Machine Vision Driver Risk Detection System
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Solution Overview
Problem
Current vehicle event recorders lack real-time detection capabilities for certain anomalous events, such as driver behavior risks, which are not effectively identified using traditional sensors alone.
Innovation Solution
A system utilizing machine vision and automated computer algorithms to analyze video data streams from interior and exterior cameras to determine driver behavior risk types, including gaze direction, head orientation, body position, hand position, lane drift, and other factors, and transmit metadata for risk assessment and response actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensors are used for event recording, then device complexity is reduced, but detection capability for driver behavior risks is insufficient
Solution Approach 1:
The patent replaces traditional mechanical sensors with machine vision systems including interior cameras, exterior cameras, and computer algorithms to detect driver behavior risks. This substitution enables detection of gaze direction, head orientation, body position, and other behavioral parameters that traditional sensors cannot capture, thereby improving reliability while accepting increased system complexity.
2Productivity
If machine vision systems are implemented, then real-time detection capability is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously capturing video frames and pre-processing images to identify key features such as driver gaze direction, head orientation, and body position. This preliminary processing enables real-time detection by preparing data in advance, reducing the computational burden during critical analysis phases and minimizing processing delays.
3Measurement precision
If multiple video data streams are processed, then detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex task of driver behavior analysis into distinct components: interior camera processing for driver姿态 and gaze detection, exterior camera processing for lane drift and surrounding environment monitoring, and separate algorithm modules for each detection parameter. This segmentation improves detection accuracy by dedicating specialized processing to each parameter while managing complexity through modular architecture.
Data Source
AI summary
A system for driver risk determination using machine vision includes an interface and one or more processors. The interface is to receive one or more video data streams. The one or more processors is/are to determine, whether a driving behavior risk type appears in one of the one or more video data streams; and in the event that the driving behavior risk type appears in the one of the one or more video data streams, indicate the driver behavior risk type.