Driver Fault Influence Vector Characterization Using Motion Vectors
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Solution Overview
Problem
Existing vehicle performance characterization techniques rely on extensive telematics systems and sensors, increasing system cost and complexity, while failing to differentiate between driver fault and external events effectively.
Innovation Solution
A driver scoring system that uses motion vectors from processed images to generate an influence vector, differentiating between driver fault and external events by analyzing changes in motion vector intensity and direction, thereby reducing the need for raw image storage and simplifying sensor requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive telematics systems and sensors (radar, lidar, depth sensors) are used to determine unsafe CAS events, then measurement precision is improved, but device complexity and system cost increase
Solution Approach 1:
The patent extracts only the essential motion information (motion vectors) from video frames rather than processing complete raw images or using multiple specialized sensors. By taking out only the necessary motion data through optical flow analysis, the system achieves safe event detection without requiring complex sensor arrays like radar, lidar, and depth sensors simultaneously
Solution Approach 2:
The patent creates a simplified representation (motion vector field) that copies only the essential motion characteristics from the visual scene. Instead of using multiple physical sensors to capture different types of data, the system creates a computational copy of motion information from standard video frames, achieving similar detection capabilities with simpler hardware
2Measurement precision
If raw images are stored for further processing and intelligent system training, then measurement precision is improved, but loss of privacy increases due to storage of sensitive information
Solution Approach 1:
The patent extracts only motion vectors from video frames before any storage or training processes. By taking out only the essential motion information and discarding the original images, the system maintains measurement precision for driver behavior analysis while eliminating privacy risks associated with storing raw images containing sensitive information like license plates
Solution Approach 2:
The patent discards raw images after extracting motion vectors, keeping only the processed motion data for storage and training. This approach allows the system to recover and utilize the essential behavioral information while permanently discarding the privacy-sensitive original images, thus resolving the privacy concern
3Device complexity
If motion vectors are used instead of raw images, then device complexity is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent performs preliminary extraction of motion vectors from video frames before any analysis or storage operations. By pre-processing the video data to extract motion information upfront, the system simplifies subsequent processing steps while maintaining sufficient precision for driver behavior characterization, as motion vectors capture the essential dynamic information needed for safety assessment
Data Source
AI summary
An apparatus, including: an interface configured to receive raw images of one or more objects across a timeseries of frames corresponding to a movement event from a perspective of a vehicle of interest (Vol); and processing circuitry that is configured to: track a change in intensity or direction information represented in motion vectors (MVs) generated based on the raw images; generate, based on the change in the intensity or direction information, a weight of an influence vector representing a Vol influence on the movement event; and transmit the weight of the influence vector and an identity of the movement event to an assessment system that is configured to utilize the weight of the influence vector in an assessment of the Vol.