Roadside Unit Topology Detection via Vehicle Vector Analysis
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Solution Overview
Problem
Manual configuration of roadside units in V2R communication systems is time-consuming, costly, and requires frequent updates due to changing external conditions, making it inefficient for traffic infrastructure monitoring.
Innovation Solution
A method using a learning algorithm in roadside units to determine vehicle positions and calculate vectors to infer traffic flow topology, eliminating the need for manual recording and configuration, and allowing for periodic recalibration to compensate for GPS inaccuracies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recording and configuration of geographical topology is performed, then precision of topology data is improved, but loss of time and cost increase significantly
Solution Approach 1:
The receiving unit automatically detects and determines the geographical topology of traffic infrastructure by itself, using vehicle status data and vector calculations, without requiring manual configuration or external assistance. This self-service approach eliminates the time-consuming manual recording process while maintaining high precision through algorithmic accuracy.
Solution Approach 2:
The system changes the approach from static manual configuration to dynamic automatic detection by utilizing real-time vehicle position data and calculating vectors based on movement patterns. This parameter change enables the system to adapt to changing external conditions automatically, eliminating the need for repeated manual updates.
2Manufacturing precision
If manual configuration is performed with great precision, then manufacturing precision of topology data is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical manual configuration process with an automated computational system that uses vehicle status data and vector mathematics to determine geographical topology. This substitution eliminates the need for manual intervention while reducing complexity through algorithmic automation, maintaining precision through mathematical calculations rather than human effort.
3Adaptability or versatility
If manual configuration is updated frequently due to changing conditions, then adaptability of the system is improved, but loss of time and cost increase
Solution Approach 1:
The system transitions from static manual configuration to dynamic automatic detection, where the receiving unit continuously determines geographical topology based on real-time vehicle movement data. This dynamic approach enables the system to automatically adapt to changing external conditions such as reconstruction work or new road sections without requiring manual updates, maintaining high adaptability while eliminating repeated configuration time.
Data Source
AI summary
The invention relates to a method for the geographic area detection of traffic infrastructure by means of a receiving unit arranged in the area of the traffic infrastructure for a detection area of the receiving device, wherein the method comprises the following steps: - Status data from several vehicles, which status data each include at least a position of the respective vehicle and optionally a time of position determination, are transmitted by the vehicles several times while the vehicles cross the detection area (2) of the receiving device (1) by means of wireless communication to the receiving unit (1);- the receiving unit (1), or a computing unit connected to the receiving unit, calculates a vector (3) for each vehicle, wherein the vector (3) extends from a first, in particular the first, received position (4) of the vehicle in the detection area (2) to a second, in particular the last, received position (5) of the vehicle in the detection area (2); - from the vectors (3) of all vehicles, the receiving unit (1) or computing unit determines the directions of travel (6) of the vehicles as well as the geographical location of carriageways (7) and/or lanes (8) of the carriageways (7) of the traffic infrastructure in the detection area (2).;