<div class="section abstract"><div class="htmlview paragraph">The driving safety performance of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using metrics in order to be able to assess the driving safety performance and compare it to that of human-driven vehicles. In this research, driving safety performance metrics and methods for the measurement and analysis of said metrics are defined and/or developed.</div><div class="htmlview paragraph">A comprehensive literature review of metrics that have been proposed for measuring the driving safety performance of both human-driven vehicles and AVs was conducted. A list of proposed metrics, including novel contributions to the literature, that collectively, quantitatively describe the driving safety performance of an AV was then compiled, including proximal surrogate indicators, driving behaviors, and rules-of-the-road violations. These metrics, which include metrics from on- and off-board data sources, allow the driving safety performance of an AV to be measured in a variety of situations, including crashes, potential conflicts, and near misses. These measurements enable the evaluation of temporal flows and the quantification of key aspects of driving safety performance. The identification and exploration of metrics focusing explicitly on AVs as well as proposing a comprehensive set of metrics is a unique contribution to the literature. The objective is to develop a concise set of metrics that allow driving safety performance assessments to be effectively made and that align with the needs of both the ADS development and transportation engineering communities and accommodate differences in cultural/regional norms.</div><div class="htmlview paragraph">Concurrent project work includes equipping an intersection with a sensor suite of cameras, LIDAR, and RADAR to collect data requiring off-board sources and employing test AVs to collect data requiring on-board sources. Additional concurrent work includes development of artificial intelligence and computer vision-based algorithms to automatically calculate the metrics using the collected data. Future work includes using the collected data and algorithms to finalize the list of metrics and then develop a methodology that uses the metrics to provide an overall driving safety performance assessment score for an AV.</div></div>

This paper presents an emergency vehicle priority control system based on connected vehicle technology, called MMITSS priority. Traditional preemption does not consider the effect of the current traffic situation, such as the presence of a freight vehicle in the dilemma zone, on an opposing movement and can have a significant negative impact on the minor movements of vehicles. A mixed integer linear programming model is developed which can consider the priority requests from multiple emergency vehicles and dilemma zone requests from freight vehicles that could be trapped in the dilemma zone. The optimization model provides an optimal schedule that minimizes the total weighted priority request delays and dilemma zone request, as well as some flexibility to adapt to other vehicles in real time. The flexible implementation of the optimal signal timing schedule is designed to improve the mobility of the non-emergency vehicles. The approach has been tested and evaluated using microscopic traffic simulation. The simulation experiments show that the proposed priority control method is able to improve the travel time of the vehicles on the minor street while ensuring safe passage of the freight vehicle at the dilemma zone without significantly delaying the emergency vehicles. The method is implemented at the Maricopa County SMARTDrive ProgramSM test bed in Anthem, Arizona.

This paper presents a priority-based coordination system that provides preferential treatment to vehicles traveling along a coordinated route. A mixed-integer linear program model is enhanced to consider coordination as a form of priority along with the multi-modal priority for eligible emergency, transit, and freight connected vehicles and provides dilemma zone protection to freight vehicles in a connected vehicle environment. The optimization model generates an optimal signal timing schedule that minimizes the total weighted delay of the coordination requests, priority requests, and dilemma zone requests, and maximizes the flexible implementation of the optimal signal timing schedule. The optimal signal timing schedule allows real-time vehicle actuation using traditional vehicle detection. The simulation experiments and statistical analysis show that priority-based coordination can achieve performance equivalent to a traditional coordination system. The priority-based coordination method is integrated into the priority control Multi-modal Intelligent Traffic Signal System and is implemented in the Maricopa County SMARTDrive ProgramSM test bed in Anthem, Arizona, and in Portland, Oregon.

<div class="section abstract"><div class="htmlview paragraph">The operational safety of automated driving system (ADS)-equipped vehicles (AVs) needs to be quantified for an understanding of risk, requiring the measurement of parameters as they relate to AVs and human driven vehicles alike. In prior work by the Institute of Automated Mobility (IAM), operational safety metrics were introduced as part of an operational safety assessment (OSA) methodology that provide quantification of behavioral safety of AVs and human-driven vehicles as they interact with each other and other road users. To calculate OSA metrics, the data capture system must accurately and precisely determine position, velocity, acceleration, and geometrical relationships between various safety-critical traffic participants. The design of an infrastructure-based system that is intended to capture the data required for calculation of OSA metrics is addressed in this paper. The designed multi-modal sensor system includes a combination of traffic video cameras, vehicle-to-infrastructure (V2I) roadside units (RSUs), National Transportation Communications for Intelligent Transportation System Protocol (NTCIP)-compliant signal controllers streaming Signal Phase and Timing (SPAT) data, and Light Detection and Ranging (LIDAR) sensors. The system is contrasted with other design options to evaluate trade-offs between capability and cost. The designed data capture system was deployed at a SMARTDrive Program^SM Test Bed intersection in Anthem, AZ that has been developed by the University of Arizona Transportation Research Institute (TRI) in cooperation with the Maricopa County Department of Transportation (MCDOT). The intersection is equipped with a sensor system that includes a fiber optic data transfer backbone to support the data transfer to a server at the MCDOT Traffic Management Center. A measurement uncertainty (MU) analysis has been conducted using experimental data to better understand the performance and reliability of the proposed sensor system design. The data capture system will enable the development and validation of a methodology to continuously measure OSA metrics by gaining rich information through fusion of multimodal data collected from available sources for safety assessment of the transportation system that includes AVs.</div></div>