Turn Lane Configuration via Probe Data Analysis

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Solution Overview

Problem

Current methods for identifying and coding turn lanes are labor-intensive, time-consuming, and expensive, lacking efficient and automated techniques for determining turn lane data from probe data.

Innovation Solution

The method involves analyzing probe data for intersections by dividing it into turn traces and through traces, calculating heading change data, and identifying local extrema points to determine the characteristics of turn lanes, such as number and location, using a system comprising a server, mobile device, and database to automate the process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual observation of videos or real-life logging is used to identify turn lanes, then turn lane data can be collected, but the process becomes labor intensive, time consuming, and expensive

Engineering Contradiction:
Improveturn lane identification accuracyVSAvoiddata collection efficiency
Core Design Contradiction:
Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical observation and logging processes with an automated computer-based system that processes probe data. The system automatically identifies turn lanes by analyzing probe vehicle trajectories and calculating heading changes, eliminating the need for human technicians to manually watch videos or log data in real-life scenarios.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables turn lane identification to serve itself by automatically processing probe data without human intervention. The automated algorithm independently performs data collection, analysis, and turn lane identification, making the system self-sufficient and eliminating dependency on manual labor.

Inventive Principle:
Principle #25Self-service

2Productivity

If automated techniques are implemented to improve productivity, then data collection speed increases, but system complexity increases

Engineering Contradiction:
Improvedata collection efficiencyVSAvoidautomation system complexity
Core Design Contradiction:
ProductivityVSDevice complexity

Solution Approach 1:

The automated system is divided into distinct functional modules: probe data reception module, trace generation module, heading change calculation module, and turn lane identification module. Each module performs a specific task, making the overall complex system manageable through functional segmentation and independent development of each component.

Inventive Principle:
Principle #1Segmentation

3Loss of time

If probe data analysis is used to automate turn lane identification, then labor and time requirements decrease, but measurement and detection difficulty increases

Engineering Contradiction:
Improvedata collection timeVSAvoidprobe data analysis complexity
Core Design Contradiction:
Loss of timeVSDifficulty of detecting and measuring

Solution Approach 1:

The patent introduces trace data as an intermediary representation that simplifies the analysis of probe data. Instead of directly analyzing raw probe data points, the system first generates traces that represent vehicle paths, then calculates heading changes from these traces. This intermediary step makes the measurement and detection process more manageable.

Inventive Principle:
Principle #24Intermediary (Mediator)

Data Source

PatentEP3064901B1Turn lane configuration
Publication Date: 2020.09.23 HERE GLOBAL BV
  • EP3064901B1 patent drawingFigure 1
  • EP3064901B1 patent drawingFigure 2
  • EP3064901B1 patent drawingFigure 3

AI summary

Systems, methods, apparatuses and computer program code are described for determining turn lane data from probe data. Probe data for an intersection including locations and headings is identified. The probe data for the intersection is divided into a first trace and a second trace based on locations. The difference in the headings, heading change data, is calculated. From the heading change data, at least two local extrema points are identified. A characteristic of the turning lane is determined based on the at least two local extrema points.