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The vehicle market at the European level (and also elsewhere) has registered a high dynamic for the adoption of electric vehicles as the future means of transport. Government policies and decisions fully support this move, but there are still massive barriers to entry into the EV market due to consumer attitudes and perceptions. Consumer attitudes and perceptions will be decisive in the market success of future electric vehicle models, given that consumers are particularly interested in the vehicles' technical and dynamic performance. The pressure from customers' demands for performance leaves its mark not only on the increase of performance and technical parameters, but directly contributes to the generation of interdependence between these parameters. This article presents a comparative statistical analysis of 203 electric vehicle models (from various construction categories), present in the European vehicle market (between the years 2019 and 2022), to highlight the direct and indirect links (interdependencies) between technical and performance parameters depending on the constructive class (type). The goal of this article is to determine whether there is a relationship between the three key performance metrics for electric vehicles—autonomy, top speed, and acceleration—and five significant technical/constructive metrics for these vehicles—battery capacity, energy efficiency, electric motor power, fast charging speed, and vehicle weight (brand and year of availability in the market). Based on the analyzed data, it can be stated that the design and construction of an electric vehicle model currently takes into account both performance and technical parameters, with a strong link between the weight of the vehicle and the energy capacity of the battery (+0.687) being highlighted and also the relationship between autonomy and vehicle weight (+0.355). The conclusions of this study can be used in the future by manufacturers for the development of new models of electric vehicles (new generic platforms and chassis) by classifying/standardizing these vehicles into specific classes, corresponding to the requirements of different classes of consumers or identifying constructive solutions specific to each type of consumer. [ABSTRACT FROM AUTHOR]

In recent years, Vehicle Make and Model Recognition (VMMR) has attracted a lot of attention as it plays a crucial role in Intelligent Transportation Systems (ITS). Accurate and efficient VMMR systems are required in real-world applications including intelligent surveillance and autonomous driving. The paper introduces a new large-scale dataset and a novel deep learning paradigm for VMMR. A new large-scale dataset dubbed Diverse large-scale VMM (DVMM) is proposed collecting image-samples with the most popular vehicle brands operating in Europe. A novel VMMR framework is proposed which follows a two-branch architecture performing make and model recognition respectively. A two-stage training procedure and a novel decision module are proposed to process the make and model predictions and compute the final model prediction. In addition, a novel metric based on the true positive rate is proposed to compare classification confusion of the proposed 2B–2S and the baseline methods. A complex experimental validation is carried out, demonstrating the generality, diversity, and practicality of the proposed DVMM dataset. The experimental results show that the proposed framework provides 93.95 % accuracy over the more diverse DVMM dataset and 95.85 % accuracy over traditional VMMR datasets. The proposed two-branch approach outperforms the conventional one-branch approach for VMMR over small-, medium-, and large-scale datasets by providing lower vehicle model confusion and reduced inter-make ambiguity. The paper demonstrates the advantages of the proposed two-branch VMMR paradigm in terms of robustness and lower confusion relative to single-branch designs. [ABSTRACT FROM AUTHOR]