Vehicle Safety Event Detection With ML Feedback Tuning
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Solution Overview
Problem
Current systems for real-time safety event detection in vehicles face challenges due to high data storage and processing requirements, leading to delayed alerts and reduced accuracy, as they rely on wireless data communications with varying bandwidth and speed, limiting the effectiveness of safety alerts provided to drivers.
Innovation Solution
A vehicle device equipped with a machine learning model feedback loop that compares probabilities from a local machine learning model and a backend server to adjust event detection, allowing for real-time adjustments without retraining, enhancing efficiency and accuracy by dynamically tuning detection parameters and reducing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is transmitted to a server for event detection, then detection accuracy can be improved, but wireless data communication delays reduce real-time effectiveness
Solution Approach 1:
The system segments event detection into two parts: a lightweight local model for real-time preliminary detection and a comprehensive server model for accurate final determination. This segmentation allows the system to benefit from both low-latency local processing and high-accuracy server processing without the delays of complete server dependency.
Solution Approach 2:
The local machine learning model performs preliminary event detection and filtering before data is sent to the server. This preliminary action reduces the amount of data requiring server processing and enables immediate local responses while more accurate server analysis is performed in parallel.
2Speed
If a comprehensive machine learning model is deployed locally for real-time detection, then response speed improves, but device complexity and processing requirements increase
Solution Approach 1:
The machine learning model is segmented into a simplified local version for real-time inference and a comprehensive server version for training and accurate detection. The local model contains only essential detection logic, reducing device complexity while maintaining real-time response capability.
Solution Approach 2:
The system changes model parameters by deploying a simplified version with reduced complexity locally while maintaining the ability to update parameters remotely. This allows real-time detection with appropriate processing requirements while avoiding the need for full-complexity models on the device.
3Measurement precision
If the machine learning model is retrained to improve accuracy, then detection precision increases, but retraining time and downtime increase
Solution Approach 1:
Model retraining is performed preliminarily on the server before deployment to the local device. This preliminary action allows comprehensive retraining to occur in advance, so that when the updated model is deployed locally, no retraining downtime is experienced by the vehicle device.
Solution Approach 2:
The trained model is copied from the server to the local device rather than transferring training data or performing retraining locally. This copying approach allows the local device to immediately use the pre-trained model without experiencing retraining downtime.
Data Source
AI summary
A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.