Pedestrian-Type Mapping for Automated Geographic Zone Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for classifying geographic areas into different zones for autonomous driving and mapping are inefficient due to high manual labor costs, limiting the extent and frequency of mapped areas and updates.
Innovation Solution
A system using computer vision to identify pedestrian types through image data processing, which determines and classifies geographic zones based on detected pedestrian types, generating and updating digital map data to improve zone classification and mapping efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to classify geographic areas into zones, then classification accuracy can be maintained, but labor costs increase and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical classification processes with an automated computer vision system that processes images to detect pedestrian types and automatically classifies geographic zones. This substitution eliminates manual labor while maintaining classification accuracy through algorithmic analysis of visual data.
Solution Approach 2:
The system enables self-service classification by allowing the computer vision algorithm to autonomously process images, identify pedestrian types, and generate zone classifications without human intervention. The automated pipeline independently completes the entire classification workflow, from image processing to map data generation.
2Quantity of substance
If manual classification methods are used, then resource consumption is controlled, but the extent and frequency of mapped areas are limited
Solution Approach 1:
The patent changes the operational parameters of the classification system by transitioning from manual processing to automated computer vision processing. This parameter change enables the system to handle larger volumes of image data and process more geographic areas simultaneously, thereby increasing coverage without proportionally increasing resource consumption.
Solution Approach 2:
The system segments the mapping task into discrete image processing units that can be independently analyzed. By dividing the geographic area into smaller zones based on pedestrian type detection in individual images, the system can efficiently process and classify multiple areas in parallel, expanding overall coverage.
3Productivity
If automated computer vision systems are implemented, then productivity and coverage increase, but system complexity increases
Solution Approach 1:
The computer vision system performs multiple functions within a single integrated platform: image processing, pedestrian type detection, geographic zone classification, and digital map data generation. This multi-functionality consolidates what would otherwise require separate systems, managing complexity while maintaining high productivity.
4Device complexity
If manual labor is used for zone classification, then system simplicity is maintained, but labor costs increase
Solution Approach 1:
The patent substitutes manual human labor with an automated computer vision system that processes images and classifies zones algorithmically. This replacement eliminates the need for human workers while maintaining system operational simplicity through standardized automated procedures.
Data Source
AI summary
An approach is provided for mapping based on pedestrian type. The approach, for instance, involves processing image data to a determine the pedestrian type of at least one pedestrian depicted in the image data. The approach also involves determining a classification of a geographic zone based on the detected pedestrian type. The approach further involves generating a digital map representation of the geographic zone based on the classification.