Connected Autonomous Vehicles Ecosystem (CAVE) is fast becoming a reality. This paper reviews various architectures that leverage IoT technology to progressively create a system of interconnected Autonomous Vehicles (AV) that is effective, robust and secure. Accurate and fast perception of the external environment is key to movement of AVs in CAVE. IoT technology through sensors and internetworking can help an AV to do reliable perception. State of the art methods for perception of AVs in CAVE are reviewed. Self-awareness is another important aspect. IoT and Artificial Intelligence (AI) can come together to assist an AV in self-diagnosing anything abnormal. One such use case is presented for onboard diagnostics in an AV. Accurate AI is key to making effective strategies and to take control actions with error feedbacks that carefully maneuver an AV from its source to destination. The transition from legacy vehicles to CAVE requires AI methods that combat threats and have effective overall movement in mixed mode traffic systems (MMTS). Strategies for effective movement in MMTS are presented. HIGHLIGHTS Role of IoT technology is identified in different areas of Connected Autonomous Vehicle Ecosystem (CAVE) A comparison of different technologies in various domains of CAVE is presented and their pros and cons are discussed Use case for onboard diagnostics of an autonomous vehicle in CAVE is presented to emphasize the role of IoT Technology in [automated] preventive maintenance Key studies are reviewed that emphasize important issues like robust architectures for CAVE, threats in CAVE and reliable functioning in mixed mode traffic systems GRAPHICAL ABSTRACT