Vehicle Insurability Scoring Using Context-Aware Peer Comparison
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Solution Overview
Problem
Current systems for measuring vehicle operator insurability rely on absolute measures that do not consider the driving context, leading to penalties for safe maneuvers required by the environment, and fail to accurately assess operator skill and risk.
Innovation Solution
A system that uses a host vehicle and one or more remote vehicles, along with sensors and a cloud computing server, to capture and analyze vehicle and environmental data. This system employs a MIROP application with various control logics to assess vehicle operator performance relative to others in similar contexts, providing a context-aware insurability score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If absolute measures such as hard braking or acceleration are used to assess insurability, then the assessment process is simple, but the accuracy of risk assessment deteriorates because context is not considered
Solution Approach 1:
The patent segments the risk assessment process into multiple components: event detection (hard braking, acceleration), context assessment (road surface conditions, traffic density, physical location), and relative performance comparison. This segmentation allows the system to consider multiple factors for accurate assessment while maintaining modular system architecture that manages complexity.
Solution Approach 2:
The patent introduces cloud computing servers as intermediaries that aggregate data from multiple vehicles and perform relative performance comparisons. This intermediary layer enables context-aware assessment by comparing operator behavior against peers in similar contexts, improving accuracy while distributing computational complexity across the network rather than requiring complex onboard processing in each vehicle.
2Reliability
If absolute thresholds are used to penalize driving events, then the system is easy to implement, but false positives increase because safe maneuvers required by context are penalized
Solution Approach 1:
The patent applies local quality by assessing driving events relative to local context conditions. Instead of applying uniform absolute thresholds, the system evaluates hard braking or acceleration events based on local factors such as road surface conditions (ice, rain, construction), traffic density, and physical location. This allows safe maneuvers required by local conditions to be distinguished from truly risky behavior, improving scoring reliability.
Solution Approach 2:
The patent changes the assessment parameter from absolute thresholds to relative performance metrics. Instead of penalizing hard braking above a fixed threshold, the system compares the operator's events and context against peer operators in similar situations. This parameter change eliminates false positives by contextualizing each event rather than applying rigid absolute rules.
3Adaptability or versatility
If existing hardware and systems are utilized, then device complexity is minimized, but the ability to implement relative performance measurement is limited
Solution Approach 1:
The patent achieves versatility by making existing hardware multi-functional. Standard sensors already present in vehicles (accelerometers, GPS, telematics systems) are utilized not only for their primary functions but also for detecting hard braking, acceleration events, and contextual information like location and speed. This universal use of existing components enables relative performance measurement without adding dedicated hardware, minimizing complexity while enhancing capability.
Data Source
AI summary
A system for measuring insurability based on relative vehicle operator performance (MIROP) includes sensors capturing host and remote vehicle information, and information about an environment of the host and remote vehicles. A controller executes a MIROP application that identifies event information within data obtained from the sensors, transmits, the event information to a cloud computing server, assesses a physical location and proximity of the host vehicle to remote vehicles participating in the system. The MIROP application assesses a road surface condition of a road segment upon which the host vehicle is traveling, estimates a traffic density on the road segment, aggregates host vehicle behavioral data and identifies event IDs within the behavioral data. The MIROP application computes a vehicle operator insurability score and automatically notifies an insurance carrier of the score as well as automatically presenting the score and suggestions to improve the score to a host vehicle operator.