Driver Scoring Using Human, Vehicle, and Context Vectors
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Solution Overview
Problem
Existing systems fail to comprehensively assess driver behavior by considering a combination of human, vehicle, and contextual factors, limiting the effectiveness of driver scoring and adaptive responses.
Innovation Solution
A method and system that embeds human, vehicle, and context influencing factors into respective vectors, concatenates them, and determines a driver score using a machine learning algorithm, with an adaptive response engine providing tailored recommendations based on the major influencer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems assess driver behavior, then driver scoring is provided, but the assessment is not comprehensive enough to consider human, vehicle, and contextual factors together
Solution Approach 1:
The patent merges three separate assessment dimensions (human factors, vehicle factors, and contextual factors) into a unified driver scoring system. Each dimension is embedded into vectors that are concatenated to form a comprehensive assessment, allowing the system to consider multiple influencing factors simultaneously rather than assessing them in isolation.
2Reliability
If driver scoring is provided without considering multiple influencing factors, then the system is simpler, but the effectiveness of driver scoring and adaptive responses is limited
Solution Approach 1:
The patent segments the driver scoring system into three distinct modules: human factor assessment, vehicle factor assessment, and contextual factor assessment. Each module processes specific types of data independently before their results are integrated through vector concatenation. This segmentation allows the system to manage complexity while maintaining comprehensive and reliable scoring.
3Measurement precision
If comprehensive factors are incorporated into driver scoring, then accuracy and relevance are enhanced, but the system complexity increases
Solution Approach 1:
The patent transforms multiple influencing factors into standardized vector representations, changing the parameters from diverse data types into a unified format suitable for machine learning processing. This parameter transformation enables the system to handle complex multi-dimensional data while maintaining computational efficiency and system manageability.
Data Source
AI summary
A method of determining a driver score for a driver of a vehicle. The method includes receiving at least one human influencing factor and embedding the at least one human influencing factor as a human vector, receiving at least one vehicle influencing factor and embedding the at least one vehicle influencing factor as a vehicle vector, and receiving at least one context influencing factor and embedding the at least one context influencing factor as a context vector. The method also concatenates the human vector, the vehicle vector, and the context vector to generate a concatenated vector and determines the driver score for the driver based on the concatenated vector utilizing a machine learning algorithm.