Crowdsourced Digital Road Map Update via Vehicle Trajectory Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital road map updating methods are inadequate, as they provide fewer than four map updates per year, failing to effectively maintain accurate and up-to-date road network information.
Innovation Solution
A computer system and method utilizing crowdsourcing, where road vehicles equipped with position sensors transmit geographical coordinates to a data collection server, which processes these data to extract trajectory curves, detect inflection points, group vertices, select central points, form road segments, and update the digital road map, optionally calculating regression functions and adapting segment shapes based on statistical dispersion and redundant segment identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional digital road map updating methods are used, then the map update frequency is low (fewer than four updates per year), but the system complexity and data processing requirements are not significantly increased
Solution Approach 1:
The system leverages the existing position sensors in road vehicles to automatically collect geographical data without requiring dedicated mapping vehicles or manual surveying equipment. The vehicles serve dual purposes: transportation and map updating, eliminating the need for specialized infrastructure.
Solution Approach 2:
The patent replaces traditional mechanical surveying methods with automated electronic data collection using GPS position sensors in vehicles. The mechanical process of manual mapping is substituted by electronic geolocation data transmission and automated processing algorithms.
2Measurement precision
If crowdsourcing data from multiple vehicles is collected, then the accuracy and detail of road map updates improve, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex data processing task into distinct operational steps: extracting trajectory curves from raw position data, detecting inflection points, grouping vertices into classes, selecting representative points, forming road segments, and merging them into digital road sections. This segmentation makes the complex process manageable and systematic.
Solution Approach 2:
The system performs preliminary processing by extracting trajectory curves and identifying inflection points before final road segment formation. This preliminary action organizes raw data into intermediate structures that facilitate the subsequent grouping and road section creation processes.
3Duration of action of moving object
If continuous data collection from vehicles is implemented, then the regularity of map updates improves, but the energy consumption and data transmission requirements increase
Solution Approach 1:
The system maintains continuous data collection by leveraging the ongoing operation of vehicles on road networks. Instead of periodic surveys, the position sensors continuously track vehicle locations, ensuring that map updating is an ongoing process that reflects current road conditions without requiring additional energy-intensive dedicated survey missions.
Data Source
AI summary
Computer systems and methods for updating and/or supplementing a digital road map through crowdsourcing, based on the generalization of geolocation systems that are integrated in the majority of modern road vehicles. The signals collected by these geolocation systems are used to update and/or supplement a digital road map through crowdsourcing. The collected data make it possible to extract data from geographical traces associated with vehicles traveling the road network and: extracting, for each geographical trace, a trajectory curve passing substantially through all of the measurements of the geographical trace; detecting the inflection points (vertices) of each trajectory curve; grouping together all of the vertices into a plurality of vertex classes, using an unsupervised classification algorithm; selecting the most central vertex in each vertex class (representative); forming, from each geographical trace, a road segment between representatives that successively intersect the course of the geographical trace when they are considered in pairs.