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 2180

Special Issue Editors

Radiolabs Associated Laboratory, Università degli Studi dell'Aquilad, 67100 L’Aquila, Italy
Interests: heterogeneous networking; vehicular communications; 5G; software-defined networking
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

Manuscript Submission Information

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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

Published Papers (2 papers)

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Research

20 pages, 1987 KiB  
Article
A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
Appl. Sci. 2023, 13(24), 13024; https://doi.org/10.3390/app132413024 - 06 Dec 2023
Viewed by 575
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
Appl. Sci. 2023, 13(4), 2094; https://doi.org/10.3390/app13042094 - 06 Feb 2023
Viewed by 968
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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