Unoccupied Vehicle Event Detection With Adaptive Low-Power Sensing
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Solution Overview
Problem
Existing vehicle monitoring systems face challenges in efficiently detecting incidents in unoccupied vehicles while optimizing energy consumption, dealing with variable environmental noise and adversarial behavior, and reducing false positives.
Innovation Solution
A method and system using low-power sensors to detect vehicle events through a preliminary detection layer with high recall and low precision, followed by a middle adjudication layer to verify genuine events, reducing overall power consumption by minimizing continuous camera recording.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous video/image recording is used to detect incidents in unoccupied vehicles, then detection accuracy is improved, but energy consumption increases significantly
Solution Approach 1:
The monitoring system is segmented into multiple layers: a low-power sensor layer (accelerometers, microphones) that continuously monitors for anomalies, and a high-power camera layer that activates only when anomalies are detected. This segmentation allows the system to maintain detection accuracy while dramatically reducing energy consumption during normal operation.
Solution Approach 2:
Instead of continuous recording, the system uses periodic sampling with low-power sensors and activates continuous camera recording only periodically when triggered by anomaly detection. This periodic action pattern reduces energy consumption while maintaining the ability to detect and record actual incidents.
2Use of energy by moving object
If low-power sensors are used to reduce energy consumption, then detection of adversarial behavior becomes more difficult, but energy consumption is reduced
Solution Approach 1:
Low-power sensors serve as intermediaries that detect adversarial behaviors (rocking, knocking, glass breaking) and trigger the camera system. These sensors act as a first line of defense, filtering out normal environmental noise while capturing suspicious patterns that indicate potential theft or vandalism attempts.
Solution Approach 2:
The low-power sensors perform preliminary detection of potential threats before activating the full camera system. By detecting anomalies such as unusual vibrations, sounds, or movement patterns in advance, the system can prepare for potential adversarial behavior while consuming minimal energy during the monitoring phase.
3Measurement precision
If adaptive trigger thresholds are used to reduce false positives, then detection precision is improved, but system complexity increases
Solution Approach 1:
The system uses feedback mechanisms where detection results and environmental context are fed back into the adaptive trigger threshold algorithm. The algorithm continuously learns from detected events and environmental conditions, adjusting thresholds to distinguish between normal noise (birds, wind, passing vehicles) and genuine threats, thereby reducing false positives while managing complexity through iterative optimization.
Data Source
AI summary
Examples described herein can involve operating, by an electronic device, at least one sensor coupled to an unoccupied vehicle to collect sensor data. A vehicle event can be detected based on the sensor data meeting or exceeding an adaptive trigger threshold that is determined based on one or more environmental features associated with an environment of the unoccupied vehicle. Based on detecting the vehicle event, the electronic device can cause a first recording device to record the environment associated with the unoccupied vehicle for a first period of time to generate a first recorded data. The at least one sensor is different from the first recording device. A vehicle event analysis can be performed, by the electronic device, on the first recorded data to determine whether the vehicle event is an incident event.