Split Lane Traffic Detection Using Camera and Probe Data Fusion
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Solution Overview
Problem
Current methods for detecting split lane traffic conditions are inaccurate due to variations in probe data quality and precision, leading to inconsistent lane-level traffic information, which affects navigation systems and autonomous vehicle operations.
Innovation Solution
A method that combines multimedia data from cameras with probe data to identify multi-modality conditions along road segments, using image analysis to estimate vehicle volume and adjust detection algorithm parameters for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If probe data is used to detect split lane traffic conditions, then lane-level traffic information can be obtained, but the accuracy varies due to variations in probe data quality and GPS precision
Solution Approach 1:
The patent combines multiple data sources including probe data from GPS, multimedia data from cameras, and probe data from mobile devices to detect split lane traffic conditions. This fusion of diverse data sources compensates for the limitations of individual sources, providing more consistent and reliable lane-level traffic information regardless of probe data quality variations or GPS precision issues.
2Measurement precision
If only probe data is used for detection, then the system remains simple, but the accuracy of bi-modality condition identification varies from junction to junction
Solution Approach 1:
The patent adjusts detection parameters and thresholds based on junction-specific characteristics and traffic conditions. By dynamically modifying detection parameters according to the particular junction geometry, traffic volume, and environmental factors, the system achieves consistent bi-modality condition detection accuracy across diverse junction types rather than using fixed parameters for all locations.
3Adaptability or versatility
If probe data quality varies, then data collection remains flexible, but the resulting lane level traffic information becomes inconsistent
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously evaluates the quality and consistency of probe data received, and adjusts its detection algorithms and parameter thresholds accordingly. When probe data quality deteriorates or becomes inconsistent, the system adapts by relying more heavily on alternative data sources such as camera feeds or adjusting detection sensitivity, thereby maintaining consistent lane-level traffic information output despite variations in input data quality.
Data Source
AI summary
A method, apparatus and computer program product are provided to improve the identification and characterization of split lane traffic and other multi-modality traffic conditions. In the context of a method, multimedia data from a camera is received that is representative of traffic conditions along a road segment upstream of a junction. The method identifies a multi-modality condition along the road segment upstream of the junction based upon an analysis of the multimedia data. The method also includes evaluating a plurality of probe points representative of travel along the road segment upstream of the junction in accordance with a detection algorithm to separately determine whether the multi-modality condition exists along the road segment upstream of the junction. The method further includes determining whether to modify one or more tuning parameters of the detection algorithm based upon the multi-modality condition identified based upon the analysis of the multimedia data.