Dynamic Acceleration Probability for Fuel Consumption
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Solution Overview
Problem
Conventional navigation systems face inaccuracies in determining fuel consumption due to reliance on static parameters, which are insufficient in dynamically changing traffic situations, and fail to accurately assess performance factors like fuel consumption.
Innovation Solution
A system that analyzes acceleration as a road segment characteristic, using a data storage medium, a bus interface, and a system controller to retrieve and update parameters of the probability distribution of acceleration from measured velocity and acceleration values, enabling more precise fuel consumption calculations and route optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static parameters (distance, number of curves, maximum or average velocity) are used to determine fuel consumption, then the system complexity is low, but the measurement precision of fuel consumption is insufficient
Solution Approach 1:
The patent transitions from static parameters to dynamic parameters by introducing probability distribution functions that continuously update based on real-time measured data (acceleration, velocity, position). The system dynamically adapts to changing traffic conditions and driver behavior, resolving the contradiction by making the estimation system flexible and responsive while maintaining manageable complexity through standardized statistical methods.
Solution Approach 2:
The system implements feedback by continuously measuring actual vehicle parameters (acceleration, velocity, position) and using these measurements to update the probability distribution functions. This closed-loop approach improves fuel consumption estimation accuracy by incorporating real-world data, while the feedback mechanism is managed through systematic data collection and processing protocols.
2Adaptability or versatility
If static parameters are used for route determination, then the ease of operation is high, but the adaptability to changing traffic conditions is poor
Solution Approach 1:
The system employs dynamic probability distribution functions that automatically adapt to changing traffic conditions, road types, and driver behavior patterns. This dynamic approach enhances adaptability while maintaining ease of operation through automated data processing and standardized statistical methods that require minimal manual intervention.
Solution Approach 2:
The system performs self-updating by automatically collecting measured data, processing it through probability distribution functions, and adjusting fuel consumption estimates without requiring manual recalibration. This self-service capability improves adaptability to changing conditions while keeping the system easy to operate, as the automation handles the complexity internally.
3Measurement precision
If actual driver behavior data is collected and analyzed, then the measurement precision of fuel efficiency improves, but the loss of time for data processing increases
Solution Approach 1:
The patent applies partial action by selectively processing data that most significantly impacts fuel consumption estimation. Rather than analyzing every possible parameter, the system focuses on key measured values (acceleration, velocity, position) that have the greatest influence on fuel efficiency, thereby improving measurement precision while minimizing data processing time through targeted data selection.
Solution Approach 2:
The system transforms raw measured data into meaningful parameters by fitting probability distribution functions to the data. This parameter transformation approach condenses large volumes of raw data into concise statistical representations (mean, standard deviation, distribution shape), improving fuel efficiency measurement accuracy while reducing processing time through efficient data summarization.
Data Source
AI summary
Systems and methods for analyzing acceleration as a road segment characteristic in a vehicle are provided for determining information about a route including for example, the estimated fuel consumption of the route. An example of a system includes a data storage medium for storing map data having road segments, a bus interface to a data bus for receiving measured values of a velocity or an acceleration from a velocity or acceleration sensor, a position data receiver configured to receive position data for determining a current position, and a system controller. The system controller is configured to identify a road segment associated with the current position. The system controller retrieves parameters of a probability distribution of acceleration associated with the identified road segment from the data storage medium. Updated parameters of the probability distribution of acceleration are determined from the measured values of the velocity or acceleration.