Connected and Autonomous Vehicles (CAVs) are emerging as crucial components of future mobility systems, driven by goals such as improving air quality, mitigating climate change, and enhancing road safety. This article explores the architecture and potential of CAVs, focusing on their energy efficiency and the impact of driving automation systems (DAS). The study uses a combination of literature review and statistical methods to analyze CAVs, considering factors such as sensor data processing, energy consumption, and the influence of DAS on vehicle efficiency. The research highlights the importance of connectivity in improving CAV safety, performance, and reliability, while also addressing concerns related to cybersecurity. Advanced sensors, high computational power, and novel technologies are discussed as key enablers of energy efficiency and comfort in CAVs. However, the study also notes that the assumption that CAV technologies will reduce energy consumption compared to traditional vehicles should not be taken for granted, as in 75% of scenarios, simulated light-duty CAVs showed worse energy efficiency. The research employs a Monte Carlo simulation to evaluate the impact of DAS on energy consumption, considering the power requirements of CAV hardware. The results indicate that while CAVs have the potential to improve energy efficiency, especially for heavy-duty vehicles, the effectiveness depends on various factors, including the design of the CAV system and the environment in which they operate. The study also discusses the main components of CAV architecture, including sensors, perception, planning, and control systems, and highlights the importance of sensor fusion techniques in achieving reliable and accurate performance. The article concludes that the development of CAVs requires a multidisciplinary approach to address the challenges associated with their implementation and operation.Connected and Autonomous Vehicles (CAVs) are emerging as crucial components of future mobility systems, driven by goals such as improving air quality, mitigating climate change, and enhancing road safety. This article explores the architecture and potential of CAVs, focusing on their energy efficiency and the impact of driving automation systems (DAS). The study uses a combination of literature review and statistical methods to analyze CAVs, considering factors such as sensor data processing, energy consumption, and the influence of DAS on vehicle efficiency. The research highlights the importance of connectivity in improving CAV safety, performance, and reliability, while also addressing concerns related to cybersecurity. Advanced sensors, high computational power, and novel technologies are discussed as key enablers of energy efficiency and comfort in CAVs. However, the study also notes that the assumption that CAV technologies will reduce energy consumption compared to traditional vehicles should not be taken for granted, as in 75% of scenarios, simulated light-duty CAVs showed worse energy efficiency. The research employs a Monte Carlo simulation to evaluate the impact of DAS on energy consumption, considering the power requirements of CAV hardware. The results indicate that while CAVs have the potential to improve energy efficiency, especially for heavy-duty vehicles, the effectiveness depends on various factors, including the design of the CAV system and the environment in which they operate. The study also discusses the main components of CAV architecture, including sensors, perception, planning, and control systems, and highlights the importance of sensor fusion techniques in achieving reliable and accurate performance. The article concludes that the development of CAVs requires a multidisciplinary approach to address the challenges associated with their implementation and operation.