SAE International is inviting global participation in its AeroTech® aerospace and defense technology conference and exhibition, which is for the first time co-located with ASM International’s AeroMat, at the Pasadena Convention Center in Pasadena, California, March 15 through 17, 2022.
This document describes machine-to-machine (M2M) communication to enable cooperation between two or more participating entities or communication devices possessed or controlled by those entities. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle with driving automation feature(s) engaged. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of manually operated vehicles or pedestrians or cyclists carrying personal devices), or road operators (e.g., those who maintain or operate traffic signals or workzones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the movement of multiple vehicles in proximity to one another. This is accomplished, for example, by sharing information that can be used to influence (directly or indirectly) DDT performance by one or more nearby road users.
There are many industries where safety is a major, if not the primary, concern, such as aviation and nuclear power. These industries rely on many layers of standards for designing, developing, and deploying safety critical systems and technologies. While unmanned aircraft system (UAS) operations and UAS Traffic Management (UTM) are often touted as “safety critical”, the systems and technologies are not being held to the same standards as traditional aviation, with its long pedigree of safety. There are multiple reasons for this dichotomy. One such reason is that design assurance standards, such as DO-178 for software, do not fit with modern technology such as web-based communication and machine learning. At the architecture level, the federated approach to UTM has led to a void in the Systems Engineering process. Nobody “owns” the entire system and therefore nobody owns the Systems Engineering process where many safety related design decisions are traditionally made.
In the age of 5G, the cloud constitutes a massive computational resource. Such capability is greatly underutilized, especially for the purpose of vehicle diagnostics and prognostics. Diagnostics and prognostics run mostly in the limited and cost sensitive electronic module of the vehicle. Utilizing vehicle connectivity, along with the massive capability of the cloud would allow the deployment of smarter algorithms that provide improved vehicle performance and operation management. In this paper, a streamlined process to develop and deploy off-board diagnostics is presented. The process included developing multiphysics digital twins and running the diagnostics off-board. It was demonstrated on a fleet of virtual Hybrid Electric Vehicles (HEV). The Digital Twin replica was created using Simulink® and Simscape®. The microcontroller used to demonstrate the diagnostic is a Raspberry Pi hardware running in real time.
The advancements of autonomous vehicles or advanced driver assistance systems in terms of safety, driving experience, and comfort against manual driving results in extensive adoption of them across the modern automotive sector. The autonomous vehicles are equipped with numerous sensing and actuating components both inside as well as outside the vehicles to perceive the environment, perform path planning, and intelligently control the autonomous vehicles. The perception mechanism includes fused information of multiple sensors such as camera, RADAR, and LiDAR to effectively understand all the dynamic driving environments. Some of the intentional and unintentional mechanisms such as cyber-attacks and natural variations of the environment, etc., across the sensor's external interface with the environment cause the degradation of the perception mechanism.