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Organized in cooperation with SAE International, AVL’s International Commercial Powertrain Conference- ICPC, happens every two years. It is the premier forum for truck, agricultural and construction equipment manufacturers to discuss powertrain technology challenges and solutions across their industries. This event offers a unique opportunity for engineers to address the synergy effects and distinctive characteristics of commercial vehicles, agricultural tractors and non-road vehicles, and industrial machinery. In 2017, the 9th ICPC focused on alternative powertrain technologies and innovations improving operating efficiency. These proceedings focus on: • Future challenges for engines and emissions • Smart Technologies Changing Farming • Cyber Physical Systems in Agriculture Business • OEM View of the Future of the Construction Machinery Industry • Powertrain Developments • CO2 Reduction • CVT Transmission Platform Technology • Autonomous and Connected Trucks

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.

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.

As a promising strategic industry group that is rapidly evolving around the world, autonomous vehicle is entering a critical phase of commercialization from demonstration to end markets. The global automotive industry and governments are facing new common topics and challenges brought by autonomous vehicle, such as how to test, assess, and administrate the autonomous vehicle to ensure their safe running in real traffic situations and proper interactions with other road users. Starting from the facts that the way to autonomous driving is the process of a robot or a machine taking over driving tasks from a human. This paper summarizes the main characteristics of autonomous vehicle which are different from traditional one, then demonstrates the limitations of the existing certification mechanism and related testing methods when applied to autonomous vehicle.

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.

ARINC Report 679 defines the functional characteristics of an airborne server that will support Electronic Flight Bags (EFBs) and similar peripherals used in the flight deck, cabin, and maintenance applications. The document defines how EFBs will efficiently, effectively, safely, and securely connect to the airborne server in a way that offer expanded capabilities to aircraft operators. The airborne server has two main functions, first to provide specific services to connected systems, and second to provide centralized security for the EFB and its data. This document is a functional airborne server definition. It does not define the physical characteristics of the server.

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.

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.

Abaco Systems Inc. is launching a new family of avionics devices for test and simulation, development, and dataloading that feature Thunderbolt 3 interfaces. The new portable, high-speed, low-latency avionics devices – RCEI-830A-TB and QPM-1553-TB – are designed for a broad range of avionics applications and include Thunderbolt 3-to-PMC/XMC interfacing with ARINC 429 and MIL-STD-1553 protocols.

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)

Breathing Life into Artificial Intelligence and Next Generation Autonomous Aerospace Systems Robotic Rotational Molding Creates New Opportunities for Military and Aerospace Applications Rim-Driven Electric Aircraft Propulsion High-Speed Midwave Infrared Cameras Enable Military Test Range Tracking System What Today's Advances in Radar Technology Mean for Testing and Training Tackling Ruggedization Challenges for RF Communications in Software Defined Radios AUVSI XPONENTIAL 2023 The Blueprint for Autonomy Multi-Scale Structuring of the Polar Ionosphere Understanding a radically new sensing capability for polar ionospheric science introduced by observational evidence recently provided by the electronically steerable Resolute Bay Incoherent Scatter Radar (RISR). Stepped-Frequency Distributed Radar for Through-the-Wall Sensing A technical analysis of the effectiveness of distributed radar for through-the-wall sensing applications.