Autonomous Vehicle Fleet Search for Missing Objects
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Solution Overview
Problem
Conventional methods for finding missing pets are inefficient, relying on manual searches and database matching by third parties, which can be time-consuming and limited in scope.
Innovation Solution
A system utilizing a fleet of autonomous vehicles that passively search for a specific object by transmitting user-provided characteristics to the vehicles, which filter and transmit images for user review, allowing for a broader and more efficient search area coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual searches and database matching by third parties are used to find missing pets, then the search process can be initiated quickly, but the search efficiency and coverage are limited and time-consuming
Solution Approach 1:
Autonomous vehicles perform multiple functions: they continue their regular transit operations while simultaneously conducting object searches. The vehicles are not dedicated solely to search tasks but serve dual purposes, increasing search capacity without requiring vehicles to idle or deviate from their routes
Solution Approach 2:
The system pre-registers objects in a database using unique traits (e.g., pore and crease patterns on dog noses) before searches are initiated. This preliminary data collection enables rapid identification when objects are found, eliminating the need for on-site verification during active searches
2Area of stationary object
If autonomous vehicles are dispatched specifically to search for objects, then search coverage can be increased, but vehicle availability for other tasks decreases and operational costs increase
Solution Approach 1:
The autonomous vehicles perform dual functions by continuing their scheduled transit operations while simultaneously conducting object searches along their routes. This eliminates the need to divert vehicles from their primary tasks, maintaining high utilization efficiency while expanding search coverage to areas traversed by the vehicle fleet
Solution Approach 2:
The system dynamically assigns search tasks to vehicles based on their existing routes and locations. Vehicles receive search requests and characteristics from the computing system, and autonomously determine which objects to search for based on their current trajectory, allowing flexible adaptation without disrupting overall fleet operations
3Reliability
If all images captured by autonomous vehicles are transmitted to the computing system for processing, then comprehensive search results can be achieved, but data transmission time and system load increase
Solution Approach 1:
The autonomous vehicles extract and transmit only the most relevant image data to the computing system. The vehicle's onboard system identifies images containing potential matches based on search characteristics and filters out irrelevant images before transmission, sending only candidate images for further processing
Solution Approach 2:
The system performs preliminary filtering and processing of images on the autonomous vehicles before transmission to the computing system. This preliminary action reduces the volume of data requiring centralized processing, decreasing transmission time and system load while maintaining search accuracy
Data Source
AI summary
A computing system that can receive an object search request from a user indicating a request to search for a specific object in an area traversed by one or more autonomous vehicle. The object search request can include a set of physical characteristics of the specific object. The computing system can then transmit a signal to an autonomous vehicle indicating a request for the autonomous vehicle to search for the specific object. The signal can cause the autonomous vehicle to transmit an image, selected based on a physical characteristic of the object, to the computing system. The computing system can then generate a score indicative of a difference between one or more physical characteristic of the object in the image and the specific object. The computing system can then selectively transmit the image to a mobile device operated by the user based on the score.