Browse Topic: Risk management
Automated vehicles, in the form we see today, started off-road. Ideas, technologies, and engineers came from agriculture, aerospace, and other off-road domains. While there are cases when only on-road experience will provide the necessary learning to advance automated driving systems, there is much relevant activity in off-road domains that receives less attention. Implications of Off-road Automation for On-road Automated Driving Systems argues that one way to accelerate on-road ADS development is to look at similar experiences off-road. There are plenty of people who see this connection, but there is no formalized system for exchanging knowledge. Click here to access the full SAE EDGETM Research Report portfolio.
This Engineering Bulletin and its annexes provide guidance on the application of Human Engineering principles and practices to the analysis, design, development, testing, fielding, support, accident investigation, and training for military and commercial products throughout their intended life cycles.
The purpose of this Standard is to provide an integrated set of fundamental processes to aid a developer in the engineering or reengineering of a system. Use of this Standard is intended to help developers a) establish and evolve a complete and consistent set of requirements that will enable delivery of feasible and cost-effective system solutions; b) satisfy requirements within cost, schedule, and risk constraints; c) provide a system, or any portion of a system, that satisfies stakeholders over the life of the products that make up the system. NOTE—The term product is used in this standard to mean: a physical item, such as a satellite (end product), or any of its component parts (end products); a software item such as a stand-alone application to run within an existing system (end product); or a document such as a plan, or a service such as test, training, or maintenance support, or equipment such as a simulator (enabling products). d) provide for the safe and/or cost-effective
This SAE EDGE™ Research Report builds a comprehensive picture of the current state-of-the-art of human-robot applications, identifying key issues to unlock the technology’s potential. It brings together views of recognized thought leaders to understand and deconstruct the myths and realities of human- robot collaboration, and how it could eventually have the impact envisaged by many.Current thinking suggests that the emerging technology of human-robot collaboration provides an ideal solution, combining the flexibility and skill of human operators with the precision, repeatability, and reliability of robots. Yet, the topic tends to generate intense reactions ranging from a “brave new future” for aircraft manufacturing and assembly, to workers living in fear of a robot invasion and lost jobs.It is widely acknowledged that the application of robotics and automation in aerospace manufacturing is significantly lower than might be expected. Reasons include product variability, size, design
Unsettled Topics in Automated Vehicle Data Sharing for Verification and Validation Purposes discusses the unsettled issue of sharing the terabytes of driving data generated by Automated Vehicles (AVs) on a daily basis. Perception engineers use these large datasets to analyze and model the automated driving systems (ADS) that will eventually be integrated into future “self-driving” vehicles. However, the current industry practices of collecting data by driving on public roads to understand real-world scenarios is not practical and will be unlikely to lead to safe deployment of this technology anytime soon. Estimates show that it could take 400 years for a fleet of 100 AVs to drive enough miles to prove that they are as safe as human drivers.Yet, data-sharing can be developed – as a technology, culture, and business – and allow for rapid generation and testing of the billions of possible scenarios that are needed to prove practicality and safety of an ADS – resulting in lower research
This document addresses measurement uncertainty and consumer risk as they relate to AS8879 thread inspection. It describes the rationale, theory and methodology used to generate the technical content of the AS5870. The document describes how to calculate measurement consumer risk. It documents all of the calculation methods which industry employs today to calculate what is commonly called measurement uncertainty (Appendices A, B, C, D, E and F). These, in turn, are used to calculate measurement uncertainty ratios which are required inputs to calculate measurement consumer risk. Users of this document can apply the information described herein for the evaluation of the capability of their measurements based on the measurement consumer risk. It involves the analysis of the measurement (product) distribution and biases of both the product and measurement system distributions. It protects the consumer from the worst case distribution results.
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