Parking Route Probability Ranking for Faster Destination Parking
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Solution Overview
Problem
Conventional navigation devices often fail to provide accurate and timely information about parking availability near a destination, leading to prolonged and frustrating searches for parking, increased driver distraction, and potential safety risks due to the volatile nature of parking opportunities.
Innovation Solution
A navigation device that utilizes probabilistic methods to evaluate the likelihood of parking availability by aggregating data from multiple sources, including user behavior and real-time updates, to recommend routes with the highest probability of finding available parking spots near a destination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional navigation devices present a list of parking opportunities to users, then users can see available parking locations, but users cannot determine the actual availability and must visit each location sequentially, leading to prolonged search time and driver distraction
Solution Approach 1:
The system performs preliminary actions by proactively gathering real-time parking availability data from multiple sources (sensors, user reports, municipal systems) before the user needs to search for parking. This allows the navigation device to pre-calculate and present the most likely available parking locations, eliminating the need for users to sequentially visit each location and significantly reducing search time.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring parking availability through sensors, user reports, and municipal data systems. This real-time feedback loop allows the navigation device to update parking availability information dynamically, presenting users with current and accurate data about which parking locations are actually available, thereby reducing unnecessary trips to full parking locations.
2Reliability
If users sequentially visit parking opportunities to check availability, then they can find available parking, but driver distraction increases and safety risks are elevated
Solution Approach 1:
The system performs self-service by automatically gathering, verifying, and presenting parking availability information without requiring user intervention. The navigation device autonomously queries multiple data sources, processes the information, and presents the most likely available parking locations to the user, eliminating the need for drivers to manually check each parking location and thereby reducing driver distraction and safety risks.
Solution Approach 2:
The system acts as an intermediary between parking management systems, sensors, and users. It collects data from municipal parking systems, sensors, and user reports, processes this information through algorithms that predict availability, and presents the results to users. This intermediary role consolidates multiple information sources and presents a single, reliable answer, eliminating the need for users to sequentially visit parking locations.
3Loss of information
If navigation devices provide detailed parking opportunity maps, then users can see parking locations, but users may choose unfavorable locations (far away or expensive) when better options exist
Solution Approach 1:
The system changes parameters by transforming raw parking location data into a prioritized list based on multiple parameters including real-time availability probability, distance from destination, cost, and user preferences. The navigation device calculates a composite score for each parking location and presents them in order of likelihood to be the best choice, eliminating the need for users to manually evaluate multiple factors and make complex decisions about which parking location to choose.
Data Source
AI summary
Navigation systems are often tasked with routing a vehicle to a destination. Navigation device configurations are provided and involve probabilistic evaluation of parking routes in a vicinity of a destination that may be generated and compared, and may be optionally weighted by various factors. The evaluation is performed to identify a parking route with parking opportunities that collectively present a high probability of vacancy as compared with other parking routes, which may be presented to the user and/or appended to a current route of an autonomous vehicle.