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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.

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.

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. 

When vehicles share certain information wirelessly via Dedicated Short Range Communications (DSRC), they enable a new layer of electronic vehicle safety that, when needed, can generate warnings to drivers and even initiate automatic preventive actions. Vehicle location and velocity provided by Global Navigation Systems (GNSS), including GPS, are key in allowing vehicle path estimation. GNSS is effective in accurately determining a vehicle's location coordinates in most driving environments, but its performance suffers from obstructions in dense urban environments. To combat this, augmentations to GNSS are being contemplated and tested. This testing has been typically done using a reference GNSS system complimented by expensive military-grade inertial sensors, which can still fail to provide adequate reference performance in certain environments.

We report here results from first year of the BMW MINI E deployment. BMW deployed 450 MINI E?s to North America. Nearly 50% were leased by households in Los Angeles and the New York area. PH&EV Center researchers surveyed MINI E drivers throughout their year with the vehicles, focusing on the experiences of 50 households who volunteered for more detailed interviews. We report here their experiences with driving electric vehicles, adaptions to daily range limitations, and using electricity as a fuel. Presenter Thomas Turrentine, Univ. of California-Davis

ECOtality North America, in partnership with the Idaho National Laboratory (INL), Nissan North America, General Motors, and over 40 government, electric utility, and private organizations, has launched a large-scale demonstration of electric vehicle charging infrastructure. This demonstration, called The EV Project, will deploy more than 15,000 level 2 and DC fast chargers in private residence, commercial, and public locations in seven market areas in Arizona, California, Oregon, Tennessee, Texas, Washington state, and Washington, D.C. The EV Project will also include a total of 5,700 Nissan Leaf battery electric vehicles and 2,600 Chevrolet Volt extended range electric vehicles, operated by consumers and fleets in each of the market areas. This demonstration, which is funded by the U.S. Department of Energy�s (DOE) Vehicle Technologies Program, represents the largest ever deployment of electric vehicles and charging infrastructure.

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.

This article characterizes the special features of drilling of CFRP/Titanium and -Aluminium stacks. Simplified theoretic models will show how CFRP/Titanium stacks should be machined without scratches and burn marks contacting carbon. Low axial forces and smart chip removal technology are the main characteristics of the drilling tool technology, optimized to reach H8 quality in one shot operation. Presenter Peter Mueller-Hummel, Cutting Tools Inc.

The presentation provides an overview about the activities of Eurocae Working Group 72 (WG-72) starting with a brief synopsis of the context which suggested why such a committee should be established in 2006. It then goes into further detail about the drivers for the work of the committee, which call for the products to be delivered. It addresses some of the challenges with respect to its users. It points out that one of the lessons the committee learned was importance of the focus on the users, such that the products provide their maximum utility. Hence, the users should better be among the participants to achieve this objective. Other industries have dealt with the subject of Information System (or Cyber-Physical) Security long before this industry was forced to consider it. Consequently there are many industry standards and national or international norms, which may help to develop what is deemed needed for Civil Aviation.

In Aeronautic industry, when we launch a new industrialization for an aircraft sub assembly we always have the same questions in mind for drilling operations, especially when focusing on lean manufacturing. How can we avoid dismantling and deburring parts after drilling operation? Can a drilling centre perform all the tasks needed to deliver a hole ready to install final fastener? How can we decrease down-time of the drilling centre? Can a drilling centre be integrated in a pulse assembly line? How can we improve environmental efficiency of a drilling centre? It is based on these main drivers that AIRBUS has developed, with SPIE and SOS, a new generation of drilling centre dedicated for hard materials such as titanium, and high thicknesses. The first application was for the assembly of the primary structure of A350 engine pylons. The main solution that was implemented meeting several objectives was the development of orbital drilling technology in hard metal stacks.

A significant step is achieved on the flight control actuation system toward the more electrical aircraft through the Airbus A380, A400M and the A350 development phase ongoing. The A380/A400M/A350 features a mixed flight control actuation power source distribution, associating electrically powered actuators with conventional FlyByWire hydraulic servocontrols. In the scope of the preparation of the future Airbus Aircraft, this paper presents the perspectives of the use of the EMA technologies for the flight control systems in the more electrical aircraft highlighting the main technical challenges need to treat: jamming susceptibility, ?on board? maintenance reduction, Operational reliability increase, power electronics and power management optimization, and regarding the environmental constraints, the predicted performances; the benefits associated to the optimized utilization of on-board power sources.

ISO 26262 is the actual standard for Functional Safety of automotive E/E (Electric/Electronic) systems. One of the challenges in the application of the standard is the distribution of safety related activities among the participants in the supply chain. In this paper, the concept of a Safety Element out of Context (SEooC) development will be analyzed showing its current problematic aspects and difficulties in implementing such an approach in a concrete typical automotive development flow with different participants (e.g. from OEM, tier 1 to semiconductor supplier) in the supply chain. The discussed aspects focus on the functional safety requirements of generic hardware and software development across the supply chain where the final integration of the developed element is not known at design time and therefore an assumption based mechanism shall be used.

Exatec� PC glazing technology team, has developed advanced weathering and abrasion resistant coatings technology that can be applied to protect polycarbonate. It is of particular interest to quantify and understand the factors that determine the surface abrasion performance of coated PC in rear window and backlight applications that have a wiper system. In the present study we describe Exatec's lab scale wiper testing equipment and test protocols. We also describe adaptation of optical imaging system to measure contrast and nano-profiling using nano-indenter, as post wiper surface characterization methods. These methods are more sensitive to fine scratches on glazing surface than standard haze measurement and mechanical profilometry. Three coating systems were investigated; Siloxane wetcoat (A), Siloxane wetcoat (B), and Siloxane wetcoat (B) plus plasma coat (Exatec� E900 coating). The performance comparisons were made using all these surface characterization methods.

This technical paper collection discusses optical techniques; digital correlation; holography; shearography; nondestructive testing.

Abstract Truck platooning comprises a number of trucks equipped with automated lateral and longitudinal vehicle control technology, which allows them to move in tight formation with short following distances. This study is an initial step toward developing an understanding of the occupant injury risks associated with the multiple sequential impacts between truck platoons and roadside safety barriers, regardless of whether the crash is associated with a malfunction of automated control or human operation. Full-scale crash impacts of a tractor-trailer platoon into a concrete bridge guardrail were simulated for a specific Test Level condition according to the Manual for Assessing Safety Hardware (MASH) standards. The model of the bridge barrier was developed based on its drawings, and material properties were assigned according to literature data.

Abstract A number of studies have shown that driving an unfamiliar vehicle has the potential to introduce additional risk, especially for novice drivers. However, such studies have generally used statistical methods based on analyzing crash and near-crash data from a range of driver groups, and therefore the evaluation has the potential to be subjective and limited. For a more objective perspective, this study suggests that it would be worthwhile to consider vehicle dynamic signals obtained from the Controller Area Network (CAN-Bus) and smartphones. This study, therefore, is focused on the effect of driver experience and vehicle familiarity for issues in driver modeling and distraction. Here, a group of 20 drivers participated in our experiment, with 13 of them having participated again after a one-year time lapse in order for analysis of their change in driving performance.

Abstract Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components. Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data. Secondly, the LCM has an ability of learning the global spatial-temporal information via Temporal Modeling Blocks (TMBs). Finally, a two-layer Long Short-Term Memory (LSTM) network is used to learn video contextual features combined with lane boundary based distance features in lane change events.