Lane Count Detection Using Spectral Analysis and Probe Data

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Solution Overview

Problem

Current methods for automatically determining the number of lanes in a road are inaccurate, particularly for roads with two or five lanes, leading to significant manual labor and high error rates in generating map data for navigation devices.

Innovation Solution

A computer-implemented method using spectral analysis of photographic images to identify lane dividers by searching for characteristic spectral signatures, filtering images to confine searches within road boundaries, and combining image data with probe data to determine lane counts with a confidence level assessment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automatic analysis of probe data is used to determine lane count, then time and manual labor are reduced, but accuracy of lane count identification deteriorates

Engineering Contradiction:

Improvetime and manual laborVSAvoidaccuracy of lane count identification

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent combines probe data analysis with image data analysis to determine lane count. The system processes both types of data together and uses a confidence level metric to evaluate the quality of the combined analysis results, thereby improving accuracy while maintaining automation benefits

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system calculates a confidence level based on the agreement between probe data and image data analysis results. This feedback mechanism allows the system to identify when automatic analysis is reliable and when manual verification may be needed, improving overall measurement precision

Inventive Principle:

Principle #23Feedback

2Measurement precision

If manual identification of lane count is used, then accuracy is maintained, but time consumption and labor costs increase

Engineering Contradiction:

Improveaccuracy of lane count identificationVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs automatic self-analysis of both probe data and image data to determine lane count and assess confidence levels without requiring constant manual intervention. This automated self-service approach maintains high accuracy while significantly reducing time consumption compared to purely manual methods

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical analysis with automated computer-based analysis of probe data and image data. The system uses algorithms to process data and determine lane count, substituting human labor with automated computational methods that maintain accuracy while reducing time loss

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If spectral analysis of images is performed to identify lane dividers, then lane count accuracy is improved, but processing complexity increases

Engineering Contradiction:

Improvelane count accuracyVSAvoidprocessing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system extracts spectral signatures from image data to identify lane dividers. By isolating and analyzing specific spectral characteristics rather than processing entire images, the method improves lane count accuracy while managing processing complexity through targeted feature extraction

Inventive Principle:

Principle #2Taking out (Extraction)

4Productivity

If search for lane dividers is confined to within road boundaries, then processing efficiency is improved, but risk of missing lane dividers increases

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidcompleteness of lane divider identification

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary identification of road boundaries before searching for lane dividers. By establishing the search area in advance based on road boundary detection, the system improves processing efficiency while maintaining reliability through a structured two-stage approach that ensures lane dividers within boundaries are not missed

Inventive Principle:

Principle #10Preliminary action

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

The method significantly improves the accuracy of lane counting, increasing correct identifications by 13.5% compared to probe data alone, enabling more reliable generation of map data for navigation systems.

Implementation Method 1

carrying out a spectral analysis to identify lane dividers of the road

Methodology Applied

Scientific EffectSpectral analysis: Absorption Spectroscopy

Implementation Method 2

The spectral analysis may comprise generating an image frequency spectrum by carrying out a Fourier transform of the image

Methodology Applied

Scientific EffectFourier transform:

Data Source

PatentUS9355321B2Automatic detection of the number of lanes into which a road is divided

Publication Date: 2016.05.31 TOMTOM GLOBAL CONTENT

AI summary

This invention concerns a computer-implemented method for determining a number of lanes on a road. The method comprises receiving an image, the image being a photographic image of the road or an image derived from the photographic image of the road, carrying out an analysis of the image to identify lane dividers of the road and determining a number of lanes into which the road is divided from the identification of the lane dividers. The method may comprise determining a confidence level for the determined value for the number of lanes. Map data of a plurality of roads may be generated using the value for the number of lanes determined from the computer-implemented method.