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This paper demonstrates the possibilities of using Automotive waste plastic material from “end of life” vehicles (ELVs), in the Footwear Industry to manufacture shoe components. The study establishes the sustainability of the flow of ELVs, from the European Car Parc and identifies and estimates the quantity of plastic materials potentially available for recycling from ELVs. Four potential materials, Acrylonitrile/butadiene/styrene (ABS), Polypropylene (PP), Polypropylene/ethylene/propylene/diene (PP/EPDM) and Polyamide (PA), were identified and three materials (PP, PP/EPDM and ABS) were reprocessed from ELV components and evaluated by the Footwear Industry. As a result, ABS was recommended as an economically, suitable replacement for HIPS, the current material used for manufacturing shoe heel components.

In general for Vehicle-to-Vehicle (V2V) communication, message authentication is performed on every received wireless message by conducting verification for a valid signature, and only messages that have been successfully verified are processed further. In V2V safety communication, there are a large number of vehicles and each vehicle transmits safety messages frequently; therefore the number of received messages per second would be large. Thus authentication of each and every received message, for example based on the IEEE 1609.2 standard, is computationally very expensive and can only be carried out with expensive dedicated cryptographic hardware. An interesting observation is that most of these routine safety messages do not result in driver warnings or control actions since we expect that the safety system would be designed to provide warnings or control actions only when the threat of collision is high.

Countless articles and publications3,4,5 have documented and proven the efficacy, benefits and value of operating within a lean system. Furthermore, there exists common agreement amongst leading organizations successfully implementing a lean system that in order to do so it must take into consideration the entire enterprise, that is, from supplier to customer and everything in between6. One of the core issues this paper addresses is when the optimal time is to train and educate the people who currently have, or will have, influence over the ‘enterprise’.

TFC/IW, total fuel consumption divided by inertia weight is reported with other engineering variables for recent EPA data for industry passenger cars and truck. TFC/IW is used in comparisons between gasoline and diesel engines, 49 States and California, passenger cars and trucks. The California fuel economy penalty due to more stringent emissions standards is discussed. The relationship between TFC/IW and ton miles per gallon is shown. Special attention is focused on 4 cylinder gasoline powered vehicles in 49 States passenger car fleet. The use of TFC/IW to answer the question, ‘What Changed?’ when comparing the fuel economies of two fleets is described.

The SHIFT-MATE is a dashboard mounted computer based device that cues a truck driver to shift more efficiently. Through electronic circuitry, key vehicle parameters are monitored, computed, then via graphic display, instructs the driver when to shift for improved fuel economy. The theory of operation is described in the text.

A sense of “personal integrity” blocks pilot use of new information about how he thinks. Research on human performance under stress done over the past fifty years indicates increased rigidity and regression to earlier learned behavior in high stress, and in low Stress a shift in attention to any domestic situation or on the job controversy which is of higher stress than that of the job at hand, all without the pilot's knowledge. Informal surveys of commercial pilot training and commercial pilot attitudes towards these studies indicate that the study findings directly confront learned cultural responses. Pilot and trainer reactions prevent the information from being adequately investigated or formally taught. The findings are not written into training manuals and pilots who are informally given the information do not have adequate access to the knowledge when it is needed.

A large number of testing procedures have been developed to ensure vehicle safety in common and extreme driving situations. However, these conventional testing procedures are insufficient for testing autonomous vehicles. They have to handle unexpected scenarios with the same or less risk a human driver would take. Currently, safety related systems are not adequately tested, e.g. in collision avoidance scenarios with pedestrians. Examples are the change of pedestrian behaviour caused by interaction, environmental influences and personal aspects, which cannot be tested in real environments. It is proposed to use augmented reality techniques. This method can be seen as a new (Augmented) Pedestrian in the Loop testing procedure.

A survey of the in-service fuel consumption of passenger vehicles and derivatives in the Australian fleet was carried out in 1984-85. Seven hundred and four owners across Australia took part in the survey. Vehicle owners reported by questionnaire the amount of fuel used during four tank fills of normal operation, the distance travelled, and other details of the operating circumstances. The survey shows a clear downward trend in the fuel consumption of the Australian passenger fleet. The data also provides comparisons of actual fuel consumption obtained on the road, with laboratory derived values for fuel consumption. Vehicles in a sub-set of 40 were fitted with fuel flow meters during the survey and tested to Australian Standard 2077 for fuel consumption. The questionnaire method is shown to be a valid and accurate technique for determining in-service fuel consumption.

The current research was aimed at determining the most effective way to use alternative renewable feedstock to power a diesel engine. Mimusops elengi, a new and novel biofuel was recognized for this current study, which is widely available in the south of India. The investigation was conducted on B20 volume basis (20% Mimusops elengi methyl ester blended with 80% diesel). Furthermore, it was recognized that when the performance characteristics were traded off, the emission magnitude has slightly higher. To address the diesel engine pollution, an oxygenated nano additive like titanium oxide was dissipated only with the fuel blend at distinct mass fractions of 25 parts per million (ppm) with differing injection pressures of 180 bar, 200 bar, 220 bar, and 240 bar. The tests were created using a statistical programme known as design of experiments, which is purely based on Taguchi and response surface methodology.

A multi vector design tool to accurately predict instrument panel obscuration was developed to insure that critical legal displays in vehicles are not obscured. The concept provides for a computer generated light source shaped to replicate the human eyes. The light source is then projected onto a 3D math based arrangement and the resultant shadows are visible on the instrument panel surface and its displays. Design studios require criteria for the placement of the instrument cluster gages and displays, various controls, switches, and steering column stalks before an interior theme can be completed. Therefore, instrument panel obscuration and visibility must be determined early in the design process. The obscured areas are a function of the instrument panel surface, steering wheel rim, hub, spokes, and the location of the driver's eyes. This light source method allows engineers and designers the ability to quickly determine obscured areas.

Vehicle dynamics is a discipline of mechanical engineering that benefited of significant improvements thanks to the progress of computational engineering. Vehicle dynamics engineers are using CAE for the development of a vehicle with MBS and FEA. The concurrent use of these two technologies is a standard in the automotive industry. However the current simulation process is not fully efficient because local geometrical and material nonlinearities are not accurately modeled in classical MBS software. This paper introduces a methodology for vehicle dynamics simulation integrating MBS capabilities in one single nonlinear FEA environment enabling an accurate modeling of nonlinearity in vehicles.

The requirement for crew resource management (CRM), or aircrew coordination training (ACT) in military parlance, has been well documented and attested to. In addition, aircraft systems training has become more intense and more in-depth in the new aircraft designs, especially in multi-crew and complex aircraft such as the MV-22 Osprey Tiltrotor. (see Figure 1) Former training systems detailed training procedures that called for classroom training and simulation/simulator training followed by flight training. Improvements in aircraft flight skills training provide increased flying training capability coupled with reduced training time by integrating a mixed simulation/flight training syllabus, e.g. two to three simulation periods followed by one or two flight training periods covering the same material/skills. In addition, the simulation training will introduce new skills; the following flight periods will further refine/hone those skills.

The paper focuses and develops issues raised by the SAE paper ‘THE FUTURE OF VEHICLE ELECTRICAL POWER SYSTEMS AND THEIR IMPACT ON SYSTEM DESIGN’ [1] and describes the realisation of a vehicle with a 12 V architecture of flexible configuration and a power management function. The paper describes the methodology, reasoning and mission behind the creation of the vehicle, developed after collaborative exercises in Europe and the USA, and resulting in a joint programme involving a major vehicle manufacturer and a European system supplier. The electrical system is becoming the focus of activity world-wide due to rapid changes in vehicle requirements, in the areas of safety, environmental and functional demands. There are opportunities for:- (a) Improved starting (b) Integrated management of power generation and demand. (c) Higher system integrity (d) Higher efficiency (e) Improvement of the vehicle electrical environment, giving benefits in component cost.

The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.

Plug-in Hybrid Electric Vehicles (PHEVs) use electric energy from the grid rather than fuel energy for most short trips, therefore drastically reducing fuel consumption. Different configurations can be used for PHEVs. In this study, the parallel pre-transmission, series, and power-split configurations were compared by using global optimization. The latter allows a fair comparison among different powertrains. Each vehicle was operated optimally to ensure that the results would not be biased by non-optimally tuned or designed controllers. All vehicles were sized to have a similar all-electric range (AER), performance, and towing capacity. Several driving cycles and distances were used. The advantages of each powertrain are discussed.

Driver state monitoring is a crucial technology for enhancing road safety and preventing human error-caused accidents in the era of autonomous vehicles. This paper presents CogniSafe, a comprehensive driver monitoring system that uses deep learning and computer vision methods to detect various types of driver distractions and fatigue. CogniSafe consists of four modules: Driver anomaly detection and classification: A novel two-phase network that proposes and recognizes driver anomalies, such as texting, drinking, and adjusting radios, using multimodal and multiview input. Gaze estimation: A video-based neural network that jointly learns head pose and gaze dynamics, achieving robust and efficient gaze estimation across different head poses. Eye state analysis: A multi-tasking CNN that encodes features from both eye and mouth regions, predicting the percentage of eye closure (PERCLOS) and the frequency of mouth opening (FOM).

Due to the rising costs of fuel and increasingly stringent regulations, auto makers are in need of technology to enable more fuel-efficient powertrain technologies to be introduced to the marketplace. Such powertrains must not sacrifice performance, safety or driver comfort. Today's engine and powertrain manufacturers must, therefore, do more with less by achieving acceptable vehicle performance while reducing fuel consumption. One effective method to achieve this is the extreme downsizing of current direct injection spark ignited (DISI) engines through the use of high levels of boosting and cooled exhaust gas recirculation (EGR). Key challenges to highly downsized gasoline engines are retarded combustion to prevent engine knocking and the necessity to operate at air/fuel ratios that are significantly richer than the stoichiometric ratio.

“Digital Prototype” simulations have been used at DaimlerChrysler to achieve vehicle level NVH objectives. The effectiveness of these simulations to guide the design when faced with vehicle parameter uncertainties is discussed. These uncertainties include, but are not limited to, material properties, material gauges, damping, structural geometry, loads, boundary conditions and weld integrity. Manufacturing and assembly processes introduce variations in the nominal values of these parameters resulting in a scatter of vehicle level NVH simulation responses. An example of a high frequency NVH concern will be studied and modified to arrive at robust design guidance when faced with uncertainty. The validity of a “deterministic digital prototype” simulation model and its relevant role as a “trend predictor” rather than “absolute predictor” tool in guiding the design is also discussed.