Crowd-Sensed Fuel Consumption Prediction in Vehicle Navigation
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Solution Overview
Problem
Existing navigation systems lack the ability to provide drivers or autonomous systems with detailed fuel consumption and cost estimates for different routing options, relying on basic distance and time calculations that do not account for varying road conditions and vehicle-specific factors.
Innovation Solution
A system and method that collect crowd-sensed data from vehicles, including road grade, speed, acceleration, elevation, and weather, using both physics-based and machine learning models to estimate fuel consumption and cost, which are then integrated into navigation routing options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If navigation systems provide only basic distance and time calculations, then the system complexity remains low, but the accuracy of fuel consumption estimation deteriorates
Solution Approach 1:
The patent introduces crowd-sensed data from telematics systems as an intermediary to bridge the gap between basic navigation data and accurate fuel consumption estimation. This intermediary layer collects real-world driving data (acceleration, braking, speed, road grade) from multiple vehicles and uses it to train machine learning models that provide accurate fuel consumption predictions without requiring complex onboard sensors in each vehicle.
Solution Approach 2:
The patent replaces complex mechanical sensor systems with data-driven machine learning models. Instead of equipping each vehicle with sophisticated fuel consumption sensors, the system uses machine learning algorithms trained on crowd-sensed data to predict fuel consumption, substituting physical measurement systems with computational models that achieve high accuracy.
2Measurement precision
If navigation systems collect and process crowd-sensed data from multiple vehicles, then fuel consumption estimation accuracy improves, but data collection and processing complexity increases
Solution Approach 1:
The patent makes the navigation system universal by integrating multiple data sources (map data, point of interest data, real-time traffic data, crowd-sensed data) into a single unified system. The machine learning model processes all these diverse data types to provide comprehensive fuel consumption estimates, making the system adaptable to various driving conditions and vehicle types without requiring separate specialized systems.
Solution Approach 2:
The patent implements feedback mechanisms where crowd-sensed data from actual vehicle operations is continuously collected and used to retrain and refine the machine learning models. This feedback loop allows the system to improve its estimation accuracy over time by learning from real-world driving patterns, adjusting to changing conditions, and correcting any systematic errors in the predictions.
3Reliability
If navigation systems integrate multiple data sources and machine learning models, then routing decision quality improves, but computation time and processing resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing crowd-sensed data, map data, and training models before they are needed for routing decisions. The machine learning models are trained in advance on historical data, and the results are cached or pre-computed for common routing scenarios. When a user requests navigation, the system can quickly retrieve and apply pre-processed information rather than performing all computations in real-time.
Data Source
AI summary
A system and method for providing navigation routing options to a vehicle driver, including estimated fuel consumption and fuel cost. A server collects data from a large number of road vehicles driving different routes, where the data includes road grade, average speed, stop/start and acceleration/deceleration info and vehicle specifications, and the data is collected via a telematics or other wireless system. The server also receives map data, point of interest data and real-time traffic data from their respective providers. When a driver of a road vehicle requests navigation routing from a start point to a destination, the server provides multiple routing options including not only distance and time for each routing option, but also fuel consumption and cost. The estimated fuel consumption is computed using models based on the crowd-sensed data from the other vehicles driving the routes, where the models include a physics-based model and a machine learning model.