In-Vehicle Lost Item Detection Using Multi-Distance Sensors
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Solution Overview
Problem
Existing methods for managing lost articles in vehicles are limited, as passengers typically realize they have lost an item after exiting the vehicle, making it difficult to efficiently handle and recover the lost article.
Innovation Solution
A lost article management system that uses multiple cameras and sensors installed at varying distances from the passenger's seated position to detect passengers and articles, generate detection information, and employ a lost status prediction model to determine if an article has been lost, prompting a confirmation request to the passenger.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras and sensors are installed at varying distances to detect passengers and articles, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides the detection task into multiple segments by installing cameras and sensors at different distances (first camera at first distance, second camera at second distance). Each detection device captures different aspects of the passenger and article, and the results are integrated to achieve comprehensive and accurate detection.
Solution Approach 2:
The detection system uses a nested structure where multiple detection devices are positioned at different distances from the seated point, creating concentric detection zones. The first camera/sensor operates at a closer distance while the second camera/sensor operates at a farther distance, with their detection results nested and combined for comprehensive analysis.
2Measurement precision
If a lost status prediction model is used to determine whether an article has been lost, then lost article recognition accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The lost status prediction model is trained in advance with comprehensive data about passenger behavior, article placement patterns, and collection status. This preliminary training enables the model to quickly infer lost article status during actual operation without requiring complex real-time calculations, thus reducing processing time while maintaining high accuracy.
Solution Approach 2:
The system continuously monitors detection information from multiple cameras and sensors, feeds this data into the prediction model, and receives feedback in the form of lost status determination. This feedback mechanism allows the system to adaptively update its understanding of article status based on changing conditions, improving recognition accuracy over time.
Data Source
AI summary
A lost article management method in a vehicle includes: generating first detection information by detecting a passenger or an article by using a first camera or first sensor installed in the vehicle and separated by a first distance from a seated point of the passenger; generating second detection information by detecting the passenger or the article by using a second camera or second sensor installed in the vehicle and separated by a second distance from the seated point; generating third detection information by detecting a background excluding the passenger and the article; providing a lost status prediction model; determining whether an article has been lost from the first detection information to the third detection information; and displaying a lost article confirmation request on a plurality of display devices installed in the vehicle when it is determined that the article has been lost.