Vehicle Interior Occupancy AI for Left-Behind Item Detection
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Solution Overview
Problem
Existing systems fail to effectively detect and alert occupants when items are left behind in vehicles, leading to potential loss or theft.
Innovation Solution
Implementing a trained artificial intelligence (AI) model within vehicles to predict when items will be left behind based on sensor data, perform checks when the area is unoccupied, and send alerts to occupants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing detection systems are used, then the system structure is simple, but the detection precision and reliability are insufficient to effectively detect left items
Solution Approach 1:
The system divides the vehicle interior into multiple monitoring zones (seats, floor areas, compartments) and uses distributed sensors to detect occupancy status in each zone. This segmentation enables precise tracking of item locations without requiring a single complex detection system.
Solution Approach 2:
The system performs preliminary detection of occupancy status continuously before the item is actually left behind. By monitoring zone occupancy in advance and tracking changes over time, the system can predict when items will be left and prepare for detection, improving overall detection precision.
2Reliability
If continuous monitoring is performed to improve detection reliability, then the reliability improves, but the energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system performs periodic occupancy checks at intervals. The control unit activates sensors and processing only when needed (when occupancy changes are detected or at scheduled intervals), maintaining detection reliability while significantly reducing energy consumption compared to continuous operation.
Solution Approach 2:
The system uses the vehicle's existing occupancy detection infrastructure and leverages the natural occupancy changes that occur during normal vehicle operation. By triggering detection sequences based on occupancy transitions rather than continuous operation, the system maintains reliability while minimizing energy use.
3Measurement precision
If AI model execution is performed continuously to improve prediction accuracy, then the prediction precision improves, but the processing time and computational resources increase
Solution Approach 1:
The system executes the AI prediction model in advance of actual item abandonment, using current occupancy data to predict future states. This preliminary execution allows the system to prepare detection sequences and alert strategies before items are actually left, improving prediction precision without requiring continuous real-time processing.
Solution Approach 2:
The AI model is executed periodically based on occupancy changes rather than continuously. The control unit triggers prediction executions when occupancy status changes or at scheduled intervals, maintaining accurate predictions while reducing computational load and processing time compared to continuous model execution.
Data Source
AI summary
An example operation includes one or more of receiving a notification that one or more of a plurality of locations of an interior of a vehicle is occupied, executing a trained artificial intelligence (AI) model to predict when at least one item will be left in the interior of the vehicle based on the notification, performing at least one check related to the at least one item based on the prediction, receiving a notification that one or more of the plurality of locations of the interior of the vehicle is unoccupied, executing the at least one check to determine the at least one item has been left in the interior of the vehicle, and, in response to the at least one item having been left in the interior of the vehicle, sending an alert.