Parking Spot Availability Prediction Using Roaming Data
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Solution Overview
Problem
Existing solutions for locating parking spots for vehicles fail to accurately predict the availability duration of parking spots and provide insufficient information for drivers to locate them safely, leading to potential safety hazards.
Innovation Solution
A method that uses roaming data from ego vehicles and remote vehicles to estimate the availability of parking spots by aggregating data and employing machine learning to generate historical patterns, providing drivers with the geographic location and estimated availability time of parking spots, and allowing autonomous vehicles to automatically park or guide human drivers to available spots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing solutions are used to locate parking spots, then drivers can find parking locations, but the system cannot accurately predict how long a parking spot will remain available
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical parking spot availability data and vehicle roaming patterns before the actual parking search. This historical data is used to train machine learning models that can predict future availability durations, allowing the system to provide accurate predictions in advance rather than reacting to current conditions only.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual parking spot availability and comparing it with predictions. This feedback loop allows the machine learning model to be continuously refined and improved, increasing prediction accuracy over time. The system learns from actual outcomes to better predict future availability durations.
2Loss of information
If drivers use displays to show parking spot information, then drivers receive detailed information about available parking spots, but driver safety decreases due to increased distraction
Solution Approach 1:
The system introduces an intermediary approach by using autonomous vehicles or advanced driver assistance systems as mediators between the driver and the parking search task. These intermediaries automatically handle the complex task of locating and navigating to parking spots, reducing the cognitive load and visual attention required from the driver, thereby minimizing distraction while still providing necessary information.
Solution Approach 2:
The system enables self-service functionality where the autonomous vehicle system independently performs the parking search and navigation tasks without requiring continuous driver intervention. The system autonomously processes parking spot information, evaluates availability, and guides the vehicle to the destination, freeing the driver from the distraction of manually monitoring displays for parking information.
Data Source
AI summary
The disclosure describes a method for an ego vehicle. The method includes providing roaming data to a server, the roaming data describing a roaming pattern of the ego vehicle in a geographic area as a function of time. The method further includes providing a request to the server that describes a need for the ego vehicle to park. The method further includes receiving, from the server, a geographic location of an available parking spot and an estimated length of time the available parking spot will remain available.