Driver-Specific Vehicle Setup Prediction for Race Tuning
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Solution Overview
Problem
Conventional systems fail to accurately predict driver subjective preferences for race vehicle setups, leading to inefficient optimization and reliance on trial-and-error methods for vehicle configuration.
Innovation Solution
A machine learning-based system is trained on driver-specific feedback to predict subjective opinions on various vehicle setups, allowing for the selection of optimal configurations that balance subjective preferences with objective performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional trial-and-error methods are used for vehicle setup optimization, then real-world simulations are required to test each configuration, but this leads to excessive time consumption and reduced productivity
Solution Approach 1:
The patent creates a digital twin or virtual model of the driver's subjective preferences through machine learning. Instead of physically testing each vehicle setup with the real driver, the system uses a trained model that copies and replicates driver feedback patterns. This virtual copy allows rapid evaluation of multiple setups without time-consuming real-world simulations, directly resolving the contradiction between prediction accuracy and time consumption.
Solution Approach 2:
The system performs preliminary training of the machine learning model using historical driver feedback data before actual vehicle setup optimization begins. This preliminary action creates a pre-trained predictive capability that can quickly evaluate new setups without requiring fresh real-world testing. The model is prepared in advance to anticipate driver preferences, eliminating the need for time-consuming trial-and-error testing during the optimization process.
2Reliability
If extensive real-world simulations are conducted to test vehicle setups, then accurate driver preference data is obtained, but the complexity of the optimization process increases significantly
Solution Approach 1:
The patent replaces the mechanical system of physical vehicle setup changes and real-world track testing with an information-processing system. Instead of mechanically adjusting vehicle parameters and physically testing each configuration, the system uses machine learning algorithms to process historical feedback data and computationally predict driver preferences. This substitution maintains optimization reliability while reducing the operational complexity of the process.
Solution Approach 2:
The machine learning model serves as an intermediary between the vehicle setup parameters and driver subjective feedback. Rather than directly connecting physical setup adjustments with real-world driver testing, the model mediates by translating setup parameters into predicted driver responses based on learned patterns. This intermediary layer simplifies the optimization system by eliminating the need for complex real-world testing infrastructure while maintaining prediction accuracy.
3Adaptability or versatility
If manual vehicle setup adjustment is performed based on driver feedback, then customization to driver preferences is achieved, but the productivity and efficiency of the optimization process decreases
Solution Approach 1:
The system enables self-service optimization by allowing the machine learning model to automatically predict driver preferences and identify optimal vehicle setups without requiring manual driver involvement for each testing iteration. The model serves itself by learning from historical data and independently evaluating multiple configurations, maintaining high adaptability to driver preferences while dramatically improving productivity through automation.
Solution Approach 2:
The system efficiently explores the vehicle setup space by systematically changing parameters and using the machine learning model to predict their impact on driver preferences. Instead of manual trial-and-error adjustment, the system computationally evaluates multiple parameter combinations simultaneously, identifying optimal configurations that customize to driver preferences while maintaining high productivity through parallel virtual testing rather than sequential physical adjustments.
Data Source
AI summary
Systems and methods are provided for optimizing vehicle setups through predictive determinations on subjective driver preferences. Examples provided herein include training a driver specific model based on first vehicle setups and subjective driver feedback of a driver on each first vehicle setup; applying the driver specific model on a second vehicle setup; generating, by the driver specific model, predicted subjective driver feedback on the second vehicle setup predictive of an opinion of the driver on the second vehicle setup; and selecting an optimal vehicle setup based on the predicted subjective driver feedback.