Vehicle Risk Visualization Using Personalized Driving Behavior Patterns
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Solution Overview
Problem
Existing methods for presenting driving risk information to drivers rely on statistical data that is not personalized and lacks visual aids, making it difficult for individuals to understand and address their specific driving risks.
Innovation Solution
A computer-implemented method and system that analyzes vehicle sensor data to predict personalized driving risks associated with specific vehicle components, presenting this information through a visual display that highlights risky areas of the vehicle, along with guidance on how to reduce these risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If statistical data is used to present driving risk information, then information can be provided to drivers, but the information is not personalized and is difficult to understand
Solution Approach 1:
The patent applies local quality by transitioning from generic statistical risk data to personalized risk information specific to each driver's behavior patterns and vehicle conditions. The system analyzes individual sensor data to generate customized risk assessments, making the information both personalized and comprehensible through visual representations of specific risky areas.
Solution Approach 2:
The patent transforms abstract statistical risk data into visual spatial representations on a display device. By mapping risk information to visual dimensions (heat maps, highlighted areas on vehicle diagrams), the system makes incomprehensible statistical data intuitively understandable through graphical visualization.
2Adaptability or versatility
If personalized risk analysis is implemented, then driving risk information can be customized for individuals, but system complexity increases
Solution Approach 1:
The patent segments the complex task of personalized risk analysis into distinct processing stages: sensor data collection, driving behavior analysis, risk factor identification, and visual presentation. This segmentation manages system complexity by breaking down the personalization process into modular, manageable components that can be processed sequentially.
Solution Approach 2:
The system implements self-service by automatically collecting sensor data, analyzing driving patterns, and generating personalized risk assessments without requiring manual intervention. The automated processing reduces operational complexity while maintaining high adaptability to individual driver behaviors.
3Ease of operation
If visual aids are added to risk information, then comprehension improves, but information processing requirements increase
Solution Approach 1:
The patent creates simplified visual copies or representations of risk data through graphical interfaces and heat maps. Instead of processing and displaying all raw sensor data, the system generates condensed visual summaries that convey risk information efficiently, reducing computational requirements while enhancing comprehension.
Data Source
AI summary
A computer-implemented method can include analyzing sensor data from a vehicle to determine one or more driving behaviors of a driver, determining one or more patterns in the one or more driving behaviors over a predetermined period, and determining probabilities of damaging portions of the vehicle based on the one or more patterns.