An Intelligent Driver Training System Based on Real Cars.

Area of Science:

• * Engineering and Machine Learning
• * Automotive Training Systems

Background:

• * Effective driver training relies on accurate observation of trainee performance.
• * Vehicle operation can be objectively quantified through changes in vehicle state parameters.

Purpose of the Study:

• * To develop and validate a driver training model using a multiple-embedded-sensor network.
• * To quantitatively assess critical vehicle state parameters for machine learning models.

Main Methods:

• * Identification and quantitative representation of six critical vehicle state parameters.
• * Development of a multiple-embedded-sensor net system for real-time data collection.
• * Data vectorization, sequencing, and labeling, validated by expert driving instructors.

Main Results:

• * A trained model capable of providing real-time operational guidance to drivers.
• * The model effectively analyzes post-training performance improvements.
• * Demonstrated good results in practical applications, validating the system's effectiveness.

Conclusions:

• * The proposed driver training model is valid and reliable.
• * The multiple-embedded-sensor net approach offers a robust method for driver performance assessment.
• * This technology enhances the quality and efficiency of driver training programs.