Safety assessment in future scenarios with Automated Vehicles

Nowadays, several Advanced Driving Assistance Systems (ADAS) are installed in vehicles, helping drivers with sevral tasks. Human drivers are evermore less involved in driving thanks to the technological help. According to the automation rate of the vehicles and the human involvment, vehicles can be considered partially or fully automated. The partially automated vehicles (AVs) belong to the SAE level 2-3 and follow a cautious behavior because they are still controlled for some tasks by human drivers. The fully automated ones belong to the SAE level 4-5, and their behavior is thought to be more aggressive since there is no need for human drivers to take over maneuver or manage some driving tasks. The reliability of technologies is considered greater than the ones of men for managing and reacting to any changes in traffic conditions, so the behavior is more assertive, headway between vehicles reduced, and greater acceleration and deceleration. Starting from these assumptions, in this thesis, three different vehicle typologies are studied, regular vehicles (RVs), Partially AVs (SAE level 2-3), and Fully AVs (SAE level 4-5) for crash assessments in future scenarios (short-term, mid-term, and long-term). This work aims at providing a methodological framework that can be used in every context and for every road type considering the introduction of technologies in traffic for safety assessments. This aspect is crucial since, practically speaking, plans for mobility and road design procedures require safety assessments projected in long temporal horizons. During this considered period there is the great chance that the vehicle types circulating on roads drastically change. Not considering new vehicles and their interactions with RVs in future scenarios can lead to misestimations of safety. The methodological framework was applied to a real-world case, in the context of the SUMP for the Province of Bari. The main results of this study highlight the importance of automation in traffic. Traffic made just of Fully AVs drastically decrease the crash frequency. Contrary, promiscuity of vehicles in traffic enhances the crash occurrence if compared to the current scenario. In order to foresee the impact of such changes in traffic, an ad hoc Safety Performance Function for AVs was developed, with the intent of predicting crashes in the future with AVs.