Autonomous Vehicle Risk Benchmarking for Coverage Pricing
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Solution Overview
Problem
Current risk assessment systems for autonomous vehicles lack the ability to accurately measure and account for the unique risks associated with autonomous driving features, leading to inadequate insurance pricing and coverage, as they rely on outdated demographic and vehicle characteristic-based models that do not consider the advanced technologies and varying levels of automation in autonomous vehicles.
Innovation Solution
An electronic risk measuring and scoring system that assesses autonomous vehicles by evaluating component valuation, operational, contextual, technical, legal, and cyber risks, using benchmarking and simulation to generate risk classes and scores for dynamic risk-transfer modeling, allowing for real-time adaptation of insurance premiums based on the level of automation and usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demographic and vehicle characteristic-based models are used for risk assessment, then the system is simple and easy to implement, but it cannot accurately measure and account for unique risks associated with autonomous driving features
Solution Approach 1:
The risk assessment system is segmented into multiple specialized modules: autonomous operation feature identification module, operational risk data module, contextual risk data module, technical performance module, legal risk data module, and cyber risk data module. Each module handles specific aspects of risk assessment, allowing the system to accurately evaluate autonomous vehicle risks while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
The electronic risk measuring and scoring system is designed as a multi-functional platform that handles diverse risk types (operational, contextual, technical, legal, cyber) and serves multiple purposes: risk assessment, insurance pricing, and coverage determination. This universal system replaces multiple separate assessment approaches with a single integrated solution that accommodates various autonomous driving scenarios.
2Measurement precision
If comprehensive risk data collection and analysis systems are implemented, then risk assessment accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-identifying autonomous operation features and categorizing risk types before detailed assessment. The electronic system pre-processes and structures data from multiple sources (operational, contextual, technical, legal, cyber) into organized modules, enabling faster subsequent risk calculation and insurance pricing without sacrificing assessment accuracy.
Solution Approach 2:
The patent replaces manual or mechanical risk assessment processes with an electronic risk measuring and scoring system that automatically collects, processes, and analyzes comprehensive risk data. This electronic substitution enables rapid processing of large datasets from multiple risk categories, significantly reducing data processing time while maintaining high assessment accuracy through automated computational methods.
Data Source
AI summary
Proposed is an electronic risk measuring and scoring system and method for an autonomous vehicle, comprising: an vehicle component valuation unit to valuate the autonomous vehicle; an automated ranking unit for determining ranking associated with forward-looking accident frequencies and severities for the autonomous vehicle based on the valuation thereof, and at least on one of operational risk data, contextual risk data, technical performance of the autonomous vehicle, legal risk data and cyber risk data therefor; a benchmarking unit for benchmarking autonomous vehicle risks associated with the autonomous vehicle based on testing and/or simulating an autonomous vehicle component modelling structure for the autonomous vehicle; a risk class unit for generating a risk space with one or more risk classes for the autonomous vehicle based on the benchmarking; and a scoring unit for generating scores or indices, to calibrate and rate and/or price risk-transfers, and/or as input for risk-transfer modeling.