Driver Image Capture System Using Sensor-Triggered Sampling
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Solution Overview
Problem
Existing vehicle event recorders face challenges in accurately associating sensor data with the driver, particularly in capturing and processing driver images for identification, as continuous recording is resource-intensive and requires the driver to be looking in the direction of the camera for successful identification.
Innovation Solution
A system that uses sensor data, such as accelerometer, lane marker, and turn signal data, to determine when the driver is likely looking in the direction of an inward-facing camera, adjusting image capture frequency and area scanning to increase the chances of capturing the driver's face, and employing audio data for identification when image data is insufficient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous driver image recording is implemented, then driver identification accuracy is improved, but resource consumption and system load increase significantly
Solution Approach 1:
The system implements periodic image capture triggered by specific events (accidents, harsh maneuvers, location-based events) rather than continuous recording. The processor determines whether to capture driver images based on sensor data indicating anomalous events, creating a periodic sampling approach that reduces resource consumption while maintaining identification accuracy when needed.
Solution Approach 2:
The system performs preliminary analysis of sensor data (accelerometer, gyroscope, vehicle state sensors) to predict when driver images are likely to be captured successfully. By anticipating events before they occur and pre-positioning capture triggers, the system optimizes resource allocation to capture images only when driver presence and proper orientation are likely.
2Reliability
If image capture frequency is increased, then probability of capturing driver face improves, but processing load and storage requirements increase
Solution Approach 1:
The system applies partial action by capturing images at selective moments rather than continuously. It uses sensor data to determine partial capture scenarios - capturing images only when anomalous events are detected or when driver presence is confirmed, avoiding excessive capture during normal driving conditions. This reduces processing load while maintaining sufficient reliability for identification purposes.
3Measurement precision
If the system waits for driver to look at camera, then identification accuracy improves, but time to capture image increases
Solution Approach 1:
The system performs preliminary detection of driver presence and orientation using sensor data before the actual image capture. By analyzing accelerometer, gyroscope, and vehicle state data in advance, the system predicts when the driver is likely to be in a position suitable for identification, triggering capture at the optimal moment without excessive delay.
Solution Approach 2:
The system uses feedback from multiple sensors (accelerometer, gyroscope, vehicle state sensors) to continuously monitor driver position and orientation. This real-time feedback allows the processor to determine the optimal capture moment when driver face is likely visible to the camera, balancing identification accuracy with minimal capture delay.
Data Source
AI summary
A system for capturing an image of a driver includes an input interface and a processor. The input interface is to receive sensor data associated with a vehicle. The processor is to determine whether to capture a driver image based at least in part on the sensor data and, in the event it is determined to capture the driver image, to indicate to capture the driver image.