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This training class describes how to develop and safeguard safety critical embedded software in serial projects with Simulink in compliance with ISO 26262 (part 6). Beginning with a general overview of the ISO standard, we proceed by focusing on the ISO 26262 requirements that are specifically relevant to model based development. We address the impact the standard has had on model-based development with Simulink, as well as the requirements for model and software architecture in safety critical software. We also look at modeling guidelines and testing before wrapping up the class by assessing ISO 26262 readiness of controller functions.

This course examines ADAS and autonomous vehicle technologies that offer the potential to increase safety while attempting to optimize the cost of car ownership. LIDAR (light detection ranging) and Infrared camera sensing are seeing a rapid growth and adoption in the industry. However, the sensor requirements and system architecture options continue to evolve almost every six months. This course will provide the foundation to build on for these two technologies in automotive applications. It will include a demonstration model for LIDAR and Infrared camera.

This course is offered in China only and presented in Mandarin Chinese. The course materials are bilingual (English and Chinese). As the complexity of products increases, traditional text-based systems engineering can no longer meet the needs. To solve the problem, Model-based Systems Engineering offers a unified communication platform among relevant staff by carrying out diagram-based unambiguous description, analysis and design for the demand, and structure and behavior of complex systems in the form of a model.

Advanced Driver Assist System (ADAS) and autonomous vehicle technologies have disrupted the traditional automotive industry with potential to increase safety and optimize the cost of car ownership. Light detection and ranging (LIDAR) sensing, a sensing method that detects objects and maps their distances, is seeing rapid growth and adoption in the industry. However, the sensor requirements and system architecture options continue to evolve. This course will provide the foundation to build LIDAR technologies in automotive applications.

Advanced Driver Assist System (ADAS) and autonomous vehicle technologies have disrupted the traditional automotive industry with potential to increase safety and optimize the cost of car ownership. Among the challenges are those of sensing the environment in and around the vehicle. Infrared camera sensing is seeing a rapid growth and adoption in the industry. The applications and illumination architecture options continue to evolve. This course will provide the foundation on which to build near infrared camera technologies for automotive applications.

Use of Model-Based Systems Engineering (MBSE) has been growing across industry, extending beyond defense and aerospace to include various commercial enterprises such as automotive and healthcare. Tool vendors are quick to point out benefits of this model-based approach and practices but are not always clear how MBSE benefits can be realized on a project. When deployed successfully, several key considerations should be addressed that maximize the value for a use-case. This four-hour class will discuss the nature and purpose of the MBSE approach and how key information is used for successful MBSE deployment as it relates to Systems Management.

Photographs and video recordings of vehicle crashes and accident sites are more prevalent than ever, with dash mounted cameras, surveillance footage, and personal cell phones now ubiquitous. The information contained in these pictures and videos provide critical information to understanding how crashes occurred, and  analyze physical evidence. This course teaches the theory and techniques for getting the most out of digital media, including correctly processing raw video and photographs, correcting for lens distortion, and using photogrammetric techniques to convert the information in digital media to usable scaled three-dimensional data.

Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers. Unlike traditional rules-based approaches to coding intelligent systems, networks are trained and the internal structure created during the training process is too complex to be understood by humans, yet in operation networks are able to classify objects of interest at error rates better than rates achieved by humans viewing the same input data.

Many technical projects, most vehicle and component testing, and all accident reconstructions, product failure analyses, and other forensic investigations, require photographic documentation. Roadway evidence disappears, tested or wrecked vehicles are repaired, disassembled, or scrapped, and components can be tested for failure. Photographs are frequently the only evidence that remains of a wreck, or the only records of subjects before or during tests. Making consistently good images during any inspection is a critical part of the evaluation process. 

From lenses to composition, these videos cover everything you need to know when photographing accident reconstruction. Join accident reconstruction expert Tom Vadnais as he demonstrates how to photograph trucks, passenger vehicles, and accident sites, as well as a looks at must-have photography equipment.

Introduction to Power Electronic Converters: In this course, you will learn why we need power converters. We will discuss the basic principles of power conversion and you will receive a brief overview of the various types of power converters. You will learn how to recognize different voltage, current, and power levels, as well as the AC or DC character of electrical power, through examples of common, present-day electrical applications. Power conversion and efficiency in battery systems: In this course, you will be taken into the lab to get acquainted with power conversion principles and efficiency measurements.

Hybrid Electric Vehicle (HEV) models currently populating the vehicle electrification landscape indicate their dominance in the market. Although electric, plug-in, and fuel cell vehicles are making inroads in the market, the HEV stands as the market leader in adoption, and manufacturers have no plans on diminishing HEV production. HEV powertrain operation is totally different from the traditional vehicle. It is essential to understand the various operating modes and how failure modes in the hybrid system affect its operation.