Driver-Centric Fuel Efficiency Prediction Using Behavioral Data
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Solution Overview
Problem
Drivers experience significant variations in fuel efficiency due to factors like speed, congestion, weather, and driving behavior, making it difficult to predict efficiency when switching between vehicle classes or models, as manufacturers' estimates do not account for individual driving habits and conditions.
Innovation Solution
A vehicle-based computing system that uses a processor to gather and analyze user data, compare it to similar drivers' data, and provide personalized fuel efficiency predictions, as well as offer coaching to mimic efficient driving behaviors, utilizing cruise control to mirror the behavior of more efficient drivers on specific routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manufacturer MPG estimates are used for comparing vehicles, then standardization and comparability are improved, but accuracy in real-world conditions deteriorates
Solution Approach 1:
The system creates a digital copy of efficient driver behavior by recording and storing driving patterns, pedal inputs, steering actions, and other operational data. This copied behavior is then used to generate instructional content that helps less efficient drivers replicate the efficient driving patterns, thereby improving real-world MPG accuracy while maintaining standardized comparison frameworks
Solution Approach 2:
The system implements feedback by comparing a driver's actual behavior against the copied efficient driver behavior, providing real-time or post-trip feedback on deviations. This feedback loop enables drivers to understand how their behavior impacts MPG and makes adjustments to align with efficient patterns, bridging the gap between estimated and actual fuel efficiency
2Ease of manufacture
If standardized MPG testing is used, then manufacturing simplicity is improved, but driver-specific behavior adaptation deteriorates
Solution Approach 1:
The system enables self-service by allowing drivers to automatically record their own driving behavior, store it in their personal profiles, and generate their own customized instructional content without requiring manufacturer intervention. The system autonomously compares behavior, identifies inefficiencies, and provides personalized feedback, adapting to each driver's unique patterns while maintaining simple standardized testing protocols
3Measurement precision
If driver behavior data is collected and analyzed, then fuel efficiency prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system extracts only the most relevant behavioral parameters from complex driving data, such as acceleration patterns, braking behavior, steering inputs, and pedal application rates. By selectively extracting key metrics rather than processing all raw data, the system maintains high prediction accuracy while reducing computational complexity and processing requirements
Data Source
AI summary
A system includes a processor configured to receive a user profile responsive to an efficiency determination request for a vehicle model. The processor is also configured to obtain efficiency-affecting data from the user profile. The processor is further configured to compare the efficiency-affecting data to data gathered from drivers of the vehicle model, to determine a correlation between the user profile and similar drivers of the vehicle model. Also, the processor is configured to predict fuel efficiency for the new vehicle model based on efficiency achieved by the similar drivers.