Vehicle Parking Optimization Using Availability Probability
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Solution Overview
Problem
Current methods for finding parking spaces are inefficient, especially in areas with scarce parking, as they do not adequately consider the probability of availability and relative position of parking spaces in relation to the destination and starting location, leading to suboptimal choices.
Innovation Solution
A method that optimizes the search for a parking space by using multiple parameters such as the probability of availability, relative position of the parking space to the destination, and journey time from the starting location, allowing for the selection of the most suitable option based on these criteria, which can be supported by driver assistance or navigation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional navigation systems are used to guide the vehicle to the destination, then the vehicle can reach the destination, but the driver cannot park the vehicle directly at the destination due to scarce parking spaces
Solution Approach 1:
The system performs preliminary actions by determining the probability of finding parking spaces at various locations before the vehicle arrives at the destination. It calculates optimization parameters including journey time, distance to destination, and availability probability in advance, allowing the driver to make informed decisions about where to park before reaching the final destination.
2Productivity
If the driver searches for parking spaces without optimization parameters, then the search process is simple, but the efficiency and reliability of finding a suitable parking space is low
Solution Approach 1:
The system applies parameter changes by introducing multiple optimization parameters (journey time, distance to destination, availability probability) to transform the parking search process from a simple random search into an optimized decision-making process. These parameters enable systematic evaluation of different parking options based on quantitative criteria.
Solution Approach 2:
The system introduces an intermediary optimization module that acts as a mediator between the driver and parking spaces. This intermediary calculates and presents optimized parking recommendations by processing multiple parameters (availability probability, journey time, distance) and providing a ranked list of parking options, thereby simplifying the driver's decision-making process.
3Length of stationary object
If only the distance to destination is considered for parking space selection, then the parking space is close to the destination, but the journey time from starting location and availability probability are not optimized
Solution Approach 1:
The system applies another dimension by expanding the evaluation criteria from a single dimension (distance to destination) to multiple dimensions including journey time, availability probability, and distance to destination. This multi-dimensional approach allows the system to identify parking spaces that optimize the overall parking experience rather than just minimizing distance.
Data Source
AI summary
A method optimizes a parking space search for a vehicle. During the method, at least one solution option is ascertained, and an optimization solution is ascertained from the at least one solution option, wherein the probability of the availability of at least one parking space is used as an optimization parameter, the position of the at least one parking space relative to a destination is used as an additional optimization parameter, expressed as the duration until the arrival at the destination from the at least one parking space, and the drive duration from a starting location to the at least one parking space is used as an additional optimization parameter.