Vehicle Storage Compartment Monitoring With Pattern-Based Object Detection
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Solution Overview
Problem
Autonomous vehicles lack effective means to ensure that storage compartments are accurately monitored for contents before and after delivery or pickup, leading to potential unauthorized or missing items, which can result in inefficiencies and safety concerns.
Innovation Solution
Implementing a machine-learning camera system that creates an artificially patterned background within the storage compartment to detect changes and disturbances, allowing for real-time monitoring and confirmation of contents through image processing, with notifications and actions taken by human operators for any discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles are equipped with storage compartments for object delivery and pickup, then delivery capability is improved, but monitoring accuracy of compartment contents deteriorates
Solution Approach 1:
A camera system is introduced as an intermediary device to monitor the storage compartment contents. The camera captures images of the compartment interior, and these images are processed by a machine learning model to detect objects and verify their presence or removal, thereby achieving accurate monitoring without direct human observation
Solution Approach 2:
The patent replaces manual visual inspection of storage compartments with an automated optical system. Instead of humans physically checking compartments, a camera-based imaging system combined with machine learning algorithms automatically detects and tracks objects, substituting mechanical/human monitoring with an automated electronic system
2Difficulty of detecting and measuring
If manual monitoring of storage compartments is performed, then detection capability is improved, but labor requirements and time consumption increase
Solution Approach 1:
The system performs self-monitoring through automated camera imaging and machine learning-based object detection. The compartment contents are monitored automatically without requiring human operators to physically inspect each compartment, enabling the system to detect and track objects independently
Solution Approach 2:
The camera system continuously captures images of the storage compartment throughout the vehicle's operation. Rather than periodic manual checks, the monitoring occurs continuously or at key moments (before delivery, after pickup), ensuring uninterrupted surveillance of compartment contents and enabling real-time tracking of object status
3Measurement precision
If automated camera systems are implemented for monitoring, then monitoring accuracy is improved, but system complexity increases
Solution Approach 1:
The system creates visual copies (images) of the storage compartment contents and processes these copies to detect objects. Instead of directly manipulating or physically analyzing objects in the compartment, the machine learning model analyzes image data, which is a simplified representation that can be processed computationally without physical complexity
Data Source
AI summary
The present invention extends to methods, systems, and computer program products for changing vehicle configuration based on vehicle storage compartment contents. At an autonomous vehicle, a camera is mounted inside a storage compartment. The camera monitors the interior of the storage compartment. The camera can confirm that the storage compartment is empty when it is supposed to be empty and contains an object when it is supposed to contain an object. Any discrepancies can be reported to a human operator. The human operator can instruct the autonomous vehicle to change configuration to address discrepancies. In one aspect, a machine-learning camera memorizes a background pattern permeated to a surface of the storage compartment. The machine-learning camera detects objects in the storage compartment based on disturbances to the background pattern.