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Automated driving system (ADS) manufacturers, developers, and operators need to provide clear information on their safety approach to relevant stakeholders. Explainability to diverse audiences helps build trust in statements from these organizations towards the shared value of safety. A defined list of core safety topics can help set expectations when communicating deployment and use-case-specific automated vehicle (AV) safety information. The topics listed in this best practice are implementation-agnostic and broadly applicable. This best practice describes how safety is continuous and connected throughout lifecycle stages and highlights considerations when including safety metrics as part of the communicated information. It lists topics that are considered core, provides a rationale, illustrative examples where applicable, suggestions of content that could be included for the example, and lists references and industry examples for further information.

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This chapter delves into the field of multi-agent collaborative perception (MCP) for autonomous driving: an area that remains unresolved. Current single-agent perception systems suffer from limitations, such as occlusion and sparse sensor observation at a far distance. To address this, three unsettled topics have been identified that demand immediate attention. First, it is crucial to establish normative communication protocols to facilitate seamless information sharing among vehicles. Second, collaboration strategies need to be defined, including identifying the need for specific collaboration projects, determining the collaboration partners, defining the content of collaboration, and establishing the integration mechanism. Finally, collecting sufficient data for MCP model training is vital. This includes capturing diverse modal data and labeling various downstream tasks as accurately as possible.

And as much as there are technical questions regarding network latency, bandwidth, cybersecurity, etc., aspects like human workload, attentiveness, and situational awareness also need to be clarified.

And as much as there are technical questions regarding network latency, bandwidth, cybersecurity, etc., aspects like human workload, attentiveness, and situational awareness also need to be clarified.

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On the other hand, the potential risks associated with CAV deployment related to technical vulnerabilities are safety and cybersecurity issues that may arise from flawed hardware and software. Cybersecurity and Digital Trust Issues in Connected and Automated Vehicles elaborates on these topics as unsettled cybersecurity and digital trust issues in CAVs and follows with recommendations to fill in the gaps in this evolving field. ...Cybersecurity and Digital Trust Issues in Connected and Automated Vehicles elaborates on these topics as unsettled cybersecurity and digital trust issues in CAVs and follows with recommendations to fill in the gaps in this evolving field. ...This report also highlights the importance of establishing robust cybersecurity protocols and fostering digital trust in these vehicles to ensure safe and secure deployment in our modern transportation system.

From hydrogen-fueled buses to AI-driven advancements and cybersecurity, this book offers an unparalleled glimpse into the future of transit. Navigate the complexities of transit planning in a post-COVID world, where innovative solutions are essential to tackle infrastructure challenges and workforce shortages.

The development of highly automated driving functions (AD) recently rises the demand for so called Fail-Operational systems for native driving functions like steering and braking of vehicles. Fail-Operational systems shall guarantee the availability of driving functions even in presence of failures. This can also mean a degradation of system performance or limiting a system’s remaining operating period. In either case, the goal is independency from a human driver as a permanently situation-aware safety fallback solution to provide a certain level of autonomy. In parallel, the connectivity of modern vehicles is increasing rapidly and especially in vehicles with highly automated functions, there is a high demand for connected functions, Infotainment (web conference, Internet, Shopping) and Entertainment (Streaming, Gaming) to entertain the passengers, who should no longer occupied with driving tasks.

The VCE Laboratory testbeds are connected with an Amazon Web Services (AWS) cloud-based Cyber-security Labs as a Service (CLaaS) system, which allows students and researchers to access the testbeds from any place that has a secure internet connection. ...VCE students are assigned predefined virtual machines to perform designated cyber-security experiments. The CLaaS system has low administrative overhead associated with experiment setup and management. ...VCE Laboratory CLaaS experiments have been developed for demonstrating man-in-the-middle cyber-security attacks from actual compromised hardware or software connected with the TestCube.

Symbolic code execution is a powerful cybersecurity testing approach that facilitates the systematic exploration of all paths within a program to uncover previously unknown cybersecurity vulnerabilities. ...Symbolic code execution is a powerful cybersecurity testing approach that facilitates the systematic exploration of all paths within a program to uncover previously unknown cybersecurity vulnerabilities. This is achieved through a Satisfiability Modulo Theory (SMT) solver, which operates on symbolic values for program inputs instead of using their concrete counterparts.

A significant milestone in advancing cybersecurity within the automotive industry is the release of the first international standard for automotive cybersecurity ISO/SAE 21434:2021 ‘Road Vehicles — Cybersecurity Engineering’. A recently published type approval regulation for automotive cybersecurity (UN R155) is also tailored for member countries of the UNECE WP.29 alliance. ...Thus, the challenges for embedded automotive systems engineers are increasing while frameworks, tools and shared concepts for cybersecurity engineering and training are scarce. Hence, cybersecurity training in the automotive domain necessitates an understanding of domain-specific intricacies and the unique challenges at the intersection of cybersecurity and embedded systems engineering, elevating the need for improving the skill set and knowledge of automotive cybersecurity engineers. ...Hence, cybersecurity training in the automotive domain necessitates an understanding of domain-specific intricacies and the unique challenges at the intersection of cybersecurity and embedded systems engineering, elevating the need for improving the skill set and knowledge of automotive cybersecurity engineers. This paper delves into an automotive cybersecurity training concept aimed at enhancing the proficiency of development engineers.

Since the early 1990’s, commercial vehicles have suffered from repeated vulnerability exploitations that resulted in a need for improved automotive cybersecurity. This paper outlines the strategies and challenges of implementing an automotive Zero Trust Architecture (ZTA) to secure intra-vehicle networks. ...This research successfully met the four requirements and demonstrated that using ZT principles in an on-vehicle network greatly improved the cybersecurity posture with manageable impact to system performance and deployment.

This paper gives a definition of the SDV concept, provides views from different aspects, discusses the progress in vehicle E/E architecture, especially zone-based architecture with centralized computation, and various technologies including High-Performance Computing (HPC) platform, standardized vehicle software architecture, advanced onboard communication, Over-The-Air (OTA) update, and cybersecurity etc. that collectively enable the realization of SDV.

This method's detailed approach ensures that cybersecurity requirements can be readily implemented as a part of feature design, addressing the concerns of feature owners directly. ...This paper will discuss the merits of asset based approach to cybersecurity over attack based recognizing the inherent strengths and limitations of both methods and underscores the need for a unified approach.

Thus, assessments of cybersecurity, maintenance, interactions with passengers, etc., while important, are out of scope for this document.

The aircraft lifecycle involves thousands of transactions and an enormous amount of data being exchanged across the stakeholders in the aircraft ecosystem. This data pertains to various aircraft life cycle stages such as design, manufacturing, certification, operations, maintenance, and disposal of the aircraft. All participants in the aerospace ecosystem want to leverage the data to deliver insight and add value to their customers through existing and new services while protecting their own intellectual property. The exchange of data between stakeholders in the ecosystem is involved and growing exponentially. This necessitates the need for standards on data interoperability to support efficient maintenance, logistics, operations, and design improvements for both commercial and military aircraft ecosystems. A digital thread defines an approach and a system which connects the data flows and represents a holistic view of an asset data across its lifecycle.

Abstract Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time.