ADAS Telematics Risk Scoring for Accident Probability Forecasting
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Solution Overview
Problem
Existing risk assessment systems for vehicles equipped with Advanced Driver-Assistance Systems (ADAS) fail to accurately measure and forecast accident probabilities due to inadequate consideration of ADAS features, leading to inefficient risk transfer and insurance pricing.
Innovation Solution
An electronic system that collects telematics data via mobile devices to measure the impact of ADAS features on accident risks, providing a real-time ADAS risk score for dynamic risk transfer and insurance calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional risk assessment systems are used for vehicles with ADAS features, then the system complexity remains low, but the measurement precision of accident probabilities deteriorates due to inadequate consideration of ADAS features
Solution Approach 1:
The system segments risk assessment into multiple components: ADAS feature detection module, telematics data collection module, environmental condition analysis module, and risk score calculation module. Each module handles specific aspects of risk assessment independently, improving measurement precision while managing system complexity through modular architecture.
Solution Approach 2:
The electronic system performs multiple functions including collecting telematics data, detecting ADAS features, analyzing environmental conditions, calculating risk scores, and providing real-time assessments. This multi-functionality consolidates what would otherwise require multiple separate systems, improving measurement precision without proportionally increasing complexity.
2Reliability
If real-time ADAS risk score assessment is implemented, then the risk transfer optimization improves, but the use of energy increases due to continuous data collection and processing
Solution Approach 1:
The system implements periodic risk assessment cycles where telematics data is collected, processed, and updated at defined intervals rather than continuously. The risk score is recalculated periodically based on accumulated data, maintaining reliable risk transfer optimization while reducing energy consumption compared to continuous real-time processing.
Solution Approach 2:
The system performs preliminary data collection and pre-processing of telematics information before full risk calculation is required. By preparing data in advance and maintaining buffered information about ADAS features and environmental conditions, the system reduces the computational energy needed during actual risk assessment events while maintaining reliability.
3Measurement precision
If comprehensive telematics data collection is performed to assess ADAS impact, then the measurement precision of risk factors improves, but the loss of time increases due to data aggregation and processing
Solution Approach 1:
The system collects and pre-processes telematics data continuously in the background before risk assessment is needed. ADAS feature detection, environmental condition monitoring, and telematics data aggregation are performed proactively, so when risk calculation is required, the data is already prepared and available, reducing processing time while maintaining comprehensive measurement precision.
Solution Approach 2:
The system introduces intermediate data processing layers that aggregate and filter telematics information before final risk calculation. Summary statistics and pre-computed features serve as intermediaries between raw data collection and risk score generation, reducing the time required for comprehensive analysis while maintaining measurement precision through structured data representation.
Data Source
AI summary
Proposed is an electronic risk measuring and scoring system, in particular for mobile telematics devices and method thereof. In particular, an electronic risk measuring and scoring system which measures an ADAS risk score measure measuring the impact of ADAS features to the accident risk associated with a motor vehicles, and which rates and calibrates a risk-transfer user-specifically thereby capturing the impact of ADAS features in measures of risk-transfer claims frequency and severity.