Compensating Driver Speed-Tracking Error With NAR Neural Network
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Solution Overview
Problem
Drivers often experience speed-tracking errors when attempting to follow recommended speeds, leading to diminished benefits in traffic flow and fuel efficiency.
Innovation Solution
A system utilizing a nonlinear autoregressive (NAR) neural network to predict and compensate for speed-tracking errors in real-time by classifying drivers into types based on historical data, computing a compensated recommended speed, and communicating it to the driver through a human-machine interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a driver is provided with a recommended speed, then traffic flow and fuel efficiency are improved, but speed-tracking error occurs which diminishes these benefits
Solution Approach 1:
The system performs preliminary classification of drivers into types based on historical speed-tracking error data before providing recommended speeds. By pre-characterizing driver behavior patterns, the system can anticipate tracking errors and adjust recommendations proactively, thereby maintaining both traffic flow efficiency and speed-tracking accuracy.
Solution Approach 2:
The system continuously monitors actual speed deviations from recommended speeds and uses this feedback to refine driver classification and update future speed recommendations. This closed-loop feedback mechanism enables the system to learn from past tracking errors and improve subsequent recommendations, resolving the contradiction between providing guidance and achieving accurate tracking.
2Use of energy by moving object
If speed recommendations are provided to improve fuel efficiency, then energy consumption is reduced, but driver speed-tracking error diminishes the benefit
Solution Approach 1:
The system changes the parameter of recommended speed based on the driver's classified type and predicted tracking behavior. By adjusting the recommended speed parameter to account for anticipated tracking errors, the system ensures that the actual achieved speed maintains optimal fuel efficiency while achieving reliable speed tracking for that specific driver type.
3Measurement precision
If the system provides personalized speed recommendations, then speed-tracking error is reduced, but device complexity increases due to driver classification and prediction mechanisms
Solution Approach 1:
The system segments the driver population into distinct types based on speed-tracking behavior patterns. By dividing drivers into categories, the system can apply simplified prediction models specific to each segment rather than attempting to model all drivers individually, thereby improving tracking accuracy while managing system complexity through structured segmentation.
Solution Approach 2:
The system creates simplified behavioral models (copies) of driver types based on historical data. Instead of complex real-time analysis of each driver's unique characteristics, the system uses these pre-created behavioral patterns to predict tracking errors and adjust recommendations, reducing computational complexity while maintaining prediction accuracy.
Data Source
AI summary
Systems and methods for compensating for driver speed-tracking error are disclosed herein. One embodiment computes a recommended speed for a vehicle; classifies a driver of the vehicle as a particular type of driver among a plurality of driver types based on measured speed-tracking error, wherein the speed-tracking error is a difference between the recommended speed and an actual speed of the vehicle; predicts the speed-tracking error at a future time increment based on the speed-tracking error at one or more past time increments using a nonlinear autoregressive (NAR) neural network associated with the particular type of driver; computes a compensated recommended speed for the vehicle based on the recommended speed and the predicted speed-tracking error at the future time increment; and communicates the compensated recommended speed to the driver.