The development of intelligent transportation systems (ITS) has made advanced and comprehensive simulation essential for the safe and efficient evaluation of connected and autonomous vehicles (CAVs). This paper addresses the crucial role of co-simulation in advancing ITS and CAV technologies, highlighting the limitations of single-platform simulations in capturing the benefits of integrated simulation environments. We explore and classify various simulators, based on their tasks within autonomous driving systems (ADS), and compare open-source and commercial simulators. Through a set of case studies, we demonstrate how co-simulation enhances the realism and comprehensiveness of testing frameworks, addressing the multifaceted challenges in ITS. Despite technical challenges such as fidelity, data handling, integration, and human factors, co-simulation provides a robust approach to developing innovative and safer urban mobility solutions. Future advances in artificial intelligence (AI), machine learning (ML), and quantum computing may further enhance co-simulation capabilities, thereby fostering safer and more efficient transportation simulations for academia, industry, and regulatory bodies.
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