Advanced driving assistance and automation technologies promise to disrupt road transportation as it is known today. One of the first example of widely available Advanced Driver Assistance Systems (ADAS) is the Adaptive Cruise Control (ACC), which regulates the longitudinal vehicle dynamics requiring only the supervision of the driver. Unfortunately, the actual properties of these systems still remain almost unknown to the research community. Experimental campaigns involving these vehicles are indeed still limited by the resources they require and by the difficulties related to their setup. The available ones, although very important to shed light on the main features of commercial ACC systems, are usually limited in number of vehicles, traffic dynamics and geographical coverage they involve (see for example Milanes and Shaldover, 2014, Knoop et al. ,2019, Makridis et al., 2019). To provide the research community with a new tool to study the behavior of different ACC systems, the present study presents the data collected during a series of car-following experimental campaigns involving 12 commercial ACC equipped vehicle models of different brands tested in different conditions. The trajectory datasets have a sampling rate of 10Hz. Post-processing has been performed in order to filter or remove problematic sections such as tunnels, roundabouts and tolls. First experimental results confirm the previous findings about reaction time, time headway and string stability and provide inputs to researchers and policymakers on the potential impact of ACC-equipped vehicles on traffic dynamics when the penetration of these vehicles will increase in the near future. Additionally, they suggest several directions to consider in order to improve the performance of the ACC controllers, reduce potential downsides arising from ACC heterogeneity on public roads and maximize the benefits that such systems can bring to traffic flows. The database of vehicle trajectories used in this study is publicly available to the community to facilitate further research on the topic.

Recent research has demonstrated the potential benefits of connected, autonomous vehicles (CAVs) to the performance of urban networks. Specifically, several proposals have been made for policies and related technologies that either perform more efficiently when the proportion of CAVs is relatively high or that exclude human driven vehicles (HDVs) altogether. This same body of research has also identified several challenges faced by such networks, especially in the context of shared autonomous vehicles (SAVs). We propose a lane-use policy for networks of exclusively CAVs with the goal of preserving priority within any two-class, arbitrary priority assignment regime. We investigate the merits of such a policy by adopting a simple occupancy-based, two-class priority scheme in a network of SAVs. We will demonstrate that by granting and preserving priority for occupied vehicles, average travel times and speeds for passengers are improved with limited degradation in these measures for other, i.e. unoccupied, vehicles. The proposed lane-use policy is developed on realistic physical limitations of the street network and without the need for trajectory reservations.