Basic Simulation Environment for Highly Customized Connected and Autonomous Vehicle Kinematic Scenarios.

Area of Science:

• Automotive Engineering
• Robotics
• Simulation Technology

Background:

• Connected and Autonomous Vehicles (CAVs) require accurate kinematic simulations for reliable verification.
• Existing simulation frameworks may lack the fidelity to represent real-world complexities.

Purpose of the Study:

• To propose a novel four-layer framework for enhancing the reality of CAV kinematic simulation scenarios.
• To ensure the accuracy and reliability of CAV verification through a robust simulation methodology.

Main Methods:

• Developed a four-layer framework: road network, vehicle operating, uncertainties modelling, and demonstrating layers.
• Implemented target position-based vehicle updating and kinematic models for realistic vehicle behaviors.
• Incorporated CAV uncertainties, including position error and communication delay, into the simulation.

Main Results:

• A simulation platform was developed and validated through comparisons of simulated and theoretical vehicle delays.
• Verified the effectiveness of uncertainty modeling mechanisms using a rear-end collision avoidance scenario.
• Successfully realized and verified a slot-based intersections (SIs) control strategy.

Conclusions:

• The proposed framework significantly enhances the realism and credibility of CAV kinematic simulations.
• The developed platform effectively supports the verification of CAVs and their control strategies.
• The methodology provides a reliable basis for advancing CAV testing and validation.