Common evaluation strategies can be categorized into objective and subjective approaches. In the area of AV development, most of the objective metrics rely on the direct comparison of modeled driving data to a unique single driving sample using any distance measure [10]. Common metrics for evaluating prediction or planning frameworks employ displacement errors, measured, for example, as the distance between the actual and predicted trajectories [7]. Such metrics indicate how accurately the predicted trajectory matches the individual, human-driven trajectory. However, in the case of larger displacement errors, no conclusions can be drawn as to whether the trajectory was still plausible and only the behavior deviated with regard to safety, for example, or whether the trajectory exhibited functional problems. Therefore, in some individual cases, more sophisticated evaluation strategies are applied, e.g., taking into account functional errors such as road violations [11] or unrealistic headways [12]. To quantify the similarity between driver models and human traffic behavior in driving simulation, macroscopic analyses are performed. With the help of endurance tests, synthetic data are generated and compared with real traffic data in typical highway scenarios, such as cut-in maneuvers [13]. Typical indicators to describe human behavior in related works are average and maximal velocity, frequency and exceeding of speeding [14], acceleration and headway [15,16], as well as Time-to-Collision (TTC) and longitudinal distance [17]. Based on such parameters, the relative validity of the macroscopic behavior of driver models can be determined [18,19]. Such methods compare observed macroscopic parameters of artificial vehicles with a distribution of respective parameters among real vehicles. For comparing distributions, statistical approaches such as Kolmogorov–Smirnov in the study conducted by Wang et al. [20], or Kullback–Leibler divergence as in research by Kuefler et al. [6] are applied.

However, most approaches focus on highway traffic and do not consider contextual influences, which in turn raises doubts about the applicability of such methods to more complex urban traffic since driving behavior is affected by various external influences [21]. Subjective approaches, on the other hand, measure human-likeness using questionnaires, interviews, or surveys that automatically consider behavior within an individual context. The underlying assumption of such methods is that either what is perceived as real or can not be distinguished from artificial behavior defines human-likeness. Y. Zhang et al., for example, adapted the Turing test and asked participants to classify the driving behavior of another vehicle into either artificial or human-driven [22]. Similarly, Dumbuya et al. asked subjects to rate how realistic they perceived a drive completed by different driver models and how likely it was that the drive was conducted by a real human driver [23]. Further research investigates the human-likeness of driver models and the extent to which the perceived realism of a VE is affected by the behavior [1,24]. Since subjective methods always require an experiment involving participants, such methods are not suitable for an iterative development process due to the effort associated with the evaluation.

The demand for mobility in urban environments is increasing due to trends such as urbanization and thus driver models for urban traffic are gaining in importance. Since meaningful evaluation strategies are the indispensable basis for future developments, current research aims to overcome the aforementioned challenges and addresses the question of how to quantify the human similarity of artificial models' behavior in urban environments.