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COMVEC™ conference is the only North American event that addresses vehicles and equipment spanning on-highway, off-highway, agricultural, construction, industrial, military, and mining sectors.

Abstract In the automotive domain, the overall complexity of technical components has increased enormously. Formerly isolated, purely mechanical cars are now a multitude of cyber-physical systems that are continuously interacting with other IT systems, for example, with the smartphone of their driver or the backend servers of the car manufacturer. This has huge security implications as demonstrated by several recent research papers that document attacks endangering the safety of the car. However, there is, to the best of our knowledge, no holistic overview or structured description of the complex automotive domain. Without such a big picture, distinct security research remains isolated and is lacking interconnections between the different subsystems. Hence, it is difficult to draw conclusions about the overall security of a car or to identify aspects that have not been sufficiently covered by security analyses.

Abstract Connected vehicles and intelligent transportation systems are currently evolving into highly interconnected digital environments. Due to the interconnectivity of different systems and complex communication flows, a joint risk analysis for combining safety and security from a system perspective does not yet exist. We introduce a novel method for joint risk assessment in the automotive sector as a combination of the Diamond Model, Failure Mode and Effects Analysis (FMEA), and Factor Analysis of Information Risk (FAIR). These methods have been sequentially composed, which results in a comprehensive risk management approach to information security in an intelligent transport system (ITS). The Diamond Model serves to identify and structurally describe threats and scenarios, the widely accepted FMEA provides threat analysis by identifying possible error combinations, and FAIR provides a quantitative estimation of probabilities for the frequency and magnitude of risk events.

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.

With the development of vehicle intelligence and the Internet of Vehicles, how to protect the safety of the vehicle network system has become a focus issue that needs to be solved urgently. The Controller Area Network (CAN) bus is currently a very widely used vehicle-mounted bus, and its security largely determines the degree of vehicle-mounted information security. The CAN bus lacks adequate protection mechanisms and is vulnerable to external attacks such as replay attacks, modifying attacks, and so on. On the basis of the existing work, this paper proposes an authentication method that combines Hash-based Message Authentication Code (HMAC)-SHA256 and Tiny Encryption Algorithm (TEA) algorithms. This method is based on dynamic identity authentication in challenge/response made and combined with the characteristics of the CAN bus itself as it achieves the identity authentication between the gateway and multiple electronic control units (ECUs).

Abstract In the pursuit of advancing autonomous vehicles (AVs), data-driven algorithms have become pivotal in replacing human perception and decision-making. While deep neural networks (DNNs) hold promise for perception tasks, the potential for catastrophic consequences due to algorithmic flaws is concerning. A well-known incident in 2016, involving a Tesla autopilot misidentifying a white truck as a cloud, underscores the risks and security vulnerabilities. In this article, we present a novel threat model and risk assessment (TARA) analysis on AV data storage, delving into potential threats and damage scenarios. Specifically, we focus on DNN parameter manipulation attacks, evaluating their impact on three distinct algorithms for traffic sign classification and lane assist.

Abstract Automotive software is increasingly complex and critical to safe vehicle operation, and related embedded systems must remain up to date to ensure long-term system performance. Update mechanisms and data modification tools introduce opportunities for malicious actors to compromise these cyber-physical systems, and for trusted actors to mistakenly install incompatible software versions. A distributed and stratified “black box” audit trail for automotive software and data provenance is proposed to assure users, service providers, and original equipment manufacturers (OEMs) of vehicular software integrity and reliability. The proposed black box architecture is both layered and diffuse, employing distributed hash tables (DHT), a parity system and a public blockchain to provide high resilience, assurance, scalability, and efficiency for automotive and other high-assurance systems.

In the “What’s Next for Aerospace and Defense: A Vision for 2050” study, AIA, New York City-based McKinsey & Company, and other industry partners reveal a comprehensive 30-year, Industry 4.0 forecast of air travel and spaceflight based on improvements in automation and digitization, next-generation materials, alternative energy sources and storage, and increased data throughput.

As technology and functionality of vehicle systems change, so do data recording needs. In ADS-dedicated vehicles (DV), the ADS perceives the environment and handles vehicle motion control, i.e., the dynamic driving task (DDT), as described in SAE J3016. When an ADS takes the place of a human driver, its sensing, processing, and control systems necessitate new considerations for data recording. Data recording is important to crash reconstruction, system performance investigations, and event analysis. It enables industry-wide improvements in ADS safety. This best practice makes recommendations for the ADS-DV data needed to support: (1) information about what the ADS "saw" and "did" and (2) identify the technology-relevant factors that contributed to the event.

An ADS-operated vehicle’s operational design domain (ODD) is defined by the manufacturer based on numerous factors. Research is underway at other organizations to define and organize ODD elements into taxonomies and other relational constructs. In order to enhance collaboration and communication between manufacturers and developers and transportation authorities, common terms and consistent frameworks are needed. The conceptual framework presented by Automated Vehicle Safety Consortium establishes a lexicon that can be used consistently by ADS developers and manufacturers responsible for defining their ADS ODD. A common framework and lexicon will reduce confusion, align expectations, and therefore build public trust, acceptance, and confidence.

AVSC Best Practice for Interactions Between ADS-DVs and Vulnerable Road Users (VRUs) AVSC00009202208 establishes common terminology and a baseline understanding of the challenges posed, and framework to evaluate automated driving system-dedicated vehicle (ADS-DV) interactions with VRUs. This best practice can facilitate communication among the industry and public, help calibrate expectations of all traffic participants, and improve broader acceptance of SAE level 4 and level 5 ADS-equipped vehicles.

AVSC Information Report for Change Risk Management AVSC00010202304 provides a process for change risk management for fleet-operated ADS-DVs using level 4 or 5 automation. The document addresses risks resulting from planned and unplanned changes in an ADS-DV design and/or operation. This information report is based on the concept of risk-informed decision-making. Making risk management decisions such as safety and change management, safety analysis, and safety assurance are especially applicable when moving from concept to production intent for the ADS-DV. Change Risk Management (CRM) does not replace best practices or other methods for managing safety anomalies or change management processes. It may instead be viewed as an additional resource that elaborates on how safety anomaly management and change management can be performed.

SAE EDGE Research Reports provide examinations significant topics facing mobility industry today including Connected Automated Vehicle Technologies Electrification Advanced Manufacturing

Abstract Secure boot is a fundamental security primitive for establishing trust in computer systems. For real-time safety applications, the time taken to perform the boot measurement conflicts with the need for near instant availability. To speed up the boot measurement while establishing an acceptable degree of trust, we propose a dual-phase secure boot algorithm that balances the strong requirement for data tamper detection with the strong requirement for real-time availability. A probabilistic boot measurement is executed in the first phase to allow the system to be quickly booted. This is followed by a full boot measurement to verify the first-phase results and generate the new sampled space for the next boot cycle. The dual-phase approach allows the system to be operational within a fraction of the time needed for a full boot measurement while producing a high detection probability of data tampering.

New for 2022, AeroTech® will deliver even more robust programming by teaming up with AeroMat to deliver learning opportunities dedicated to: Additive Manufacturing and Materials, Environment and Sustainable Aviation (Sustainability), Autonomy and AI, Safety and Human Factors, Modeling, Simulation and Testing, Cybersecurity / Cyber-Physical Security, Industry 4.0 Smart Manufacturing and Assembly, IDEAL Summit (inclusion, diversity, equity, accessibility and leadership), Advanced Air Mobility (AAM) and Multimodal Mobility (M3)

New for 2022, AeroTech® will deliver even more robust programming by teaming up with AeroMat to deliver learning opportunities dedicated to: Additive Manufacturing and Materials, Environment and Sustainable Aviation (Sustainability), Autonomy and AI, Safety and Human Factors, Modeling, Simulation and Testing, Cybersecurity / Cyber-Physical Security, Industry 4.0 Smart Manufacturing and Assembly, IDEAL Summit (inclusion, diversity, equity, accessibility and leadership), Advanced Air Mobility (AAM) and Multimodal Mobility (M3)

New for 2022, AeroTech® will deliver even more robust programming by teaming up with AeroMat to deliver learning opportunities dedicated to: Additive Manufacturing and Materials, Environment and Sustainable Aviation (Sustainability), Autonomy and AI, Safety and Human Factors, Modeling, Simulation and Testing, Cybersecurity / Cyber-Physical Security, Industry 4.0 Smart Manufacturing and Assembly, IDEAL Summit (inclusion, diversity, equity, accessibility and leadership), Advanced Air Mobility (AAM) and Multimodal Mobility (M3)