Parking Space Prediction Using Occupant Data Analysis
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Solution Overview
Problem
In densely populated areas, parking lots often exceed capacity, leading to long wait times as drivers cannot predict when parked vehicles will leave their desired spaces, causing inefficiencies in parking.
Innovation Solution
A system and method that utilize cameras to analyze occupant and vehicle information, predict departure times of parked vehicles, and provide this information to drivers via a terminal, allowing them to preempt available spaces by calculating expected departure times and sequences using deep learning algorithms and weighting values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If parking spaces are allocated on a first-come-first-served basis without prediction, then parking lot capacity is maximized, but driver wait time increases significantly
Solution Approach 1:
The system performs preliminary analysis of occupant information and vehicle information before the driver arrives at the parking lot. By predicting departure times in advance using deep learning algorithms, the system provides drivers with information about which parking spaces will become available soon, allowing them to plan their parking strategy ahead of time rather than waiting idle in the parking lot.
Solution Approach 2:
The server acts as an intermediary between parked vehicles and arriving drivers. It collects data from cameras, analyzes occupant and vehicle information, predicts departure times, and transmits this processed information to terminal devices. This intermediary system bridges the information gap without requiring direct communication between drivers and parked vehicle occupants.
2Measurement precision
If real-time monitoring of all parked vehicles is implemented, then parking space availability information becomes accurate, but system cost and complexity increase
Solution Approach 1:
The system extracts only the necessary information from camera footage - specifically occupant information (number of people, gender, age, relationship) and vehicle information (vehicle number, model, door/tailgate opening次数). By focusing on extracting only these relevant features rather than analyzing all visual data, the system achieves accurate predictions while keeping processing requirements manageable.
Solution Approach 2:
The deep learning model is trained offline using historical data, allowing it to perform predictions autonomously without requiring manual intervention or complex real-time analysis infrastructure. Once trained, the model self-services by automatically analyzing new data and generating predictions, reducing the need for continuous human oversight or complex system maintenance.
3Productivity
If drivers wait for desired parking spaces to become available, then parking space utilization is optimized, but overall parking lot throughput decreases
Solution Approach 1:
Drivers perform preliminary planning before entering the parking lot by checking the terminal device for predicted availability information. This allows them to make informed decisions about which parking spaces to target and when to arrive, preventing idle waiting and optimizing their parking journey timing to coincide with predicted space availability.
Solution Approach 2:
The system provides feedback to drivers about predicted departure times and parking space availability through terminal devices. This feedback loop allows drivers to adjust their arrival times and parking strategies based on real-time predictions, optimizing both individual parking efficiency and overall parking lot throughput by reducing unnecessary waiting and vehicle circulation.
Data Source
AI summary
The present disclosure relates to a system and method for providing information regarding a parking space. The system includes a terminal located in a vehicle desired to be parked and a server that obtains occupant information and vehicle information of vehicles parked in parking spaces through cameras installed in a parking lot, predicts departure time of the parked vehicles based on the occupant information and the vehicle information, and provides parking space information based on the predicted departure time to the terminal.