# Objectively Scoring the Human-Likeness of Artificial Driver Models
###### Abstract  
Several applications of artificially modeled drivers, such as autonomous vehicles (AVs) or surrounding traffic in driving simulations, aim to provide not only functional but also human-like behavior. The development of human-like AVs is expected to improve the interaction between AVs and humans in traffic, whereas, in a driving simulation, the objective is to create realistic replicas of real driving scenarios to investigate various research questions under safe and reproducible conditions. In urban traffic, driving behavior strongly depends on the situational context, which introduces new challenges not only for modeling but also for the evaluation of such models. However, current objective assessment strategies rarely consider situational context and human similarity, whereas subjective approaches are not suitable for iterative development processes. In this paper, we present a first attempt to make the plausibility and human-likeness of vehicles' trajectories objectively measurable. A multidimensional quality function is presented that incorporates various parameters characterizing human-like driving behavior and compares each of those parameters to human driving behavior under similar conditions. Among other things, our validation results show that the presented evaluation methodology is scalable to a wide range of situations has the ability to identify model weaknesses, and is able to reflect the way people distinguish between artificial and human behavior. traffic data; driver models; autonomous driving; driving simulation; urban; human factors +
Footnote †: journal: Medical  
and there is still a long development journey ahead. Therefore, meaningful evaluation strategies are important to determine the capabilities and limitations of developed models. In urban traffic, driving behavior is highly dependent on the situation, which introduces even more complexity to both modeling and evaluation. For the resulting model behavior, provided by a driver model, the planning or prediction module can be described by the spatiotemporal movement, the trajectory. However, current metrics for assessing the quality of trajectories rarely consider situational context and are often bound to specific ground truth (GT) data for comparison. Common evaluation strategies for trajectory prediction models, for example, usually rely on spatiotemporal distance measures to compare GT and artificially generated trajectories for quantifying model performance [6; 7]. Previously published research identified cases in which behavior deviates from the real trajectory but is still plausible [8]. In some cases, for example, the error value was large because the model chose a longer time gap than the individual human in a right-of-way situation. Both trajectories did not lead to a collision and were not critical. Thus, when comparing artificial trajectories to any individual human-driven trajectory, the result may show large error values but be still plausible, and vice versa. This can be remedied by evaluating a trajectory detached from individual behavior using a general metric that provides insight into how the artificial trajectory fits within the range of human behavior in similar situations. In summary, current research is intensively addressing the problem of developing driver models, planning, and prediction modules for complex urban situations, whereas the challenge of adequately evaluating the results is rarely considered. Therefore, this paper aims at creating a plausibility metric that can be applied to driver models and sub-modules by evaluating the human-likeness of trajectories while considering the situational context. In the following paper, human-likeness is understood as a significant similarity to the behavior exhibited by humans in comparable traffic situations, measured by different objective parameters. Driving behavior is assumed to be conditional and therefore dependent on various external influences [9]. This paper is organized as follows.