Machine Learning Parking Spot Occupancy Detection

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Solution Overview

Problem

Finding parking is a time-consuming and environmentally detrimental issue worldwide, with approximately 30% of vehicle emissions attributed to drivers searching for parking, contributing to the global climate crisis.

Innovation Solution

The use of machine learning-based systems, including image sensors and electronic hardware devices, to determine parking spot availability by processing images and communicating occupancy likelihoods to users, utilizing various machine learning models for real-time and predictive parking availability, even in challenging conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If drivers search for parking manually, then they can find parking spots, but it consumes excessive time and creates harmful emissions

Engineering Contradiction:

Improvetime to find parkingVSAvoidvehicle emissions

Core Design Contradiction:

Loss of timeVSObject-generated harmful factors

Solution Approach 1:

The patent introduces an intermediary system consisting of image sensors, processors, and communication devices that mediate between parking spots and drivers. The system captures images of parking spots, processes them to determine occupancy status, and communicates availability to drivers through electronic displays, eliminating the need for drivers to manually search for parking

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the mechanical manual search process with an automated electronic system. Instead of drivers physically driving around and visually checking parking spots, the system uses image sensors to capture images, processors to analyze occupancy status, and communication devices to transmit information, substituting electronic automation for manual mechanical search

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If image sensors and machine learning models are deployed to determine parking availability, then parking spot occupancy can be accurately detected, but device complexity increases

Engineering Contradiction:

Improveparking occupancy detection accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the parking detection system into distinct functional modules: image sensors for capturing images, processors for analyzing occupancy status, and communication devices for transmitting information. This segmentation allows each component to perform its specific function independently, improving detection accuracy while managing complexity through modular architecture

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs machine learning models that automatically analyze images and determine parking occupancy without requiring manual intervention. The processors self-service by autonomously processing images, identifying vehicles, and determining occupancy status, reducing the need for complex manual monitoring systems

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12183201B1Machine learning parking determination

Publication Date: 2024.12.31 ARORA KRISH

AI summary

Apparatuses, systems, methods, and computer program products for machine learning parking determination. A method includes receiving an image of a parking spot taken by an image sensor. A method includes processing an image of a parking spot using a machine learning model to determine a likelihood that the parking spot is occupied. A method includes communicating to a user, on an electronic display screen of a hardware computing device, whether a parking spot is occupied or available based on a determined likelihood that the parking spot is occupied.