Driving Event Analysis Using Video and Proximity Context
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Solution Overview
Problem
Existing telematics systems fail to accurately analyze driving events and adjust driver scores based on comprehensive data analysis, including image, video, and object proximity data, leading to potential misclassification of driving behaviors.
Innovation Solution
A framework that utilizes vehicle sensors, cameras, and external data sources to collect and analyze driving data, identifying high-risk events and adjusting driver scores by considering both internal vehicle conditions and external factors, using image, video, and object proximity data to determine the cause of driving events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If telematics systems rely solely on vehicle operational data for driving event analysis, then the system complexity is reduced, but the measurement precision of driver behavior assessment deteriorates
Solution Approach 1:
The patent combines multiple data sources including vehicle operational data, image data from cameras, video data, and object proximity data from sensors into a unified driving event analysis system. This integration allows the system to comprehensively assess driving events by cross-referencing information from different sources, thereby improving measurement precision without excessive complexity increase.
Solution Approach 2:
The telematics system is designed to perform multiple functions: it processes vehicle operational data for basic monitoring, analyzes image data for visual context, processes video data for temporal patterns, and integrates object proximity data for spatial awareness. This multi-functional approach enables accurate driver behavior assessment through diverse data types.
2Measurement precision
If multiple data sources including image, video, and object proximity data are integrated for driving event analysis, then the measurement precision of driving event causes is improved, but the device complexity increases
Solution Approach 1:
The patent segments the driving event analysis into distinct modules: one for processing vehicle operational data, another for image data analysis, a third for video data processing, and a fourth for object proximity data integration. Each module handles specific data types independently, then results are synthesized to identify driving event causes, reducing overall system complexity while maintaining high measurement precision.
3Reliability
If comprehensive data analysis is performed to determine driver responsibility in driving events, then the reliability of driver scoring is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary analysis of incoming data streams by pre-processing image data, video data, and sensor data to identify key features and potential driving events before full integration. This preliminary action reduces the computational burden during final driver responsibility determination, maintaining reliable driver scoring while reducing data processing time.
Data Source
AI summary
A driving analysis server may be configured to receive vehicle operation data from vehicle sensors, and may use the data to identify a potentially high-risk or unsafe driving event by the vehicle. The driving analysis server also may receive corresponding image data, video, or object proximity data from the vehicle or one or more other data sources, and may use the image, video, or proximity data to analyze the potentially high-risk or unsafe driving event. A driver score for the vehicle or driver may be calculated or adjusted based on the analysis of the data and the determination of one or more causes of the driving event.