###### Abstract
Such vehicles are integrated and interact with IoT devices, computational units, data centers, satellites, and various sensing technologies to form a heterogeneous and distributed infrastructure, unifying hardware, networks, and software. The synergy between the fleet and the infrastructure forms a cohesive system designed to let vehicles perform complex tasks, often requiring coordination (e.g., pickup or delivery operations, search and rescue activities, traffic engineering [2; 3]), while achieving the superior efficiency, safety, and adaptability of transportation within diverse and dynamic environments. In order to achieve such levels of performance, these systems typically rely on collecting and broadcasting large amounts of data (e.g., sensor data, GPS traces, or maps), which need to be properly collected and processed in a timely manner in order to support tasks and to optimize various related services, such as route planning, task scheduling, or monitoring. Examples of studies that have considered such systems, as well as their design and performance analysis, are numerous and almost always of the multi-disciplinary type, involving computer scientists/engineers and experts in the telecommunications, materials science, electronics, and automotive fields (see, e.g., [4; 5]). In this direction, as is well documented in the literature, one of the most effective and widely used strategies, especially in a real-time context, to efficiently collect, store, and process data for decision-support and optimization purposes is to consider a graph model of the data and to employ suitable graph algorithms to solve properly defined computational problems of interest. Successful applications of graph-based modeling and graph algorithms for supporting the provision of services in modern information systems are various and span diverse domains, from navigation systems, where graphs are used to model and represent road networks and shortest-path algorithms are employed to determine the best routes [6; 7], to airline management systems, where graphs are used to model flights connecting airports and maximum-flow algorithms are used to schedule departures and arrivals [8], up to distributed systems of entities, where graphs are used to model interactions between entities and coloring algorithms are exploited to achieve various forms of consensus or coordination [9; 10]. Thus, on the one hand, research in this context has been extensive from a theoretical perspective in both computer science and engineering, mostly due to the plethora of domains where graph-based modeling and processing find applications [11; 12; 13]. On the other hand, works that have focused on the implementation, deployment, and evaluation of the practical performance of graph algorithms for real-world systems and specifically for systems of autonomous vehicles have been much rarer. In this paper, we contribute to filling this gap in the literature in the following ways: (i) We describe the main features of a real-world information system that employs semi-autonomous connected vehicles; this system was designed and deployed and is currently under test in the city of L'Aquila (Italy) within the broader EMERGE initiative, a smart mobility and communications project to develop technologies for autonomous and assisted driving systems to support both \"everyday\" and \"emergency\" operations. (ii) We present an overview of computational challenges arising in the considered application domain and provide a systematic survey of known algorithmic results for one of the most relevant classes of computational problems that have to be addressed in said domain, namely, _pickup and delivery problems_. (iii) We showcase the main phases of the implementation, deployment, and testing of one of the algorithmic components of the mentioned real-world system. This paper is organized as follows.