Fuel Consumption Prediction Using Driver Behavior Features
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Solution Overview
Problem
Existing methods for predicting fuel consumption efficiency fail to account for various driver-related factors, leading to inaccurate predictions of future fuel consumption.
Innovation Solution
A method and system that analyze past user driving behavior to determine driving features affecting fuel consumption efficiency, combined with information about future trips, to predict future fuel consumption using machine learning models like artificial neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple mileage tracking between refills is used to predict fuel consumption, then the prediction method is simple and easy to implement, but the prediction accuracy is low because driver-related factors are not considered
Solution Approach 1:
The patent segments the fuel consumption prediction into multiple components: trip-specific factors (route, distance, traffic) and driver behavior factors (acceleration patterns, braking habits, idle time). By analyzing past trips and segmenting driver behaviors into quantifiable features, the system achieves accurate predictions while maintaining implementation feasibility through modular data collection and processing.
Solution Approach 2:
The system performs preliminary analysis of past driving behavior to establish driver-specific patterns before making future predictions. By pre-processing historical data to extract meaningful features (acceleration rates, braking intensity, route preferences), the system prepares predictive models in advance that can quickly and accurately predict future fuel consumption without complex real-time calculations.
2Measurement precision
If driver behavior analysis is incorporated into fuel consumption prediction, then the prediction accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between raw driving data and prediction output: a feature extraction module that converts complex driver behaviors into standardized metrics (e.g., average acceleration rate, braking frequency, idle duration). This intermediary processing simplifies the data structure and makes it easier to integrate with trip-specific factors, reducing overall system complexity while maintaining prediction accuracy.
Solution Approach 2:
The system uses a unified predictive model that handles both trip-specific factors and driver behavior factors through a single algorithmic framework. By designing a multi-functional prediction engine that can process various input types (route data, traffic conditions, driver patterns) through the same computational structure, the patent avoids the need for multiple separate systems, thereby reducing complexity while achieving comprehensive prediction accuracy.
Data Source
AI summary
Method and system for predicting fuel consumption efficiency. For example, the method includes collecting past user driving data for one or more past vehicle trips that have already been made by a user, analyzing the past user driving data to determine one or more past user driving features related to a past fuel consumption efficiency of the user, collecting information for one or more future vehicle trips that will be made by the user during a predetermined future period of time, and predicting a future fuel consumption efficiency of the user during the predetermined future period of time based at least in part upon the information for the one or more future vehicle trips and the determined one or more past user driving features.