The transport sector is undergoing significant transformation, driven by the need for carbon neutrality and enhanced mobility. Connected and Autonomous Vehicles (CAVs) are key components in this transition, with road safety being the primary driver of their development. However, other benefits such as comfort and energy savings are also important. While much of the research on CAVs focuses on Information and Communication Technology (ICT), less attention is given to the overall vehicle system. This work aims to assess the efficiency improvements of CAVs and provide an overview of their architecture. It combines a literature survey with statistical methods to analyze the impact of CAV hardware on energy consumption. The study highlights that in 75% of scenarios, simulated light-duty CAVs worsen energy efficiency, while heavy-duty vehicles show more promising results. The research also discusses the data processing and management strategies used in CAVs, including the use of advanced sensors and computational units. The Monte Carlo simulation evaluates the effect of driving automation systems on vehicle energy consumption, considering optimal driving behavior and additional power consumption from CAV hardware. The findings suggest that the assumption that CAV technologies will reduce energy consumption compared to reference vehicles should be approached with caution.The transport sector is undergoing significant transformation, driven by the need for carbon neutrality and enhanced mobility. Connected and Autonomous Vehicles (CAVs) are key components in this transition, with road safety being the primary driver of their development. However, other benefits such as comfort and energy savings are also important. While much of the research on CAVs focuses on Information and Communication Technology (ICT), less attention is given to the overall vehicle system. This work aims to assess the efficiency improvements of CAVs and provide an overview of their architecture. It combines a literature survey with statistical methods to analyze the impact of CAV hardware on energy consumption. The study highlights that in 75% of scenarios, simulated light-duty CAVs worsen energy efficiency, while heavy-duty vehicles show more promising results. The research also discusses the data processing and management strategies used in CAVs, including the use of advanced sensors and computational units. The Monte Carlo simulation evaluates the effect of driving automation systems on vehicle energy consumption, considering optimal driving behavior and additional power consumption from CAV hardware. The findings suggest that the assumption that CAV technologies will reduce energy consumption compared to reference vehicles should be approached with caution.