AI Parking Allocation Using Sensor Fusion and User Profiles
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Solution Overview
Problem
Existing parking solutions lack accurate evaluation of parking spots based on vehicle profiles using audio, video, RF, and imaging data, and do not utilize fine-tuned parking models for improved accuracy and efficiency.
Innovation Solution
An AI-based system that integrates machine learning and blockchain technology to process user and sensory data, generating predictive models for real-time parking spot allocation by analyzing user profiles and historical data, ensuring secure and transparent transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual parking allocation methods are used, then the system is simple to operate, but the accuracy and efficiency of parking spot allocation deteriorates
Solution Approach 1:
The patent replaces manual mechanical parking allocation processes with an AI-based automated system that uses machine learning models, sensors, and computational algorithms to automatically evaluate parking spots and allocate them to vehicles, thereby improving accuracy while managing complexity through automation
Solution Approach 2:
The system enables self-service parking allocation where the AI model autonomously evaluates parking spots based on sensor data, vehicle profiles, and historical information without requiring manual intervention, allowing the system to serve itself in making allocation decisions
2Ease of operation
If basic automation is implemented to allow drivers to find vacant spots, then the ease of operation improves, but the accuracy of parking spot evaluation deteriorates
Solution Approach 1:
The patent introduces an AI intermediary layer between the driver and the parking spot evaluation process. The machine learning model acts as a mediator that processes sensor data, vehicle profiles, and historical information to provide accurate evaluations, while the driver simply receives recommendations without needing to understand the underlying complexity
Solution Approach 2:
The system replaces basic automated spot-finding with AI-powered evaluation that uses multiple data sources including audio, video, RF, and imaging data, substituted by fine-tuned parking models that provide superior accuracy while maintaining user-friendly operation
3Measurement precision
If comprehensive sensor data collection is implemented, then the accuracy of parking spot evaluation improves, but the device complexity and data processing requirements worsen
Solution Approach 1:
The patent merges multiple types of sensor data (audio, video, RF, imaging) with vehicle profile information and historical data into a unified evaluation framework. The machine learning model integrates these diverse data sources to comprehensively evaluate parking spots, achieving high accuracy through data fusion rather than relying on any single sensor type
Solution Approach 2:
The system employs a universal AI evaluation model that can process multiple types of data from various sensor sources and apply them to different parking spot evaluation scenarios. This multi-functional approach allows the same underlying system to handle diverse data formats and evaluation criteria without requiring separate specialized systems for each data type
Data Source
AI summary
A system for an automated processing of parking data based on user-related data including a processor of a parking processing server node configured to host a machine learning (ML) module and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to receive a parking request comprising user profile data from the at least one user-entity node, derive the user profile data from the parking request, acquire sensory data from a vicinity of at least one vacant parking spot, parse the sensory data based on the user profile data to derive a plurality of key classifying features, query a local parking database to retrieve local historical parking spot allocation'-related data based on the plurality of key classifying features, generate at least one classifier feature vector based on the plurality of key classifying features and the historical parking spot allocation'-related data, provide the at least one classifier feature vector to the ML module configured to generate a predictive model for producing at least one parking recommendation parameter and generate a parking allocation verdict based on the at least one parking recommendation parameter and provide a verdict-related notification to the at least one user-entity node.