###### Abstract  
The introduction of 5G technologies has enabled the possibility of designing and building several new classes of networked information systems that were previously impossible to implement due to limitations on data throughput or the reliability of transmission channels. Among them, one of the most interesting and successful examples with a highly positive impact in terms of the quality of urban environments and societal and economical welfare is a system of semi-autonomous connected vehicles, where IoT devices, data centers, and fleets of smart vehicles equipped with communication and computational resources are combined into a heterogeneous and distributed infrastructure, unifying hardware, networks, and software. In order to efficiently provide various services (e.g., patrolling, pickup and delivery, monitoring), 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. As is well documented in the literature, one of the most effective ways to achieve this purpose, especially in a real-time context, is to adopt a graph model of the data (e.g., to model communication networks, roads, or interactions between vehicles) and to employ suitable graph algorithms to solve properly defined computational problems of interest (e.g., shortest paths or distributed consensus). While research in this context has been extensive from a theoretical perspective, works that have focused on the implementation, deployment, and evaluation of the practical performance of graph algorithms for real-world systems of autonomous vehicles have been much rarer. In this paper, we present a study of this kind. Specifically, we first describe the main features of a real-world information system employing semi-autonomous connected vehicles that is currently being tested in the city of L'Aquila (Italy). Then, we present an overview of the 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. Finally, we discuss implementation issues, adopted software tools, and the deployment and testing phases concerning one of the algorithmic components of the mentioned real-world system dedicated to handling a specific problem of the above class, namely, the pickup and delivery multi-vehicle problem with time windows. applied algorithmics; autonomous vehicles; combinatorial optimization; algorithm engineering 20241428884142888414288414288414288414288414288414 systems that rely on effective, secure, and fault-tolerant data transmission. More specifically, such innovation has enabled the possibility of building entirely new classes of information systems and networked applications that were previously impossible to implement due to limitations in terms of data throughput or the reliability of communication channels [1]. Among the new types of systems that have experienced major diffusion, one of the most interesting and successful examples, due to the highly positive impact in terms of the quality of urban environments and societal and economical welfare, is a system of semi-autonomous connected vehicles. In a typical system of semi-autonomous connected vehicles (commonly associated with the broader class of _intelligent transport systems_), we have a set (also known as _fleet_) of _smart_ vehicles (or generically, _mobile entities_) that are equipped with high-end communication, sensing, and computational technologies and are deployed and can move/perform operations (with a certain, pre-specified degree of autonomy) in a given environment (e.g., the road network of a city).