Driver Fuel Efficiency Ranking via Environmental Segmentation
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Solution Overview
Problem
Conventional fuel efficiency ranking systems fail to account for environmental factors such as temperature and traffic conditions, leading to unfair comparisons among drivers and lack of motivation for eco-driving, as they only consider vehicle models without controlling other influencing variables.
Innovation Solution
A method and system that collect and analyze vehicle data, including environmental and traffic factors, to calculate a driver's fuel efficiency ranking by using a communication device, servers for data processing, and statistical methods to regularize fuel efficiency, considering variables like temperature, traffic congestion, and vehicle model, resulting in a fair and real-time ranking system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems rank fuel efficiency based only on vehicle models, then the ranking system is simple to implement, but the ranking becomes unfair and inaccurate because it ignores environmental factors like temperature and traffic conditions
Solution Approach 1:
The patent segments the fuel efficiency evaluation by dividing drivers into different categories based on environmental factors (temperature zones, traffic conditions, vehicle types). This segmentation allows for fairer comparisons within each category rather than a single overall ranking, improving measurement precision while managing complexity through structured classification.
Solution Approach 2:
The system changes the evaluation parameters by introducing multiple variables (temperature, traffic congestion, vehicle model) instead of using only vehicle model. The server adjusts fuel efficiency values based on these parameters to normalize comparisons across different driving conditions, thereby improving ranking accuracy.
2Measurement precision
If the system considers multiple environmental factors and traffic conditions, then the fuel efficiency ranking becomes fairer and more accurate, but the data collection and processing complexity increases
Solution Approach 1:
The communication device performs multiple functions: it collects vehicle data, detects environmental factors (temperature, traffic), and transmits all this information to the server. This multi-functionality reduces the need for separate specialized devices, making the system more practical despite the increased measurement requirements.
Solution Approach 2:
The server acts as an intermediary that receives raw data from multiple sources (vehicle sensors, environmental sensors), processes and normalizes the information, and generates the final ranking. This intermediary approach simplifies the overall system architecture by centralizing the complex data processing tasks.
3Productivity
If real-time fuel efficiency data is collected and processed, then drivers receive timely feedback for motivation, but the computational resources and processing time required increase
Solution Approach 1:
The system performs fuel efficiency ranking calculations at periodic intervals (e.g., after completing a trip or at regular time intervals) rather than continuously. This periodic processing reduces the computational burden on the server while still providing timely feedback to drivers, balancing productivity with energy consumption.
Data Source
AI summary
Disclosed is a technique for ranking a driver's fuel efficiency among other drivers based on various variables associated with the environment in which the vehicle is being driven. More particularly, the present invention, selects, as a category, one or more factors affecting fuel efficiency, and, as one or more, variables another one or more. Vehicle data is then collected by a communications device which is associated with a particular trip. Next an average of the fuel efficiencies and an average of the variables for the trip are calculated accordingly the selected one more categories. The fuel efficiency of the trip corresponding to a same category for every coordinate point of the variable is then statistically processed to calculate the average of the fuel efficiency and the standard deviation of the fuel efficiency and regularized (processed as a GAP calculation) to determine the ranking of a vehicle driver.