Vehicle Transit Detection via Coordinate Transformation
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Solution Overview
Problem
Free-flow toll collection systems face challenges in accurately correlating data from sensors with different coverage areas, especially when vehicles change speed or follow non-rectilinear trajectories, leading to potential identification errors.
Innovation Solution
An apparatus and method that includes a tracking device providing information on the vehicle's trajectory, allowing data processing units to correlate sensor data by identifying positions associated with specific times, determining a comparison area that overlaps with sensor coverage areas, ensuring accurate correlation regardless of vehicle speed or trajectory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If sensors are positioned at different locations along the carriageway to detect vehicle data, then the coverage area of each sensor can be optimized for its specific function, but the coverage areas of different sensors become non-overlapping, making it difficult to correlate data from multiple sensors for the same vehicle
Solution Approach 1:
The patent introduces an intermediary coordinate transformation mechanism that mediates between sensors with non-overlapping coverage areas. By transforming detection coordinates from different sensor reference frames into a unified global coordinate system, the system enables reliable correlation of vehicle data across sensors without requiring physical overlap of coverage areas. This intermediary transformation layer resolves the contradiction by decoupling the spatial separation of sensors from the data correlation requirement.
2Length of stationary object
If sensors are positioned far from the gate to detect vehicle trajectory and speed, then the coverage area extends further along the carriageway, but the position of the sensor coverage area becomes significantly different from the gate position, complicating data correlation
Solution Approach 1:
The patent applies parameter transformation by changing the reference frame parameters from sensor-local coordinates to gate-centered coordinates. The coordinate transformation adjusts for differences in position, orientation, and scale between sensor coverage areas and the gate reference frame. This parameter change enables straightforward data correlation despite large spatial separations, reducing the complexity of correlating trajectory and speed data from distant sensors with gate detection data.
3Reliability
If multiple sensors are used to detect different vehicle parameters, then the redundancy of data acquisition improves detection reliability, but the difficulty of detecting and measuring increases due to needing to correlate data from multiple sensors with different coverage areas
Solution Approach 1:
The patent segments the data correlation process into independent coordinate transformation steps for each sensor. Instead of attempting to correlate all sensor data simultaneously, the system transforms each sensor's detection coordinates independently into the gate reference frame. This segmentation of the correlation process reduces the overall difficulty by breaking down the complex multi-sensor correlation problem into manageable individual transformations, while maintaining the reliability benefits of multiple sensors.
Data Source
Figure 1(a)~1(b)
Figure 2
Figure 3(a)~3(c)
AI summary
A system and a method for detecting the transit of a vehicle are described. The apparatus comprises sensors that provide data (registration number, speed, class, etc.) relative to the vehicles in transit. The apparatus also comprises a tracking device that provides information indicative of a sequence of positions of the vehicle through a tracking area, which includes the coverage areas of all the sensors of the apparatus. Each position is associated with the time at which the vehicle passed through that position. To determine whether the data detected by a sensor at a detection time tE are relative to the vehicle, at least one position is identified, in the sequence of positions, associated with a time in a range about tE and a comparison area is determined based thereon. If the comparison area overlaps the coverage area of the sensor that detected the data, it is concluded that these data relate to the vehicle.