Driver Scoring Normalization Using Road Quality Context
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Solution Overview
Problem
Conventional driver scoring techniques do not consider road conditions, leading to inaccurate assessments of driver behavior and vehicle events.
Innovation Solution
Incorporating road quality and condition data into driver scoring algorithms to adjust scoring based on road characteristics, such as potholes or uneven surfaces, using a Safety Driving Model (SDM) to normalize driving events and improve scoring accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional driver scoring techniques are used that rely only on vehicle-generated data, then the scoring system is simple to implement, but the measurement precision of driver behavior assessment deteriorates
Solution Approach 1:
The patent introduces road quality data as an intermediary element that mediates between the driver's actual behavior and the scoring assessment. By incorporating external road condition information (potholes, construction zones, weather conditions) as a mediating factor, the system can distinguish between intentional poor driving behavior and reactions to adverse road conditions, thereby improving measurement precision without requiring fundamental changes to the core scoring architecture
Solution Approach 2:
The system performs preliminary assessment of road conditions before evaluating driver behavior. By pre-fetching and analyzing road quality data (construction zones, potholes, weather conditions) prior to driver event evaluation, the system prepares contextual information in advance that enables more accurate real-time scoring decisions, improving precision while maintaining operational efficiency
2Measurement precision
If road quality data is incorporated into driver scoring algorithms, then the measurement precision of driver assessment improves, but the device complexity increases
Solution Approach 1:
The patent segments the driver scoring process into distinct modules: road quality data acquisition, road condition analysis, driver behavior event detection, and integrated scoring. By dividing the complex processing into separate functional segments, each handling specific aspects of data analysis, the system improves measurement precision through comprehensive evaluation while managing complexity through modular architecture that allows independent optimization of each component
Solution Approach 2:
The system dynamically adjusts scoring parameters and weights based on road quality conditions. When adverse road conditions are detected (construction zones, poor surface quality, weather issues), the system modifies the sensitivity and thresholds of driver behavior evaluation parameters accordingly. This adaptive parameter adjustment improves scoring accuracy by contextualizing driver actions without requiring complete redesign of the scoring algorithm
Data Source
AI summary
Techniques are disclosed to determine driver scoring that consider road quality and/or conditions in driver score computations. In contrast to the conventional approaches, the use of road conditions and/or road quality in the computation of driver scores allows for the consideration of road conditions and/or quality to improve upon conventional scoring techniques.