Vehicle Brake Control Mode Selection Using Machine Learning
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Solution Overview
Problem
Existing vehicle control systems lack the ability to effectively adapt to a wide variety of vehicle exterior and interior conditions, limiting their ability to set suitable control modes accordingly.
Innovation Solution
A vehicle control device that includes an information acquisition unit for gathering vehicle condition information and a mode acquisition unit using machine learning to determine a suitable control mode based on this information, allowing for adaptable control modes in response to diverse conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based algorithms are used to determine control modes, then the system structure is simple and easy to understand, but the system cannot effectively adapt to a wide variety of vehicle exterior and interior conditions
Solution Approach 1:
The patent replaces rule-based algorithms with machine learning models to determine control modes. The machine learning model processes vehicle condition information (both exterior and interior conditions) to output appropriate control modes, replacing the mechanical rule-based decision-making system with an intelligent learning-based system that can adapt to diverse conditions without requiring explicit programming for each scenario.
2Adaptability or versatility
If machine learning is used to estimate control modes, then the system can adapt to diverse vehicle conditions, but dedicated learning data generation is costly and time-consuming
Solution Approach 1:
The system uses the vehicle's own operational data and condition information as training data for the machine learning model. Instead of requiring external dedicated data generation, the vehicle collects and utilizes its own driving conditions, sensor data, and control mode outcomes to train and refine the model, making the data generation process self-service and eliminating the need for costly and time-consuming dedicated data collection campaigns.
3Adaptability or versatility
If multiple control modes are implemented to cover various conditions, then the system becomes more versatile, but the complexity of selecting the appropriate mode increases
Solution Approach 1:
The machine learning model continuously receives feedback from vehicle condition sensors and control outcomes to determine the most appropriate control mode. The system processes multiple vehicle condition parameters (exterior and interior) simultaneously and uses the trained model to automatically select the optimal control mode, providing a systematic feedback-based approach that simplifies the decision-making process compared to manual rule-based selection among multiple modes.
Data Source
AI summary
A braking control device includes an information acquisition unit that acquires vehicle condition information related to at least one of an external condition of a vehicle and an internal condition of the vehicle, and a mode acquisition unit that acquires, from among the plurality of control modes, a control mode according to an index output from a learner by inputting the vehicle condition information acquired by the information acquisition unit to the learner that has been subjected to machine learning for estimating the control mode suitable for the vehicle condition information.