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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.

Looking for a solution that will provide a deeper understanding of the autonomous vehicle (AV) space for your employees? Hoping to offer training as a value add for employee retention? Need a flexible option online that can be taken anywhere, anytime? SAE’s Autonomous Vehicles for Transportation Professionals is the answer. The instructor, Dr. Rahul Razdan, is a leading authority in the field with extensive background in computing from his work at Carnegie-Mellon and Harvard Universities. He has written extensively on these topics at SAE with the landmark SAE Edge reports and on his Forbes transportation column.

This course highlights the technologies enabling ADAS and how they integrate with existing passive occupant crash protection systems, how ADAS functions perceive the world, make decisions, and either warn drivers or actively intervene in controlling the vehicle to avoid or mitigate crashes. Examples of current and future ADAS functions, and various sensors utilized in ADAS, including their operation and limitations, and sample algorithms, will be discussed and demonstrated. The course utilizes a combination of hands-on activities, including computer simulations, discussion and lecture.

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.

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. 

This four-hour short course provides key considerations for the comparison of electrically supplied (Power-by-Wire, or PbW) and hydraulically supplied (Power-by-Pipe, of PbP) actuation for aerospace. The focus is put on the consequence, for designers, of changing the physical principles and the technology used. A particular attention is paid to the unavoidable side effects introduced by the technological realization. Simple examples with realistic numerical values are used to make the comparisons quantitatively realistic.

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.