Route Planning Algorithms for Fleets of Connected Vehicles: State of the Art, Implementation, and Deployment

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.

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

“…The literature on autonomous vehicles is very large and cannot be covered in this paper; therefore, we refer to the reviews proposed in [37][38][39][40], limiting our investigation mainly to the more recent works. A sociomobility taxonomy was proposed by Kassens-Noor et al [41], who reviewed and classified several papers accordingly.…”

“…This appendix contains tables showing some data about the test networks. 3 [4] 4 [3] 2.0 1600 50 9 [10] 3 [12] 12 [3] 5.0 5400 100 11 [12] 4 [5] 5 [4] 2.0 1600 50 13 [14] 4 [11] 11 [4] 5.0 1600 50 15 [16] 5 [6] 6 [5] 2.0 1600 50 17 [18] 5 [9] 9 [5] 2.5 3500 75 19 [20] 6 [8] 8 [6] 2.5 3500 75 21 [22] 7 [8] 8 [7] 3.0 3500 75 23 [24] 7 [18] 18 [7] 2.5 1600 50 25 [26] 8 [9] 9 [8] 2.0 3500 75 27 [28] 8 [16] 16 [8] 2.5 3500 75 29 [30] 9 [ [40] 11 [12] 12 [11] 2.0 3500 75 41 [42] 11 [14] 14 [11] 3.0 3500 75 43 [44] 12 [13] 13 [12] 6.0 5400 100 45 [46] 13 [24] 24 [13] 2.0 3500 75 47 [48] 14 …”