Vehicle Event Assessment Using Sensor Data and Prior Event Models
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Solution Overview
Problem
Existing methods for processing vehicle events, such as assessing damage and occupant injuries, are not optimized in terms of time efficiency and may not provide comprehensive assessments.
Innovation Solution
A method and system that utilize vehicle sensors to collect data before, during, and after an event, combined with a database of prior events generated via machine learning, to determine the assessment of the event, including fault, damage, and injury, and take appropriate actions, such as notifying insurance providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual assessment methods are used for vehicle events, then comprehensive human judgment can be applied, but the processing time is excessive and efficiency is low
Solution Approach 1:
The patent replaces manual mechanical assessment processes with automated electronic systems. Sensors capture vehicle event data (acceleration, impact forces, deployment status) and processors automatically analyze this data to determine fault, severity, and appropriate responses, eliminating the need for manual inspection and significantly reducing processing time while maintaining comprehensive assessment capabilities
Solution Approach 2:
The vehicle's own sensor systems and onboard processors perform the assessment function autonomously. The system self-evaluates the vehicle event by analyzing data from multiple sensors, determines fault conditions, assesses severity levels, and can automatically initiate appropriate responses without requiring external human intervention for the initial assessment phase
2Loss of information
If basic sensor data is collected, then processing is faster, but the assessment comprehensiveness is insufficient
Solution Approach 1:
The patent combines multiple sensor types (accelerometers, impact sensors, deployment sensors) and integrates their data streams into a unified assessment process. The processor merges information from various sources to create a comprehensive view of the vehicle event, enabling complete fault and severity assessment while managing system complexity through integrated processing architecture
Solution Approach 2:
The sensor system and processing unit are designed to perform multiple functions: detecting vehicle events, analyzing fault conditions, determining severity levels, and initiating appropriate responses. This multi-functional approach ensures comprehensive assessment capabilities are achieved without requiring separate dedicated systems for each function, thereby managing complexity
3Measurement precision
If detailed analysis of all sensor data is performed, then assessment accuracy is improved, but processing time increases
Solution Approach 1:
The system continuously monitors and pre-processes sensor data before events occur, maintaining ready-state information about vehicle conditions. When an event happens, the processor can immediately access pre-organized data and focus analysis on critical parameters, achieving high accuracy without the delay of collecting and organizing data after the event occurs
Solution Approach 2:
The assessment process is divided into distinct analytical stages: fault determination, severity assessment, and response initiation. Each stage processes specific relevant data segments rather than analyzing all sensor data comprehensively at once. This segmented approach maintains high accuracy for each assessment component while reducing overall processing time through parallel and sequential optimization
Data Source
AI summary
In various embodiments, methods and systems are provided for processing data pertaining to a vehicle event for a vehicle. In accordance with an exemplary embodiment, vehicle sensor data is obtained from one or more vehicle sensors pertaining to the vehicle event. Also in an exemplary embodiment, an assessment of the vehicle event is determined via a processor, including as to a fault or a severity, or both associated with the vehicle event, based on the vehicle sensor data.