Vehicle Loading Object Detection for Forgotten Item Alerts
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Solution Overview
Problem
There is a risk of forgetting objects when loading a vehicle, especially in conditions with poor visibility or high stress, as existing systems do not effectively alert users to items left behind in the vicinity of the vehicle.
Innovation Solution
A method and system using sensors and image recognition to detect objects before and after vehicle loading, issuing an indication if objects are added or missing, and excluding irrelevant items like animals or large objects to reduce false alarms, thereby guiding the user to inspect and load forgotten items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object detection is performed to identify forgotten items, then the reliability of vehicle loading is improved, but the device complexity increases due to sensors and processing systems
Solution Approach 1:
The sensor system is designed to serve multiple functions: detecting objects during vehicle loading, tracking object locations, identifying forgotten items, and providing alerts to users. By making the detection system universal and multi-functional, the patent reduces the need for separate specialized systems, thereby improving reliability while controlling device complexity.
Solution Approach 2:
The system automatically detects objects, compares initial and final states, identifies forgotten items, and issues alerts without requiring manual inspection or external intervention. This self-service capability improves loading reliability while minimizing the complexity of user interaction with the system.
2Loss of information
If comprehensive object detection is implemented to reduce forgotten items, then the loss of information is reduced, but the measurement precision requirements increase
Solution Approach 1:
The system performs detection at multiple stages (initial vehicle state and final vehicle state) and uses comparative analysis rather than requiring perfect single-instance detection. This partial action approach reduces the precision burden on any single detection event while still effectively identifying forgotten objects and reducing information loss.
Solution Approach 2:
The system provides feedback to users through alerts when objects are detected as forgotten, and the comparative detection method uses feedback from the initial state measurement to evaluate the final state. This feedback mechanism reduces information loss by ensuring users are informed of detection results without requiring extremely high single-instance measurement precision.
Data Source
AI summary
A method includes—responsive to activation of a vehicle for loading objects —detecting a first set of objects in vicinity of the vehicle, and—responsive to subsequent activation of the vehicle for relocation —detecting a second set of objects in vicinity of the vehicle. The method also include—responsive to a difference between the first and second sets of objects—issuing an indication for a user of the vehicle. The difference may, for example, relate to an added object, the added object being present in the second set of objects but not in the first set of objects. In some embodiments, objects may be excluded from consideration when a tracked location of the object indicates an increasing distance between the vehicle and the object. In some embodiments, objects that have an estimated size larger than a reference size may be excluded from consideration.