Effectiveness evaluation of connected and automated vehicles' driving loop: Node weights and driving reliability.

Area of Science:

• Automotive Engineering
• Network Science
• Systems Engineering

Background:

• Connected and Automated Vehicles (CAVs) represent a significant trend in the automotive industry.
• Effective network modeling and driving loop evaluation are crucial for CAV development.
• Existing models may not fully capture the complexity of CAV driving processes.

Purpose of the Study:

• To construct a network model for the driving loop of CAVs.
• To develop a method for evaluating the effectiveness of this driving loop model.
• To optimize CAV system performance, driving safety, and reliability.

Main Methods:

• Integration of the driving process with the Observation, Orientation, Decision, and Action (OODA) loop concept.
• Application of Interpretive Structural Model (ISM) and complex network theory to measure node importance.
• Utilization of fuzzy evaluation for driving reliability and comprehensive node weight determination.
• Enhancement of the information entropy model using determined node weights for effectiveness evaluation.

Main Results:

• A novel network model for the CAV driving loop was successfully established.
• A robust method for evaluating driving loop effectiveness, considering node importance and reliability, was proposed.
• The proposed method demonstrated effectiveness in optimizing CAV network models across various scenarios.

Conclusions:

• The developed network model and evaluation methodology provide a scientific basis for CAV driving loop analysis.
• This approach facilitates effective planning, modeling, evaluation, and optimization of CAV systems.
• The findings contribute to enhancing the safety and reliability of automated driving technologies.