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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.

Advanced vehicular thermal management system can improve engine performance, minimize fuel consumption, and reduce emissions by harmoniously operating computer-controlled servomotor components. In this paper, a neural network-based optimal control strategy is proposed to regulate the engine temperature through the advanced cooling system. Presenter Asma Al Tamimi, Hashemite University

A key strategy to improving the real-world fuel consumption and emissions of medium and heavy duty vehicles is the hybridization of these applications. Unlike the passenger vehicle market, medium and heavy duty applications are typically comprised of a range of components from a variety of manufacturers. Presenter Monika A. Minarcin, Navistar, Inc. Eric Rask, Argonne National Laboratory Matthew R. Smith, Navistar, Inc.

These advanced checks have resulted in development of many new diagnostic monitors, of varying types, and a whole new internal software infrastructure to handle tracking, reporting, and self-verification of OBD related items. Due to this amplified complexity and the consequences surrounding a shortfall in meeting regulatory requirements, efficient and thorough validation of the OBD system in the powertrain control software is critical. Hardware-in-the-Loop (HIL) simulation provides the environment in which the needed efficiency and thoroughness for validating the OBD system can be achieved. A HIL simulation environment consisting of engine, aftertreatment, and basic vehicle models can be employed, providing the ability for software developers, calibration engineers, OBD experts, and test engineers to examine and validate both facets of OBD software: diagnostic monitors and diagnostic infrastructure (i.e., fault memory management).

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.

Translating service information into multiple human natural languages is a major expense for multinational corporations. Indeed corporations spend significant amounts to reduce their translation expense by managing the reuse text and its translation. The new frontier in reducing translation expense is translation avoidance, eliminating text through using illustrations and animations to communicate. Presenter Arnold Taube

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.

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.

The conference of choice for the sharing of technical knowledge and networking for the global on-road and off-road commercial vehicle industry. To learn more about this industry driven and industry relevant event, visit www.sae.org/events/comvec

Experimental methods and computational applications related to tire and wheel systems as well as vehicle dynamics and simulation topics are included in the 40 papers in this technical paper collection.

The 9 papers in this technical paper collection focus on occupant protection in accident reconstruction. Topics include: particle image velocimetry; heavy truck engine retarders; repeatability and reliability of drag sled testing; pedestrian impact on low friction surface; photogrammetric measurement error associated with lens distortion; passenger vehicle response to low-speed impacts involving a tractor semitrailer; and more.

The papers in this collection focus on the latest research related to methods and techniques for reconstructing vehicular crashes involving wheeled and tracked vehicles, pedestrians, and roadside features. Emphasis is placed on experimental data and theoretical methods that will enable reconstructionists to identify, interpret and analyze physical evidence from vehicular crashes.

Active Safety and Driver assistance systems are gaining importance as many passive safety systems have already been found to have yielded significant safety benefits that are possible from the deployment of those systems in the fleet. Similar success will much depend upon how fast these systems proliferate the entire passenger vehicle fleet. It will also depend on the deployment strategies used by the industry and the government as well as consumer acceptance and market demand for these systems. Additionally, opportunities exist to use the information gained from the various onboard sensors and vision systems in active safety systems for improving the effectiveness of today’s passive safety systems such as seat belts, airbags, and post-crash safety systems even further by the integration of active and passive safety systems.

This set includes: SAE International Journal of Aerospace March 2010 - Volume 2 Issue 1 SAE International Journal of Commercial Vehicles October 2009 - Volume 2, Issue 1 March 2010 - Volume 2, Issue 2 SAE International Journal of Engines October 2009 - Volume 2, Issue 1 March 2010 - Volume 2, Issue 2 SAE International Journal of Fuels and Lubricants October 2009 - Volume 2, Issue 1 March 2010 - Volume 2, Issue 2 SAE International Journal of Materials and Manufacturing October 2009 - Volume 2, Issue 1 March 2010 - Volume 2, Issue 2 SAE International Journal of Passenger Cars - Electronic and Electrical Systems October 2009 - Volume 2, Issue 1 SAE International Journal of Passenger Cars - Mechanical Systems October 2009 - Volume 2, Issue 1 March 2010 - Volume 2, Issue 2

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.