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Advanced Technologies in Automated Driving
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Advanced Technologies in Automated Driving

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4754

Special Issue Editors

Dr. Marco Pratesi

E-Mail Website
Guest Editor
Radiolabs Associated Laboratory, Università degli Studi dell'Aquilad, 67100 L’Aquila, Italy
Interests: heterogeneous networking; vehicular communications; 5G; software-defined networking
Prof. Dr. Alessandro Neri

E-Mail Website
Guest Editor
Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, 00154 Roma, Italy
Interests: information theory; signal theory; signal and image processing and their applications to both telecommunications systems and navigation and remote sensing

Special Issue Information

Dear Colleagues,

Vehicles of the future will continuously communicate within a heterogeneous ecosystem, where information will be shared and processed through deterministic algorithms and artificial intelligence, to improve safety and efficiency of mobility. Multiple connectivity options, including satellite, will provide pervasive, ubiquitous, fault-tolerant, bearer-independent, network coverage. Vehicles and their passengers will use a set of different services (including entertainment) that will extend and evolve along the path toward autonomous driving, which is state-of-the-art for rails and a fascinating target for roads. Automated driving is a hot topic for researchers and a wide range of stakeholders, with many different fields of application and use cases. Enabling factors for challenging applications include reliable and accurate positioning and prioritized delivery of time-critical relevant messages in the resource-limited context of wireless communications, where network slicing and cooperative congestion control algorithms have to provide efficient radio resources management, even with a high density of vehicles. Quick and cost-effective testing and validation of systems will be facilitated by simulators and hardware/software-in-the-loop setups, and by network architectures that foster cooperation and reuse of facilities of multiple stakeholders.

We would like to encourage our colleagues to prepare original manuscripts to disseminate information about research results, ongoing projects, and new technological testbeds and achievements about cooperative and automated driving, not limited to roads, but also including rails and others.

Dr. Marco Pratesi
Prof. Dr. Alessandro Neri
Guest Editors

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Keywords

  • wireless communications
  • DSRC/ITS-G5/C-V2X
  • heterogeneous networks
  • C-ITS and smart mobility
  • machine learning
  • accurate positioning
  • simulation
  • virtual testing and validation
  • hardware- and software-in-the-loop (HIL/SIL)
  • autonomous driving
  • cooperative driving

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Published Papers (4 papers)

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Research

30 pages, 27742 KiB  
Article
OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
by Angel Luis Zuriarrain Sosa, Valeria Ioannucci, Marco Pratesi, Roberto Alesii, Carlo Albanese, Francesco Valentini, Elena Cinque, Alessio Martinelli and Michele Brizzi
Appl. Sci. 2024, 14(11), 4401; https://doi.org/10.3390/app14114401 - 22 May 2024
Viewed by 653
Abstract
With the development of advanced driver assistance systems (ADAS) and autonomous vehicles (AV), recent years have seen an increasing evolution of onboard sensors and communication interfaces capable of interacting with available infrastructures, including satellite constellations, road structures, modern and heterogeneous network systems (e.g., [...] Read more.
With the development of advanced driver assistance systems (ADAS) and autonomous vehicles (AV), recent years have seen an increasing evolution of onboard sensors and communication interfaces capable of interacting with available infrastructures, including satellite constellations, road structures, modern and heterogeneous network systems (e.g., 5G and beyond) and even adjacent vehicles. Consequently, it is essential to develop architectures that cover data fusion (multi–sensor approach), communication, power management, and system monitoring to ensure accurate and reliable perception in several navigation scenarios. Motivated by the EMERGE project, this paper describes the definition and implementation of an On Board Unit (OBU) dedicated to the navigation process. The OBU is equipped with the Xsens MTi–630 AHRS inertial sensor, a multi–constellation/multi–frequency Global Navigation Satellite System (GNSS) receiver with the u–blox ZED–F9P module and communication interfaces that afford access to the PointPerfect augmentation service. Experimental results show that GNSS, with corrections provided by augmentation, affords centimetre accuracy, with a Time To First Fix (TTFF) of about 30 s. During the on–road tests, we also collect: the output of fusion with inertial sensor data, monitoring information that assess correct operation of the module, and the OBU power consumption, that remains under 5 W even in high–power operating mode. Full article
(This article belongs to the Special Issue Advanced Technologies in Automated Driving)
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13 pages, 1262 KiB  
Article
Route Planning Algorithms for Fleets of Connected Vehicles: State of the Art, Implementation, and Deployment
by Mattia D’Emidio, Esmaeil Delfaraz, Gabriele Di Stefano, Giannantonio Frittella and Edgardo Vittoria
Appl. Sci. 2024, 14(7), 2884; https://doi.org/10.3390/app14072884 - 29 Mar 2024
Cited by 2 | Viewed by 825
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advanced Technologies in Automated Driving)
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20 pages, 1987 KiB  
Article
A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
by Walter Tiberti, Roberto Civino, Norberto Gavioli, Marco Pugliese and Fortunato Santucci
Appl. Sci. 2023, 13(24), 13024; https://doi.org/10.3390/app132413024 - 6 Dec 2023
Cited by 2 | Viewed by 1092
Abstract
While technological advancements and their deep integration in connected and automated vehicles is a central aspect in the evolving trend of automotive industry, they also depict a growing size attack surface for malicious actors: the latter ones typically aim at exploiting known and [...] Read more.
While technological advancements and their deep integration in connected and automated vehicles is a central aspect in the evolving trend of automotive industry, they also depict a growing size attack surface for malicious actors: the latter ones typically aim at exploiting known and unknown security vulnerabilities, with potentially disastrous consequences on the safety of vehicles, people, and infrastructures. In recent years, remarkable efforts have been spent to mitigate security vulnerabilities in intelligent and connected vehicles, in particular in the inside of vehicles, the so-called intra-vehicle networks. Despite those efforts, securing intra-vehicle networks remains a non-trivial task due to their heterogeneous and increasingly complex context. Starting from the above remarks and motivated by the industrial research and innovation project EMERGE, in this paper we report on a novel cryptographic hardware-software solution that we have designed and developed for securing the intra-vehicle network of intelligent connected vehicles: the Crypto-Engine. The Crypto-Engine relies on a lightweight hybrid-key cryptographic scheme to provide confidentiality and authentication without compromising the normal communication performance. We tested the Crypto-Engine and demonstrated that, once configured according to application-defined performance requirements, it can authenticate parties and secure the communications with a negligible overhead. Full article
(This article belongs to the Special Issue Advanced Technologies in Automated Driving)
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18 pages, 5396 KiB  
Article
Decision-Making Model of Autonomous Driving at Intersection Based on Unified Driving Operational Risk Field
by Ziming Lu, Weiwei Zhang and Bo Zhao
Appl. Sci. 2023, 13(4), 2094; https://doi.org/10.3390/app13042094 - 6 Feb 2023
Viewed by 1363
Abstract
Safety and comfort are the two major requirements for the successful implementation of self-driving cars, which are anticipated to constitute the future generation of transportation. To create safe and effective self-driving car trajectories, a novel behavioral decision model is developed. First, a risk [...] Read more.
Safety and comfort are the two major requirements for the successful implementation of self-driving cars, which are anticipated to constitute the future generation of transportation. To create safe and effective self-driving car trajectories, a novel behavioral decision model is developed. First, a risk field model for driving activities based on vehicle kinematics and Eulerian solenoids is constructed. From there, the principle of least action is applied to produce the best trajectory points. Finally, nine typical unit scenarios are simulated by matlab’s driving scenario designer to verify the feasibility of the decision-making algorithm. This study illustrates how an unified operational risk field can efficiently increase intersection passing efficiency while ensuring safety, utilizing the principle of least action. The experimental results show that in the scenario of unprotected left turn and more than 5 vehicles in the intersection, the decision-making model improves the pass rate by 23% compared with the TTI (Time To Intersection) threshold method. Full article
(This article belongs to the Special Issue Advanced Technologies in Automated Driving)
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