Driver models are essential for virtual safety assessments of automated vehicles. This study evaluates the realism of the i4Driving model, a behavioural driver model designed to emulate human-like driving, using an interactive driving Turing test combined with a think-aloud protocol in connected driving simulators. Thirty participants interacted with either a human-controlled vehicle or the i4Driving model across three motorway scenarios and rated realism, predictability, safety, and aggressiveness. Results showed that the i4Driving model was perceived as less realistic and predictable than human drivers (p < 0.001), yet participants could not reliably distinguish between the two (classification accuracy = 0.673). Think-aloud analysis revealed specific short-comings, such as unrealistic merging and speed adaptation, alongside instances of naturalistic behaviour. These findings highlight the need for improvements in tactical decision-making and demonstrate the value of combining subjective ratings with qualitative insights for refining driver models. ... Read more

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations. ... Read more