Probabilistic Parking Route Selection for Vacancy Search
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Solution Overview
Problem
Conventional navigation devices fail to effectively assist users in finding available parking opportunities near a destination, leading to prolonged and frustrating searches, increased driver distractability, and potential safety risks due to the volatile nature of parking availability.
Innovation Solution
A navigation device that utilizes a probabilistic approach to identify routes with the highest likelihood of finding available parking by aggregating and comparing parking probabilities based on various factors such as time, events, and vehicle data, providing users with a recommended route to maximize the chances of securing a vacant parking spot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional navigation devices present a list of parking opportunities to the user, then the user can see multiple options, but the user cannot determine which opportunities have available parking and must visit each sequentially, leading to prolonged search time and driver distractability
Solution Approach 1:
The system performs preliminary actions by proactively gathering and analyzing parking availability data from multiple sources (sensors, user reports, historical data) before the user needs to park. It calculates probability scores in advance and presents pre-evaluated parking opportunities with availability indicators, eliminating the need for sequential visits to each location.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring parking availability through sensor data from vehicles already at destinations, user reports of occupied/vacant spots, and historical parking patterns. This real-time feedback is used to update probability scores and dynamically adjust parking recommendations, allowing users to see current availability status before traveling.
2Ease of operation
If the user visits multiple parking opportunities sequentially to find available parking, then the user can locate a vacant spot, but the driver's attention is divided among navigation, parking search, and vehicle operation, increasing safety risks
Solution Approach 1:
The system performs self-service by automatically evaluating parking opportunities and calculating availability probabilities without requiring active user intervention. The navigation device autonomously presents pre-screened options with availability indicators, allowing the driver to make decisions with minimal cognitive load and attention diversion.
Solution Approach 2:
The system prepares parking recommendations in advance by analyzing availability data before the user begins navigation. By the time the user needs to park, the system has already identified and ranked suitable opportunities with probability scores, eliminating the need for on-the-spot searching and decision-making while driving.
3Loss of information
If conventional navigation devices display parking opportunities without availability information, then the device can show multiple options, but the user may choose unfavorable locations or pass available spots, reducing parking efficiency
Solution Approach 1:
The system incorporates real-time feedback loops that continuously update parking availability information based on sensor data from the environment, reports from other users, and historical patterns. This feedback mechanism ensures that the displayed probability scores and availability indicators reflect current conditions, enabling users to make informed decisions about which parking opportunities to pursue.
Solution Approach 2:
The system performs preliminary data gathering and analysis by collecting parking availability information from multiple sources before presenting options to the user. It calculates probability scores in advance based on sensor readings, user reports, and historical data, so that when the user views parking opportunities, the information is already evaluated and ready for decision-making.
Data Source
AI summary
Contemporary navigation systems are often tasked with routing a vehicle to a destination, but are poorly equipped to assist with finding a vacancy of a parking opportunity in which the vehicle may be parked. Static information, such as enumeration of parking garages and curbside parking meters, may be frustrating if such parking opportunities have no vacancies. The determination of vacancies with certainty may be difficult to achieve due to the characteristic volatility of parking, in which vacancies may be taken in seconds. Presented herein are navigation device configurations involving probabilistic evaluation of parking routes in a vicinity of a destination that may be generated and compared, optionally weighted by various factors, 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.