Parking Departure Inference via Contextual Sensor Analysis
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Solution Overview
Problem
Locating available parking spaces in congested urban areas is time-consuming and inefficient, as existing systems only provide information on currently open spots, offering little utility in heavily congested areas where few spots are available and are quickly taken.
Innovation Solution
A system that infers when a parked vehicle is likely to depart by analyzing contextual indicators such as objects and actions near the vehicle using sensor data and prediction models, communicating this information to other vehicles and a cloud-based system to update availability information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a driver searches for available parking spots in congested areas, then the driver can find parking spaces, but the search process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary analysis of contextual indicators (objects near vehicles, vehicle states, environmental factors) to predict future parking availability before spots are actually taken. This allows drivers to receive advance notification of upcoming available spots, eliminating the need for time-consuming searches in congested areas.
Solution Approach 2:
The system introduces an intermediary prediction layer that analyzes contextual data and translates it into actionable parking availability information. This intermediary processing converts complex sensor data about vehicles and surroundings into simplified predictions about future spot availability, making the information useful for drivers.
2Reliability
If existing parking systems provide information on currently open spots, then drivers receive real-time availability data, but this information is of limited utility in heavily congested areas where few spots are available and are quickly taken
Solution Approach 1:
Instead of only reporting current spot status, the system performs preliminary predictions about future availability by analyzing contextual indicators. This allows the system to provide reliable information about upcoming available spots even in congested areas, enabling better parking space utilization by directing drivers to spots that will be available soon.
Solution Approach 2:
The system changes the parameter being monitored from static spot occupancy to dynamic contextual indicators (vehicle states, nearby objects, environmental factors). This parameter transformation enables predictions about future availability, making the information more reliable and useful in congested areas where traditional real-time data is insufficient.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to improving the identification of available parking spaces by inferring when a vehicle is likely to depart. In one embodiment, a method includes, responsive to acquiring sensor data about a surrounding environment including at least one parked vehicle, extracting a feature from the sensor data about a context of the parked vehicle. The method includes analyzing the feature to determine a status of the vehicle in relation to whether the vehicle is remaining parked. The method includes providing the status to inform additional vehicles about an availability of a parking spot of the parked vehicle.