Visual Acceleration Indicator Using ML for Fuel-Efficient Driving

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Solution Overview

Problem

Many drivers lack the tools and knowledge to optimize their driving habits for improved fuel efficiency, despite the potential benefits of up to 30% improvement on highways and 40% in stop-and-go traffic.

Innovation Solution

A machine learning model trained on acceleration and fuel efficiency data provides real-time feedback to drivers through a visual indicator, suggesting optimal acceleration patterns based on their driving behavior.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Use of energy by moving object

If drivers maintain current driving habits, then ease of operation is maintained, but fuel efficiency deteriorates

Engineering Contradiction:

Improvefuel efficiencyVSAvoiddriving habit complexity

Core Design Contradiction:

Use of energy by moving objectVSEase of operation

Solution Approach 1:

The system continuously monitors driver acceleration behavior and provides real-time visual feedback through indicators that show whether current driving is fuel-efficient or could be improved. This feedback loop enables drivers to adjust their acceleration habits dynamically without requiring complex manual calculations or interventions.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The machine learning model is trained on aggregated data from multiple drivers and vehicles to automatically generate optimized acceleration recommendations. The system serves itself by continuously learning from real-world data and improving its recommendations without requiring manual reconfiguration or expert intervention.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If machine learning model is trained on comprehensive data, then measurement precision improves, but device complexity increases

Engineering Contradiction:

Improvefuel efficiency measurementVSAvoidmodel training complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The machine learning model is designed to handle multiple data sources (acceleration sensors, fuel consumption data, vehicle metadata) and serve multiple functions (predicting fuel efficiency, generating recommendations, evaluating driver behavior) within a single unified system, reducing overall complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system uses an intermediary processing layer that aggregates and preprocesses data from multiple vehicles and drivers before training the model. This intermediary layer simplifies the raw data into structured features that are easier for the model to process, reducing the computational complexity of training.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250304095A1Visual indicator for optimal acceleration determined by machine learning model trained on live phone and vehicle data

Publication Date: 2025.10.02 ZIEB KRISTOFER

AI summary

Methods and systems for determining optimal acceleration and related indication may be provided. A machine learning (ML) and/or artificial intelligence (AI) model may be trained using a plurality of acceleration and fuel efficiency data. The ML or AI model may be used to determine whether a given driver's driving behavior, for example acceleration and deceleration, are fuel efficient. In some embodiments an indicator may notify the driver of whether their driving behavior is fuel efficient and/or may indicate how the driving may be more fuel efficient.