As automated vehicles (AVs) become more common, it is important to understand how human-driven vehicles (HDVs) would interact with them. This research investigated HDV gap acceptance behavior in mixed traffic with AVs at a priority intersection, focusing on how mixed traffic factors affect this behavior and overall traffic efficiency. Using a driving simulator, four scenarios were tested by varying AV driving style (less defensive, more defensive, and HDV-like) and AV recognizability (distinguishable or not from HDVs). Gap acceptance models were estimated based on the collected trajectory data. These models were then integrated into the SUMO microscopic traffic simulation platform, where a T-intersection network was set up. Simulation runs varied based on AV driving style, recognizability, penetration rate (0-75% in 25% increments), and whether HDV behavioral adaptation was considered. The results indicated increased minor road vehicle delays with higher AV penetration rates. Recognizable less defensive AVs, and more defensive AVs with high penetration rates caused the largest delays for minor road vehicles compared to other conditions. Ignoring behavioral adaptation led to a delay underestimation of up to 75% for minor road vehicles. In conclusion, there is behavioral adaptation in gap acceptance of HDVs in mixed traffic environments. Taking into account the behavioral adaptation is essential for accurately assessing traffic efficiency in mixed traffic conditions, and guiding AV deployment policies.

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the road infrastructure requirements that would lead to safe operation of automated vehicles. In this context, a field test with Lane Departure Warning and Lane Keeping Systems-enabled vehicles was conducted in the province of North Holland, The Netherlands. The performance of these automated systems was evaluated using performance indicators such as Mean Lateral Position and Standard Deviation of Lane Position. In this study, the Systems Theoretic Accident Modelling and Processes (STAMP) model was adopted to understand the relationships between the various components of the “Road System”, which in this study include the road authority, the automated vehicle system, elements of the road infrastructure, and weather conditions. Empirical data from the experiment is used to estimate the relationships between the different components, followed by the assessment of their impact on the performance of the automated vehicles. It was found that visibility conditions have a significant effect on detection performance, which worsens in rainy conditions especially under streetlights. It has been also observed that there is a significant difference in Lane Position between Left Curves and Straight sections, and between lane widths less than 250 cms and those that have larger widths. These findings are combined with the results from the STAMP analysis to formulate a set of road infrastructure requirements that would lead to safe performance of Lane Assistance Systems.