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This course is offered in China only and presented in Mandarin Chinese. Driving simulator is a key component of automotive interactive design, which can provide objective data to support both the effectiveness of design and user experience. On the other hand, driving simulator and the design of its experiments are major challenges, as improper experiment design will lead to consequences such as failure in identifying designing errors of the interactive design itself.

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.

This course is verified by Probitas as meeting the AS9104/3A requirements for Continuing Professional Development. This course serves a dual purpose: it delves into fundamental DFMEA principles and their practical applications while also offering guidance on leading DFMEA teams. Participants will be introduced to crucial FMEA concepts, along with the theoretical foundations before exploring how to implement these concepts in their DFMEA endeavors. Often, the FMEA process can become a mere replication of past efforts, which poses risks for both organizations developing the products under scrutiny and the end-users.

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. 

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.

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.