Digital Twins for Dependability Improvement of Autonomous Driving

Veledar, Omar; Damjanovic-Behrendt, Violeta; Macher, Georg

doi:10.1007/978-3-030-28005-5_32

Cited by 33 publications

(33 citation statements)

References 11 publications

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“…Challenges: DT have emerged as a valuable paradigm for online predictive analytics and prognostics in TAS, e.g., for optimizing safety and security in self-driving vehicles and autonomous robots, as shown in references [83], [84], [85]. These approaches are mainly powered by AI and ML [86] to create predictive models for run-time analysis and proactive response, in order to automatically anticipate and respond to errors and failures, possibly including safety hazards.…”

Section: E Digital Twins For Tasmentioning

confidence: 99%

Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives

Flammini

Alcaraz

Bellini

et al. 2024

IEEE Trans. Emerg. Topics Comput.

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The class of Trustworthy Autonomous Systems (TAS) includes cyber-physical systems leveraging on self-x technologies that make them capable to learn, adapt to changes, and reason under uncertainties in possibly critical applications and evolving environments. In the last decade, there has been a growing interest in enabling artificial intelligence technologies, such as advanced machine learning, new threats, such as adversarial attacks, and certification challenges, due to the lack of sufficient explainability. However, in order to be trustworthy, those systems also need to be dependable, secure, and resilient according to well-established taxonomies, methodologies, and tools. Therefore, several aspects need to be addressed for TAS, ranging from proper taxonomic classification to the identification of research opportunities and challenges. Given such a context, in this paper address relevant taxonomies and research perspectives in the field of TAS. We start from basic definitions and move towards future perspectives, regulations, and emerging technologies supporting development and operation of TAS.

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Section: E Digital Twins For Tasmentioning

confidence: 99%

Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives

Flammini

Alcaraz

Bellini

et al. 2024

IEEE Trans. Emerg. Topics Comput.

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show abstract

“…An advanced driver assistance system featuring lane change prediction was realized by adopting both camera images and DT based auxiliary information from the cloud. With the help of virtualization through DTs [185] , implementation methods were designed reducing for reducing auto-driving security vulnerabilities [186,187] . DT-based penetration tests in cyber-physical systems have the potential to identify and fix vehicle vulnerabilities [188][189][190] .…”

Section: Transportation Applicationsmentioning

confidence: 99%

Shaping future low-carbon energy and transportation systems: Digital technologies and applications

Song

Wang

et al. 2022

iEnergy

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Digitalization and decarbonization are projected to be two major trends in the coming decades. As the already widespread process of digitalization continues to progress, especially in energy and transportation systems, massive data will be produced, and how these data could support and promote decarbonization has become a pressing concern. This paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure, common mechanisms and algorithms, and system-level impacts, as well as the application of digital technologies to coupled energy and transportation systems with electric vehicles. This paper also identifies corresponding challenges and future research directions, such as in the field of blockchain, digital twin, vehicle-to-grid, low-carbon computing, and data security and privacy, especially in the context of integrated energy and transportation systems.

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“…Cybersecurity in automotive has become a major issue because modern cars have gateways, vehicle to infrastructure communication, WLAN communication, busses, domain controllers, a large number of electronic control units, and millions lines of software. 25 And users expect a high level of safety [26][27][28][29] and security. [30][31][32][33][34][35][36][37] More functions are added constantly and the level of autonomous drive [38][39][40][41][42] is increasing.…”

Section: F I G U R E 1 Added Processes For Automotive Spice For Cyber...mentioning

confidence: 99%

Experiences with the automotive SPICE for cybersecurity assessment model and tools

Messnarz¹,

Ekert²,

Macher

et al. 2022

J Software Evolu Process

Self Cite

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In August 2021 the ISO 21434:2021 standard for Road vehicles—Cybersecurity Engineering has been published. At the same time the blue book from VDA (Verein der Deutschen Automobilgesellschaft; German Automotive Association) for Automotive SPICE cybersecurity assessments has been released. In addition in the period September–December 2021 the training material for iNTACS (INTernational Assessor Certification Schema) certified Automotive SPICE for cybersecurity assessors has been developed. Since February 2022 the upgrade training of assessors worldwide has started. Beside the ASPICE (Automotive SPICE) for cybersecurity blue book also a red book from VDA has been published. The red book describes the questions to check in an ACSMS (Automotive CyberSecurity Management System) audit. This paper explains the main strategy and content for ASPICE for Cybersecurity assessments and how such assessments are integrated to the overall ACSMS strategy. Also, the paper outlines an example method and tool used in ASPICE for cybersecurity assessments and how such assessment results will look like.

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Digital Twins for Dependability Improvement of Autonomous Driving

Cited by 33 publications

References 11 publications

Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives

Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives

Shaping future low-carbon energy and transportation systems: Digital technologies and applications

Experiences with the automotive SPICE for cybersecurity assessment model and tools

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