The Modeling of Super Deep Learning Aiming at Knowledge Acquisition in Automatic Driving

Area of Science:

• Artificial Intelligence
• Robotics
• Control Systems

Background:

• Current automatic driving systems struggle with optimizing multiple objectives like energy efficiency, safety, and passenger comfort simultaneously.
• Existing research faces challenges in network resolution and GPU performance for complex driving scenarios.
• Optimal control for multitarget functions in autonomous vehicles remains an open research problem.

Purpose of the Study:

• To propose a novel machine learning framework for optimal multitarget control in automatic driving.
• To address the challenge of integrating diverse objectives such as energy saving, safe driving, headway distance, and comfort.
• To leverage expert driver knowledge for enhanced autonomous driving control.

Main Methods:

• Development of a new theory to map multitarget objective functions into a unified space.
• Introduction of a Super Deep Learning (SDL) framework for knowledge acquisition from expert drivers.
• Integration of fuzzy logic relationships with acquired driver knowledge for control optimization.

Main Results:

• Demonstrated a method to unify disparate objective functions in automatic driving control.
• Successfully implemented a machine learning framework (SDL) for acquiring and utilizing expert driver knowledge.
• Achieved optimal multitarget control by combining fuzzy relationships and learned driver expertise.

Conclusions:

• The proposed SDL framework offers a significant advancement in multitarget control for automatic driving.
• This approach integrates expert human driving strategies into autonomous systems for superior performance.
• The method is theoretically expected to outperform existing fuzzy control techniques, particularly in complex scenarios.