Parking Recommendation System Using Multi-Source Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Identifying parking locations near a user's destination can be difficult, especially in large cities where parking availability is limited, and existing systems lack efficient methods to generate and transmit parking recommendations to vehicles.
Innovation Solution
A system that collects data from various sensors on vehicles and infrastructure, combines it with historical data, and uses this information to generate parking recommendations, taking into account user preferences and transmitting these recommendations to computing devices associated with the vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parking recommendations are generated using sensor data from multiple vehicles and infrastructure, then parking availability identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex parking recommendation task into multiple independent components: sensor data collection from vehicles, sensor data collection from infrastructure, historical data retrieval, real-time availability analysis, and recommendation generation. Each component operates independently and contributes to the final recommendation, improving accuracy while managing system complexity through modular design.
Solution Approach 2:
The system creates a universal parking recommendation platform that serves multiple vehicle types (autonomous and non-autonomous) and integrates multiple data sources (vehicle sensors, infrastructure sensors, historical databases). This multi-functional system handles diverse parking scenarios through a unified architecture, improving overall system efficiency and data utilization.
2Reliability
If real-time sensor data from multiple sources is processed, then parking recommendation reliability is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing historical parking data, vehicle sensor data, and infrastructure sensor data in databases before real-time recommendation generation. This pre-processing allows the system to quickly retrieve and analyze relevant information during actual parking recommendation requests, improving reliability without significantly increasing real-time processing time.
Solution Approach 2:
The system implements feedback mechanisms where parking recommendation outcomes and actual parking availability status are continuously monitored and fed back into the database. This feedback loop allows the system to learn from past performance, refine its algorithms, and improve recommendation reliability over time while optimizing processing efficiency based on accumulated experience.
3Productivity
If parking recommendations are transmitted to multiple vehicles simultaneously, then parking management efficiency is improved, but communication overhead increases
Solution Approach 1:
The system merges communication resources by using a centralized server infrastructure that consolidates recommendation generation and transmission functions. Multiple vehicles receive parking recommendations through this unified communication channel, reducing redundant communication overhead while maintaining efficient parking management across the entire vehicle fleet.
Data Source
AI summary
Systems and apparatuses for receiving data from a plurality of sensors and using the data, as well as other data, to generate a parking recommendation for a first vehicle are provided. Data may be received from sensors associated with a first vehicle for which a parking recommendation may be generated. Data may also be received from sensors associated with other vehicles and/or from one or more structures or other non-vehicle devices. In some examples, historical parking data may be extracted from a database. The collected and extracted data may be used to generate a parking recommendation for the first vehicle. In some examples, pre-stored user preferences, may be used in generating the parking recommendation as well. The parking recommendation may then be transmitted to a computing device within the first vehicle and may be displayed on the computing device.