Vehicle Sensor Data Processing for Risk Assessment
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Solution Overview
Problem
Insurance providers face challenges in accurately determining driving behaviors and identifying responsible parties in accidents, which affects premium calculations and marketing efforts due to limited access to real-time vehicle data and identification of non-policy holders driving within specific regions.
Innovation Solution
A system that collects and processes data from onboard vehicle sensors, including external sensors and operational parameters, to generate virtual models of events and identify vehicle characteristics, enabling insurers to assess risk and determine fault, and also uses this data for targeted advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If insurance providers rely on traditional business models with limited data access, then operational simplicity is maintained, but information accuracy and reliability deteriorate
Solution Approach 1:
The system segments the data collection process by using multiple independent sensors (cameras, LIDAR, radar, GPS) to capture different aspects of driving behavior and vehicle environment. Each sensor independently collects specific data types, which are then integrated to form a comprehensive view, thereby improving information accuracy without requiring a single complex data collection mechanism.
Solution Approach 2:
The patent introduces an intermediary processing system that receives raw sensor data, synchronizes timestamps, filters relevant information, and prepares structured data for analysis. This intermediary layer manages the complexity of multi-sensor integration, allowing the system to handle complex data streams while maintaining operational simplicity at the insurance provider level.
2Reliability
If real-time sensor data collection is implemented, then risk assessment accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing by synchronizing timestamps from multiple sensors in real-time and pre-filtering sensor data for relevance during the data collection phase. This preliminary organization of data reduces the computational burden during later analysis stages, enabling accurate risk assessment without excessive processing delays.
Solution Approach 2:
The patent implements event-triggered data collection where sensors continuously monitor but only transmit data when specific events occur (e.g., sudden braking, collision detection, rapid acceleration). This approach skips routine data transmission and focuses processing resources on significant events, thereby improving risk assessment accuracy for critical incidents while reducing overall data processing time and bandwidth usage.
3Measurement precision
If comprehensive sensor data is collected from multiple sources, then measurement precision is improved, but device complexity and data management difficulty increase
Solution Approach 1:
The system employs a universal data collection architecture where a single centralized platform receives and processes data from multiple sensor types (cameras, LIDAR, radar, GPS, accelerometers). This universal interface standardizes data ingestion across different sensor technologies, improving driving behavior detection accuracy while managing sensor integration complexity through a unified processing framework.
Solution Approach 2:
The patent creates synchronized virtual copies of sensor data with standardized formats and unified timestamp references. Each sensor type generates data that is copied and normalized to a common structure, allowing diverse sensor inputs to be processed uniformly. This copying approach maintains high measurement precision for detecting driving behaviors while simplifying the complexity of integrating multiple sensor types through standardized data representations.
Data Source
AI summary
A method, implemented in an electronic processing system that includes a memory and one or more processors, includes receiving, at the electronic processing system, sensor data representing information collected by a sensor (i) located on or in a first vehicle and (ii) configured to sense an environment external to the first vehicle, and storing the received sensor data in the memory. The method also includes processing the stored sensor data to determine conditions in which the second vehicle was driven, driving habits of a driver of the second vehicle, and an identifying characteristic of the second vehicle. The method further includes identifying the driver of the second vehicle using the identifying characteristic, and determining a risk level for the driver of the second vehicle using the determined conditions and driving habits.