Connected and autonomous electric vehicles: Quality of experience survey and taxonomy

Abstract More than ever, the automotive industry is shifting towards electric vehicles since environmental and sustainability concerns are becoming important to potential customers. Nowadays, automakers are also integrating connectedness and autonomous components in their produced vehicles to reduce time of travel and increase the safety of the drivers, passengers, vehicles and the whole transportation system. The popularity of Connected and Autonomous Electric Vehicles (CAEVs) led to a growing interest in their development with careful focus on performance and quality aspects. In modern terms, Quality of Experience (QoE) covers important system, context, and human influencing factors that can drive improvements in the field. A rigorous survey of the literature revealed that QoE influencing factors and performance indicators are neither thoroughly identified, classified, nor modelled in an embracing framework that can be embedded in applications. In addition, QoE investigations are usually focused on specific CAEV subsystems and a broad addressing is practically non-existing. In this paper, the literature is explored for important performance aspects of CAEVs. Recent advances are critically appraised, challenges and gaps are identified, and improvements are carefully proposed. To this end, a thorough taxonomy is developed for QoE in CAEVs with a rich set of quality indicators and a framework that facilitates the integration of QoE concepts in system development. The presented contributions are expected to guide, enable, support, and accelerate future developments in the field.