged to operate in a second mode wherein: the camera 204 captures an image of a vehicle 210 and the receiver 202 does not receive a data transmission; and the processing subsystem 206 is arranged to use the image and the trained model to detect and/or classify the vehicle 210 in the image.

Abstract: A system including a plurality of warning devices (100) each with a radio transmitter (111) and a positioning system (109). Each warning device is arranged to communicate with at least one other of the warning devices so that at least one of the plurality of warning devices can determine the mutual positions of the plurality of warning devices, determine a pattern from the mutual positions and transmit a specific message corresponding to the pattern.

Abstract: A system including a plurality of warning devices (100) each with a radio transmitter (111) and a positioning system (109). Each warning device is arranged to communicate with at least one other of the warning devices so that at least one of the plurality of warning devices can determine the mutual positions of the plurality of warning devices, determine a pattern from the mutual positions and transmit a specific message corresponding to the pattern.

Abstract: A vehicle detection and classification system which comprises a plurality of proximity sensors is distributed in a fixed spatial array relative to a road such that a distance of each sensor to the nearest adjacent sensor is less than a minimum horizontal dimension of a vehicle to be detected. The array has a maximum dimension greater than the minimum horizontal dimension of a vehicle to be detected. Each of the sensors is configured to determine presence or absence of a vehicle and to communicate data regarding said presence determination to a data processing system, wherein the data processing system is configured to use data from a plurality of the sensors to detect and classify a vehicle on the road based on at least one dimension of the vehicle.

Abstract: A vehicle detection and classification system which comprises a plurality of proximity sensors is distributed in a fixed spatial array relative to a road such that a distance of each sensor to the nearest adjacent sensor is less than a minimum horizontal dimension of a vehicle to be detected. The array has a maximum dimension greater than the minimum horizontal dimension of a vehicle to be detected. Each of the sensors is configured to determine presence or absence of a vehicle and to communicate data regarding said presence determination to a data processing system, wherein the data processing system is configured to use data from a plurality of the sensors to detect and classify a vehicle on the road based on at least one dimension of the vehicle.

Abstract: A vehicle detection and classification system which comprises a plurality of proximity sensors is distributed in a fixed spatial array relative to a road such that a distance of each sensor to the nearest adjacent sensor is less than a minimum horizontal dimension of a vehicle to be detected. The array has a maximum dimension greater than the minimum horizontal dimension of a vehicle to be detected. Each of the sensors is configured to determine presence or absence of a vehicle and to communicate data regarding said presence determination to a data processing system, wherein the data processing system is configured to use data from a plurality of the sensors to detect and classify a vehicle on the road based on at least one dimension of the vehicle.

Abstract: A vehicle (18) having a license plate tamper detection system comprising: a first sensor (4) arranged to provide monitor data corresponding to presence of a first license plate (20) on the vehicle; and a second sensor (6) arranged to provide reference data. The license plate tamper detection system is arranged to compare the monitor data with the reference data and generate a tampering event if the comparison yields more than a predetermined difference between the monitor data and the reference data.

Abstract: A vehicle (18) having a license plate tamper detection system comprising: a first sensor (4) arranged to provide monitor data corresponding to presence of a first license plate (20) on the vehicle; and a second sensor (6) arranged to provide reference data. The license plate tamper detection system is arranged to compare the monitor data with the reference data and generate a tampering event if the comparison yields more than a predetermined difference between the monitor data and the reference data.

Abstract: Method and system for zone based detection of vehicles passing into and out from a geographical zone using polygons to define the border of said zone, wherein a first polygon (32) defines a virtual border against which vehicles passing into the zone is compared by use of a GNSS system (10) comprising an OBU (2) in every vehicle to be surveyed by the method. A second polygon (31) larger than the first polygon (32) and completely enveloping said first polygon is defined as a virtual border against which objects passing out from the zone is compared by use of the GNSS system.

Abstract: A particle filter, also known as Sequential Monte Carlo Method, used to estimate vehicle position based on GNSS pseudo range measurements and other sensors. The particle filter creates an ensemble of particles and road user charging (road tolling) can be performed on each particle. The resulting distribution of assessed vehicle passages is analyzed to assess if the event actually took place or not. The use of particle filter overcomes limitations occurring while employing traditional Kalman filter based methods.

Abstract: A vehicle (18) having a license plate tamper detection system comprising: a first sensor (4) arranged to provide monitor data corresponding to presence of a first license plate (20) on the vehicle; and a second sensor (6) arranged to provide reference data. The license plate tamper detection system is arranged to compare the monitor data with the reference data and generate a tampering event if the comparison yields more than a predetermined difference between the monitor data and the reference data.

Abstract: Method and system for detecting passages by vehicle at a virtual gantry controlled by a GNSS system comprising an OBU in every vehicle to be surveyed by the system, said OBU receiving signals from satellites to consistently and frequently estimate positions for the vehicle, the method comprising the steps of: defining a virtual gantry in terms of a number of ordered passage lines across a road; determining intersection points from the intersection between the GNSS trace and the passage line; calculating a value representing probability of a true passage at that passage line; for each vehicle for which intersection points have been determined for at least two different passage lines, calculating a total probability value based on the individually calculated probability values; concluding of a true passage by the vehicle in question only if the total probability value is exceeding a predefined minimum value.

Abstract: A vehicle (18) having a license plate tamper detection system comprising: a first sensor (4) arranged to provide monitor data corresponding to presence of a first license plate (20) on the vehicle; and a second sensor (6) arranged to provide reference data. The license plate tamper detection system is arranged to compare the monitor data with the reference data and generate a tampering event if the comparison yields more than a predetermined difference between the monitor data and the reference data.

Abstract: A vehicle detection and classification system which comprises a plurality of proximity sensors is distributed in a fixed spatial array relative to a road such that a distance of each sensor to the nearest adjacent sensor is less than a minimum horizontal dimension of a vehicle to be detected. The array has a maximum dimension greater than the minimum horizontal dimension of a vehicle to be detected. Each of the sensors is configured to determine presence or absence of a vehicle and to communicate data regarding said presence determination to a data processing system, wherein the data processing system is configured to use data from a plurality of the sensors to detect and classify a vehicle on the road based on at least one dimension of the vehicle.

Abstract: A particle filter, also known as Sequential Monte Carlo Method, used to estimate vehicle position based on GNSS pseudo range measurements and other sensors. The particle filter creates an ensemble of particles and road user charging (road tolling) can be performed on each particle. The resulting distribution of assessed vehicle passages is analysed to assess if the event actually took place or not. The use of particle filter overcomes limitations occurring while employing traditional Kalman filter based methods.

Abstract: Method and system for detecting passages by vehicle at a virtual gantry controlled by a GNSS system comprising an OBU in every vehicle to be surveyed by the system, said OBU receiving signals from satellites to consistently and frequently estimate positions for the vehicle, the method comprising the steps of: defining a virtual gantry in terms of a number of ordered passage lines across a road; determining intersection points from the intersection between the GNSS trace and the passage line; calculating a value representing probability of a true passage at that passage line; for each vehicle for which intersection points have been determined for at least two different passage lines, calculating a total probability value based on the individually calculated probability values; concluding of a true passage by the vehicle in question only if the total probability value is exceeding a predefined minimum value.