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: 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 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.

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.