ML Parking Space Detection Using Vehicle Engine On-Off Data
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Solution Overview
Problem
Locating public parking spaces is time-consuming, frustrating, and inefficient, especially in urban environments, leading to wasted time, fuel, and increased pollution.
Innovation Solution
A vehicle platform utilizing a machine learning model processes geographical and vehicle data to identify public parking spaces and provides real-time notifications, reducing the time required to find available spaces by dividing areas into clusters and analyzing engine on/off conditions and locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a driver manually searches for public parking spaces by driving around and visually inspecting areas, then the driver can locate available parking spaces, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by proactively monitoring parking space availability and notifying drivers before they arrive at the destination. The server continuously tracks parking space status and sends advance notifications to drivers about available public parking spaces, eliminating the need for drivers to manually search upon arrival.
Solution Approach 2:
A server acts as an intermediary between parking spaces and drivers. The server receives data from vehicle devices about engine off conditions and locations, processes this information to identify public parking spaces, and communicates available spaces back to drivers through notifications, simplifying the parking search process.
2Reliability
If drivers search for parking spaces by driving around extensively, then they may find available spaces, but fuel is wasted and pollution increases
Solution Approach 1:
The system provides advance notification to drivers about available public parking spaces before they arrive, allowing them to plan their route directly to available spaces without unnecessary driving. This preliminary information enables drivers to avoid circling and idling, thereby reducing fuel consumption and pollution.
3Measurement precision
If the system monitors all vehicle data continuously to identify public parking spaces accurately, then parking space identification precision improves, but data processing complexity and energy consumption increase
Solution Approach 1:
The system applies different processing levels to different data sources. Vehicle devices locally filter and process their own engine status and location data to generate concise reports. The server then processes these pre-filtered reports from multiple vehicles to identify public parking spaces, distributing the computational complexity across multiple levels rather than centralizing all processing.
Data Source
AI summary
A device may receive geographical data identifying a geographical area, and may receive, from vehicle devices of vehicles, first vehicle data identifying engine off conditions, locations during engine off conditions, and durations of the engine off conditions. The device may divide, based on the geographical data, the geographical area into clusters with particular dimensions, and may process data identifying the clusters and the first vehicle data, with a machine learning model, to determine parking data identifying public parking spaces in the geographical area. The device may receive, from a set of the vehicle devices associated with vehicles parked in the public parking spaces, vehicle data identifying engine on conditions and locations during the engine on conditions, and may identify available public parking spaces based on the second vehicle data and the parking data. The device may perform one or more actions based on data identifying the available public parking spaces.