Edge Vehicle Identification for Seamless Parking Gate Throughput

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Solution Overview

Problem

Existing parking systems require user interaction at entry and exit, leading to traffic bottlenecks and inefficiencies due to user errors and manual processing delays.

Innovation Solution

An edge device equipped with machine learning models processes images from cameras to identify vehicles and track their movement, enabling automated entry and exit by matching entry and exit data sets without the need for cloud-based data processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If cloud server processes images, then centralized data processing is achieved, but network resources are excessively consumed and latency increases

Engineering Contradiction:

Improveautomated vehicle identificationVSAvoidprocessing latency

Core Design Contradiction:

Extent of automationVSLoss of time

Solution Approach 1:

The system segments the centralized cloud processing function into distributed edge computing nodes deployed at strategic locations. Each edge device independently processes images locally, eliminating the need to transmit large image files to the cloud and reducing processing latency while maintaining automated vehicle identification capabilities.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Edge devices serve as intermediary components between cameras and cloud servers. They perform preliminary image processing and vehicle identification locally, then communicate only essential results to the cloud, thereby reducing network resource consumption and processing time.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of operation

If manual ticket collection is used, then user interaction is required, but traffic bottlenecks and processing delays occur

Engineering Contradiction:

Improveuser interaction requirementVSAvoidgate processing throughput

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system implements self-service through automated vehicle identification and recognition. Vehicles are automatically identified via edge devices and machine learning models, and gates are automatically controlled based on recognition results, eliminating the need for user interaction at tickets collection and gate operations, thereby increasing processing throughput.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical ticket collection and verification process is replaced with an automated optical recognition system. Cameras capture images, edge devices process them using machine learning models to identify vehicles, and this information automatically controls gate operation, substituting manual mechanical operations with automated electronic systems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12592056B2Machine learning and computer vision solutions to seamless vehicle identification and environmental tracking therefor

Publication Date: 2026.03.31 METROPOLIS IP HOLDINGS LLC

AI summary

A device captures a series of images over time in association with a gate, each image having a timestamp. The device determines, for a vehicle approaching the entry side, from a subset of images of the series of images featuring the vehicle, a first data set comprising a plurality of parameters that describe attributes of the vehicle by inputting the subset of images into a first machine learning model and a vehicle identifier of the vehicle by inputting images of the subset featuring a depiction of a license plate of the vehicle into a second machine learning model. The device stores the data set in association with one or more timestamps with the subset of images, determines a second data set for a second vehicle approaching the exit side, and responsive to determining that the first data set and the second data set match, instructs the gate to move.