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Training / Education

Introduction to Failure Mode and Effects Analysis for Product and Process

2022-11-08
Failure Mode and Effects Analysis (FMEA) is a systematic method for preventing failure through the discovery and mitigation of potential failure modes and their cause mechanisms. Actions are developed in a team environment and address each high: severity, occurrence or detection ranking indicated by the analysis. Completed FMEA actions result in improved product performance, reduced warranty and increased product quality.
Training / Education

AS9145 Requirements for Advanced Product Quality Planning and Production Part Approval

2022-06-28
Production and continual improvement of safe and reliable products is key in the aviation, space and defense industries. Customer and regulatory requirements must not only be met, but they are typically expected to exceeded requirements. Due to globalization, the supply chain of this industry has been expanded to countries which were not part of it in the past and has complicated the achievement of requirements compliance and customer satisfaction. The IAQG has established and deployed the AS9145 Standard, as a step to help achieve these objectives.
Training / Education

Robotics for Autonomous Vehicle Systems Bootcamp

2022-04-22
The Robotics for AV Systems Bootcamp was developed by SAE International and Clemson University, with industry guidance from Argo AI. This rigorous, twelve-week, virtual-only experience is conducted by leading experts in industry and academia. You’ll develop a deep, technical understanding of how to build autonomous systems by learning to program a mobile robot through hands-on approaches using ROS, Gazebo, and Python.
Technical Paper

Performance Evaluation of an Autonomous Vehicle Using Resilience Engineering

2022-03-29
2022-01-0067
Standard operation of autonomous vehicles on public roads results in significant exposure to high levels of risk. There is a significant need to develop metrics that evaluate safety of an automated system without reliance on the rate of vehicle accidents and fatalities compared to the number of miles driven; a proactive rather than a reactive metric is needed. Resilience engineering is a new paradigm for safety management that focuses on evaluating complex systems and their interaction with the environment. This paper presents the overall methodology of resilience engineering and the resilience assessment grid (RAG) as an evaluation tool to measure autonomous systems' resilience. This assessment tool was used to evaluate the path tracking capabilities of an autonomous vehicle and measure the ability of the control subsystem to respond.
Technical Paper

Design and Hardware in the Loop testing of AEB controllers

2022-03-29
2022-01-0099
Current ADAS systems can improve vehicle safety directly influencing its dynamics, reducing the impact of human error while driving. These functionalities have a high impact on the complexity of each unit installed on the car, potentially increasing the development time. In this work, a Hardware in the Loop testing methodology for Autonomous Emergency Braking system is presented, aiming to enable a faster system development process. A commercial production brake by wire unit has been installed on a real-time driving simulator. The AEB functionality of the unit is activable in real-time during the simulation, by the means of a customizable control strategy. Three different AEB controllers have been implemented: the first one reproduces the unit stock functionality, while the two remaining compute the requested deceleration using a PID and a Fuzzy control strategy.
Technical Paper

A humanized vehicle speed control to improve the acceptance of automated longitudinal control

2022-03-29
2022-01-0095
Vehicle speed controls, as adaptive cruise control and related automated evolutions, are control systems able to follow a desired vehicle reference speed that is set by the driver and fused with information as road signs, SD maps etc.. Current normal production systems don’t distinguish among the vehicle users, only some carmakers are doing first steps towards the introduction of learning from driver to adapt the traditional control. In our work, we follow up this content with a humanized speed control, based on learning of driver longitudinal behavior. This method is able to combine machine learning algorithms, vehicle positioning and recurrent trips into existing automated longitudinal control systems. Proposed algorithm can reduce the interactions between drivers and automated systems by improving the acceptance of automated longitudinal control. Furthermore, proposed integration works mainly on speed reference that dramatically simplifies the customization of the system.
Technical Paper

Test-in-Production framework on a microcontroller environment

2022-03-29
2022-01-0112
In modern automobiles, many new complex features are enabled by software and sensors. When combined with the variability of real-world environments and scenarios, validation of this ever-increasing amount of software becomes complex, costly, and takes a lot of time. This challenges automakers ability to quickly and reliably develop and deploy new features and experiences that their customers want in the marketplace. While traditional validation methods and modern virtual validation environments can cover most new feature testing, it is challenging to cover certain real-world scenarios. These scenarios include variation in weather conditions, roadway environments, driver usage, and complex vehicle interactions. The current approach to covering these scenarios often relies on data collected from long vehicle test trips that try to capture as many of these unique situations as possible. These test trips contribute significantly to the validation cost and time of new features.
Technical Paper

Development of an Integrated Co-Simulation Environment to Model and Test Advanced DAT Features Utilizing MIL and HIL

2022-03-29
2022-01-0101
The widespread deployment of Connected and Automated Vehicles (CAVs) in the future will bring major changes in the automotive industry and the vehicle features it will offer. Currently, technology and infrastructure are not ready to test and develop CAV features fully in real traffic. Simulators are becoming popular to develop CAVs and assisted automated driving features for all levels of automation to overcome the infrastructural needs of the automotive industry. Simulators allow researchers to design CAV algorithms safely, quickly, and efficiently, and test these algorithms for various metrics. A co-simulation environment, where a vehicle simulator like CarSim and a traffic simulator like Simulation of Urban Mobility (SUMO) feed into each other, is an invaluable tool, allowing CAV features to be tested in a realistic traffic environment. This paper presents a co-simulation environment, where the vehicle simulator CarSim and the traffic simulator SUMO share data.
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