Mobile Sensor Fuel Efficiency Scoring for Near-Real-Time Driver Feedback

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Solution Overview

Problem

Existing systems fail to provide near-real-time fuel efficiency feedback to drivers, limiting their ability to improve driving habits and reduce fuel waste based on trip data.

Innovation Solution

Utilizing cell phone sensors, such as GPS and MEMS, to capture driving events like braking, speeding, and acceleration, and applying a trained prediction model to generate a fuel efficiency score.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If OBDII devices or integrated vehicle systems are used to collect fuel consumption data, then measurement precision is improved, but device complexity increases and data availability to drivers is delayed

Engineering Contradiction:

Improvefuel consumption data accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent uses a mobile device as a simplified copy of the vehicle's actual fuel consumption measurement system. Instead of requiring complex OBDII hardware integration, the mobile device independently captures driving events through its own sensors (accelerometer, GPS, microphone) and uses machine learning models to estimate fuel consumption based on these captured driving patterns, providing accurate fuel efficiency data without complex vehicle system integration

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The mobile device acts as an intermediary between the driver and the vehicle's fuel consumption data. It captures driving events independently through its sensors and communicates fuel efficiency information to the driver, eliminating the need for direct integration with the vehicle's OBDII system while still providing accurate measurement through indirect observation of driving behavior

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If OBDII devices are used to collect fuel consumption data, then measurement precision is improved, but loss of time increases as drivers cannot access feedback shortly after trips

Engineering Contradiction:

Improvefuel consumption data accuracyVSAvoiddata availability delay

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary data collection and processing by capturing driving events continuously during the trip using the mobile device's sensors. The machine learning model processes this data and generates fuel consumption estimates in near-real-time, making feedback available to drivers immediately after trips rather than requiring delayed processing through complex vehicle systems

Inventive Principle:

Principle #10Preliminary action

3Device complexity

If mobile device sensors are used to capture driving events, then device complexity is reduced, but measurement precision of fuel consumption data deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoidfuel consumption data accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system transforms the measurement approach by changing from direct fuel consumption measurement (requiring complex hardware) to indirect measurement through driving event parameters. The machine learning model converts sensor data about acceleration, braking, speed, and route characteristics into accurate fuel consumption estimates, achieving precise measurements through parameter transformation rather than direct sensing

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces mechanical/OBDII-based direct measurement systems with a software-based machine learning approach. Instead of using complex hardware to directly measure fuel consumption, the system uses sensor data combined with trained machine learning models to predict fuel efficiency, substituting physical measurement mechanisms with computational intelligence

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Measurement precision

If comprehensive sensor data collection is implemented, then measurement precision is improved, but use of energy by the mobile device increases

Engineering Contradiction:

Improvedriving event detection accuracyVSAvoidmobile device energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by selectively activating sensors and processing data only when driving events are detected. Instead of continuously collecting and processing all sensor data, the machine learning model identifies relevant driving events (acceleration, braking, idling) and focuses computational resources on these specific events, reducing overall energy consumption while maintaining measurement precision for fuel-critical behaviors

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250362139A1Systems and methods for generating fuel efficiency score based on cell phone sensor data

Publication Date: 2025.11.27 ALLSTATE INSURANCE COMPANY

AI summary

Implementations claimed and described herein provide systems and methods for determining fuel efficiency based on sensor data from a mobile device. In one implementation, sensor data from a mobile device is collected. The sensor data includes a dataset that reflects a last trip on a vehicle by the mobile device, wherein the sensor data is collected from at least one of global position system (GPS) data and micro-electro-mechanical system (MEMS) sensor data of the mobile device. Driving events comprising at least one of one or more braking events, one or more speeding events, and one or more acceleration events are determined based on the sensor data. A fuel consumption prediction is predicted via a trained prediction model based on the driving events.