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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.

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.

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.

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. 

Parts, such as bolts, turbine wheels, discs, buckets, and blades, for high strength and oxidation resistance up to 1500 F when suitably heat treated.

SAE 2011 High Efficiency IC Engines Symposium - Session 2 - Frontiers in Light-Duty Engine R&D Presenter Ulrich Spicher, Karlsruhe Inst. of Technology

This session covers topics regarding new CI and SI engines and components. This includes analytical, experimental, and computational studies covering hardware development as well as design and analysis techniques. Presenter Sung Hoon Lee, Hyundai Motor Co.

Some the OBD-II regulations have been around for a long time or seem to be intuitively obvious. It is easy to assume to assume that everyone knows how to implement them correctly, that is, until someone actually reads the words and tries to do it. Most often, these issues come up when modifying existing OBD features, not when creating completely new ones. This presentation contains a few examples of features that should have been easy to implement, but turned out not to be easy or simple. Presenter Paul Algis Baltusis, Ford Motor Co.

From 2009 until present Toyota has had a demonstration program of Prius PHV which is comprised of 600 vehicles throughout Japan, Europe and in the US. The vehicles were given to government agencies, corporations, utility companies and private individuals to use. With these demo units Toyota wanted to understand the market reaction and real world impact of plug-in technology on gasoline displacement with increased use of electricity as a fuel. This presentation shows that approximately 50% of fuel was saved using the PHVs in the US. An experiment in Toyota City shows that if public infrastructure is optimized to be convenient and located where people normally park, there is a potential to achieve an ideal fuel savings of 61%. The demonstration program shows that plug-in technology in fact saves fuel and that the proper infrastructure can optimize the fuel savings of plug-in hybrids. Presenter Avernethy Francisco, Toyota

Need for worldwide harmonization of regulations. Presenter James Vondale, Ford Motor Co.

Update on light-duty regulations. Presenter Michael J. McCarthy, California Air Resources Board

Optical fiber has begun replacing copper in avionic networks. So far, however, it has been mainly restricted to non-critical applications (video transmission to the flight deck, IFE?). In order to take advantage of the high-bandwidth, low weight, no EMI properties of optical fibers in all data transmission networks, it will be necessary to improve the testing. One part of the puzzle, which is still missing, is the self-test button: the possibility to check the network and detect potential failures before they occur. The typical testing tool of a technician involved in optical fiber cables is the ?light source ? optical power meter? pair. With this tool, one can measure the insertion loss of the fiber link. A second important parameter, the return loss at each optical connector, is not analysed. In addition, this is only a global measurement, which does not allow the detection of possible weak points.