The interest with which academia, industry and societies anticipate a full scale launch of autonomous and connected vehicles, which is one of the most critical components underpinning the transformation of a modern city to a truly ‘smart’ one, is higher than ever before. This is because these vehicles have, in theory at least, the potential to completely transform urban development as known today, with a revolution in ground transport, regulations permitting, that could dramatically change the landscape of cities and have an enormous economic, social, spatial, and mobility impact. Artificial intelligence with its deep learning functions that model high-level data concepts through the use of architectures of multiple non-linear transformations is ultimately employed as a tool which empowers the car to make better decisions than a human driver ever could; but at the same time needs to be a user-centred technology that ‘understands’ and ‘satisfies’ the human user and the markets. Although recent studies showed that a priori acceptability of fully automated cars could be likely for many drivers today, the universal embracement of such a monumental mobility paradigm transition is still a complex proposition. This is because, despite a number of potential beneficial outcomes that make driverless vehicles an inescapable future reality, the implementation of vehicle automation, will not be straightforward, predictable or unproblematic; there is a wide spectrum of social dilemmas and complicated human factors issues that may arise from such an ‘untested’ and ‘powerful’ innovation. The present work develops an understanding of its key opportunities and challenges via the analysis of a series of semi-structured in-depth interviews with academic experts in transport and vehicle automation studies. The interviews were designed to test some of the myths referring to autonomous and connected vehicles with an emphasis on exploring some of the potentially darker or ambiguous sides of this smart mobility paradigm. Therefore the present study’s main output is the formation of a ‘road map’ or ‘theory-driven taxonomy’ of the possible key impacts (positive and negative) of car automation.