AV Ride Insurance Pricing Using Predictive Risk Models
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Solution Overview
Problem
The emergence of autonomous vehicles poses challenges in determining insurance premiums, as conventional methods based on driver and vehicle attributes are insufficient for autonomous driving scenarios, requiring new techniques to assess risk and calculate premiums dynamically.
Innovation Solution
An AI-based infrastructure using machine-learning techniques evaluates risk and determines insurance premiums for autonomous vehicle rides through Usage-Based Insurance Pricing, incorporating data on routes, vehicles, weather, and road conditions, and employing pre-trained risk and pricing models to calculate risk values and loss values for each category.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional insurance pricing methods based on driver and vehicle attributes are used, then the pricing process is simple and straightforward, but the pricing accuracy and risk assessment are insufficient for autonomous driving scenarios
Solution Approach 1:
The patent segments the risk assessment into multiple independent data categories (route data, vehicle data, weather and road condition data) with dedicated risk models for each category. This segmentation allows the system to handle complexity in a structured manner while improving overall assessment accuracy through specialized evaluation of each factor.
Solution Approach 2:
The patent employs pre-trained risk models that are prepared in advance through training on historical data. These models are developed beforehand and can be directly applied to new rides without requiring real-time complex calculations, thus improving assessment speed and accuracy while managing computational complexity.
2Adaptability or versatility
If dynamic risk-based pricing is implemented for each ride, then the insurance premium accuracy is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system divides the comprehensive risk assessment into separate category-specific assessments (route, vehicle, weather conditions). Each category has its own risk model that processes relevant data independently, then combines results to form the overall premium. This segmentation enables customized pricing while managing data processing complexity through modular architecture.
Solution Approach 2:
The patent changes the pricing parameter from static (based on driver/vehicle attributes) to dynamic (based on real-time risk factors). By using pre-trained models that output risk values for different data categories, the system adapts pricing to specific ride conditions while maintaining computational efficiency through parameter optimization.
3Reliability
If multiple data categories are collected and processed for each ride, then the risk evaluation comprehensiveness is improved, but the data collection and processing time increases
Solution Approach 1:
The patent performs data collection and model training in advance. Historical data is gathered and used to train risk models before they are deployed for actual ride pricing. This preliminary action ensures comprehensive data is available for reliable risk evaluation while reducing real-time processing requirements, as the models are already trained and ready for rapid inference.
Solution Approach 2:
The patent replaces manual or rule-based risk assessment with automated machine learning models. These models automatically process multiple data categories and generate risk evaluations without requiring manual intervention or complex real-time calculations, thus improving reliability while minimizing time loss through automated processing.
Data Source
AI summary
The present disclosure relates generally to systems for facilitating the use of autonomous vehicles (AVs), and more particularly to automated artificial intelligence (AI)-based techniques for determining an insurance premium for an AV ride based upon various factors including the evaluation of risk associated with the AV ride. An automated AI-based infrastructure is provided that uses automated machine-learning (ML) based techniques for evaluating a level of risk for any particular AV ride and then determining an insurance premium for the AV ride based on the level of risk. The insurance premium determination incorporates Usage Based Insurance Pricing (UBIP) that has been customized for autonomous driving, whereby the level of risk is predicted based on information associated with the expected usage of the AV during the ride. Thus, the insurance premium is customized for each ride and can be determined as part of calculating upfront the total price of the ride.