Road Course Deviation Detection via Map Matching Error Analysis
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Solution Overview
Problem
Current traffic information systems fail to accurately identify and provide information on temporary deviations in road courses, such as those caused by roadworks, which can impact traffic flow and driver safety, as they do not consider the underlying cause of reduced traffic flow or detect lateral deviations in road courses.
Innovation Solution
A method and system that utilize positional data from vehicles to determine deviations in road courses by calculating map matching errors, allowing for the identification of start and end points, direction, and duration of deviations without relying on third-party information or additional infrastructure, particularly suitable for temporary deviations like roadworks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traffic information systems use conventional methods to monitor traffic flow, then traffic flow data can be collected, but the underlying cause of reduced flow (such as road course deviations) cannot be identified
Solution Approach 1:
The patent uses map matching as an intermediary process to bridge the gap between GPS position data and electronic map data. By comparing the actual vehicle positions with expected positions on the road network, the system indirectly detects road course deviations without requiring direct measurement infrastructure. This intermediary approach enables detection of deviations while using existing data collection systems.
2Loss of information
If third-party traffic messages are used to indicate road works, then information on affected stretches can be provided, but information on actual course deviation is not available
Solution Approach 1:
The system implements feedback by continuously comparing actual vehicle positions (from GPS data) with expected positions (from map matching against electronic maps). When deviations are detected, this feedback information is used to identify and characterize road course deviations, providing reliable, empirically-based information about actual road conditions rather than relying solely on third-party reports.
3Measurement precision
If additional infrastructure is deployed to detect road course deviations, then detection accuracy can be improved, but system cost and complexity increase
Solution Approach 1:
The system uses existing vehicle-mounted GPS receivers and electronic map data to perform self-service detection of road course deviations. Instead of requiring external detection infrastructure, the system leverages data already being collected by vehicles themselves, combined with map matching algorithms, to identify deviations autonomously.
Solution Approach 2:
The patent makes existing GPS positioning systems and electronic maps serve multiple functions: not only navigation and routing, but also detection of road course deviations. By analyzing map matching errors, the same infrastructure that provides basic navigation services also enables deviation detection, eliminating the need for dedicated detection infrastructure.
4Measurement precision
If map matching error analysis is performed on positional data, then road course deviations can be detected, but data processing complexity increases
Solution Approach 1:
The system extracts specifically the map matching error component from the overall GPS positioning data. By isolating and analyzing only the deviation between actual and expected positions (the map matching error), the system focuses computational resources on the relevant signal for deviation detection, rather than processing all aspects of positioning data equally.
Data Source
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AI summary
A method and system for identifying a lateral deviation in the course of a road segment is described. Positional data is collected from a plurality of vehicles travelling along a road stretch. A map matching error associated with the position data is used to determine an average map matching error and average absolute map matching error for travel along the road stretch. A lateral deviation in course is identified when the average map matching error measures are both above a given threshold, and where the detected deviation is of a given minimum length. Other indicators of deviation can be considered including a direction of the possible deviation, and impact on travel in the opposite driving direction. Data relating to the determined deviation is generated, including a speed of travel through the deviated region, and used to enhance traffic information.