Easing the Reuse of ML Solutions by Interactive Clustering-based Autotuning in Scientific Applications

Software is disrupting one industry after another. Currently, the automotive industry is under pressure to innovate in the area of software. New, innovative approaches to vehicles and their HW/SW architectures are required and are currently subsumed under the term “SW-defined vehicle”. However, this trend does not stop at the vehicle boundaries, but also includes communication with off-board edge and cloud services. Thinking it through further, this leads to a breakthrough technology we call “Reliable Distributed Systems”, which enables the operation of vehicles where time and safety-critical sensing and computing tasks are no longer tied to the vehicle, but can be shifted into an edge-cloud continuum. This allows a variety of novel applications and functional improvements but also has a tremendous impact on automotive HW/SW architectures and the value chain. Reliable distributed systems are not limited to automotive use cases. The ubiquitous and reliable availability of distributed computing and sensing in real-time enable novel applications and system architectures in a variety of domains: from industrial automation over building automation to consumer robotics. However, designing reliable distributed systems raises several issues and poses new challenges for edge and cloud computing stacks as well as electronic design automation

The integration of on-line monitoring and reconfiguration functions using IEEE1149.4 into a safety critical automotive electronic control unit.

This paper presents an innovative application of IEEE 1149.4 and the integrated diagnostic reconfiguration (IDR) as tools for the implementation of an embedded test solution for an automotive electronic control unit, implemented as a fully integrated mixed signal system. The paper describes how the test architecture can be used for fault avoidance with results from a hardware prototype presented. The paper concludes that fault avoidance can be integrated into mixed signal electronic systems to handle key failure modes

Automotive automation: Investigating the impact on drivers' mental workload

Recent advances in technology have meant that an increasing number of vehicle driving tasks are becoming automated. Such automation poses new problems for the ergonomist. Of particular concern in this paper are the twofold effects of automation on mental workload - novel technologies could increase attentional demand and workload, alternatively one could argue that fewer driving tasks will lead to the problem of reduced attentional demand and driver underload. A brief review of previous research is presented, followed by an overview of current research taking place in the Southampton Driving Simulator. Early results suggest that automation does reduce workload, and that underload is indeed a problem, with a significant proportion of drivers unable to effectively reclaim control of the vehicle in an automation failure scenario. Ultimately, this research and a subsequent program of studies will be interpreted within the framework of a recently proposed theory of action, with a view to maximizing both theoretical and applied benefits of this domain

User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle

Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date, little is known about driver expectations of partial driving automation and whether this affects the information they require inside the vehicle. Twenty-five participants were presented with five partially automated driving events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data was coded and analysed using grounded theory. From the results, two groupings of driver expectations were identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two groups the information preferences differed. LIP drivers did not want detailed information about the vehicle presented to them, but the definition of partial automation means that this kind of information is required for safe use. Hence, the results suggest careful thought as to how information is presented to them is required in order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the system's status and driving and were found to be more willing to work with the partial automation and its current limitations. It was evident that the drivers' expectations of the partial automation capability differed, and this affected their information preferences. Hence this study suggests that HMI designers must account for these differing expectations and preferences to create a safe, usable system that works for everyone. [Abstract copyright: Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

Assisted assignment of automotive safety requirements

ISO 26262, a functional-safety standard, uses Automotive Safety Integrity Levels (ASILs) to assign safety requirements to automotive-system elements. System designers initially assign ASILs to system-level hazards and then allocate them to elements of the refined system architecture. Through ASIL decomposition, designers can divide a function & rsquo;s safety requirements among multiple components. However, in practice, manual ASIL decomposition is difficult and produces varying results. To overcome this problem, a new tool automates ASIL allocation and decomposition. It supports the system and software engineering life cycle by enabling users to efficiently allocate safety requirements regarding systematic failures in the design of critical embedded computer systems. The tool is applicable to industries with a similar concept of safety integrity levels. © 1984-2012 IEEE

Safety-related challenges and opportunities for GPUs in the automotive domain

GPUs have been shown to cover the computing performance needs of autonomous driving (AD) systems. However, since the GPUs used for AD build on designs for the mainstream market, they may lack fundamental properties for correct operation under automotive's safety regulations. In this paper, we analyze some of the main challenges in hardware and software design to embrace GPUs as the reference computing solution for AD, with the emphasis in ISO 26262 functional safety requirements.Authors would like to thank Guillem Bernat from Rapita Systems for his technical feedback on this work. The research leading to this work has received funding from the European Re-search Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773). This work has also been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P and the HiPEAC Network of Excellence. Jaume Abella has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717. Carles Hernández is jointly funded by the Spanish Ministry of Economy and Competitiveness and FEDER funds through grant TIN2014-60404-JIN.Peer ReviewedPostprint (author's final draft

Report of the workshop on Aviation Safety/Automation Program

As part of NASA's responsibility to encourage and facilitate active exchange of information and ideas among members of the aviation community, an Aviation Safety/Automation workshop was organized and sponsored by the Flight Management Division of NASA Langley Research Center. The one-day workshop was held on October 10, 1989, at the Sheraton Beach Inn and Conference Center in Virginia Beach, Virginia. Participants were invited from industry, government, and universities to discuss critical questions and issues concerning the rapid introduction and utilization of advanced computer-based technology into the flight deck and air traffic controller workstation environments. The workshop was attended by approximately 30 discipline experts, automation and human factors researchers, and research and development managers. The goal of the workshop was to address major issues identified by the NASA Aviation Safety/Automation Program. Here, the results of the workshop are documented. The ideas, thoughts, and concepts were developed by the workshop participants. The findings, however, have been synthesized into a final report primarily by the NASA researchers