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Many accidents occur today when distant objects or roadway impediments are not quickly detected. To help avoid these accidents, longer-range safety systems are needed with real-time detection capability and without requiring a line-of-sight (LOS) view by the driver or sensor. Early detection at intersections is required for obstacle location around blind corners and dynamic awareness of approaching vehicles on intersecting roadways. Many of today's vehicular safety systems require short LOS distances to be effective. Such systems include forward collision warning, adaptive cruise control, and lane keeping assistance. To operate over longer LOS distances and in Non-LOS (NLOS) conditions, cooperative wireless communications systems are being considered. This paper describes field results for LOS and NLOS radio links for one candidate wireless system: 5.9GHz Dedicated Short Range Communications (DSRC).

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.

For the past four years the San Francisco Fire Department has owned and operated an American La France Triple Combination Engine Company powered with a Boeing Model 502 gas turbine engine. This engine company, in first line fire service, has illustrated the practicability of the gas turbine in vehicular applications. The purpose of this paper is to outline the experience gained by the use of a gas turbine engine in such an installation.

Many newer commercial vehicles have an event data recorder (EDR) that can record pre-event and post-event speeds. The EDR is incorporated into the engines electronic control module (ECM). In this study, the accuracy of the ECM-reported speed was tested during acceleration, gear shifting and braking at speeds between 16 and 88 km/h (10 to 55mph). The ECM-reported speed was compared to the speed measured by a calibrated optical 5th wheel. The results showed that the accuracy of the ECM-reported speed matched closely during acceleration, cycled to periods of under-reporting the speed during hard braking due to the ABS brake function, briefly under-reporting the speed after letting off the throttle for braking or gear shift and briefly over-reporting the speed near the end of a gear shift phase. This study also looked at calibration factors of the ECM and their effect on the ECM-reported speed.

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.

As part of an overall effort to support the National Highway Traffic Safety Administration's (NHTSA) national program to increase seat belt usage, General Motors instituted an employe seat belt use incentive program at the General Motors Technical Center in Warren, Michigan. This program was responsible for raising seat belt use at the Tech Center from 36% to 70% during its 5 1/2 month duration. The program was patterned, in part, after research work done by professor E. Scott Geller of the Virginia Polytechnic Institute and State University under a grant from the General Motors Research Laboratories and a program conducted by Berg Electronics (a DuPont subsidiary). The intent of the program was to provide sufficient positive incentive to employes to buckle up for an extended period of time, thereby establishing a seat belt use habit that will continue after the incentives are no longer offered.

The onboard “SODART” telescope silicon detector cooling system of the “Spectrum-X-Gamma” observatory, which is designed for the space objects X-ray radiation study, is described. The scale-down model of the passive cooling system description and thermal vacuum test results of this model are given. In the real cooling system the minimal detector temperature at 300 mW heat release is expected about 107 K.

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 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.

This paper describes the interrelated controls for automatic start sequencing, fuel scheduling, customer air delivery, and supervisory and protective systems as applied to the Curtiss-Wright CW657E “Jet-Air” Compressor. Model CW657E is capable of delivering 15,000 SCFM air at 85 psig (at 30°F and sea level pressure) and may be used in a diversity of manufacturing, processing, and industrial applications. A description of the control system and its operation in relation to compressor requirements, while furnishing air to feed distribution lines to air assisted water atomizing nozzles for snow making is reviewed as an example. Other models can deliver up to 30,000 SCFM with modified control systems, including pressure controls.

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).

This paper is an extension of our previous work on the CHASE (Classification by Holistic Analysis of Scene Environment) algorithm, that automatically classifies the driving complexity of a road scene image during day-time conditions and assigns it an ‘Ease of Driving’ (EoD) score. At night, apart from traffic variations and road type conditions, illumination changes are a major predominant factor that affect the road visibility and the driving easiness. In order to resolve the problem of analyzing the driving complexity of roads at night, a brightness detection module is incorporated in our end-to-end nighttime EoD system, which computes the ‘brightness factor’ (bright or dark) for that given night-time road scene. The brightness factor along with a multi-level machine learning classifier is then used to classify the EoD score for a night-time road scene. Our end-to-end ‘Night-time EoD system’ is a real-time onboard system implemented and tested on road scene data collected in Japan.

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.

The U.S. National Highway Transportation and Safety Agency's (NHTSA) early estimates of Motor Traffic Fatalities in 2009 in the United States [1] show continuing progress on improving traffic safety on the U.S. roadways. The number of total fatalities and the fatality rate per 100 Million Vehicle Miles (MVM), both show continuing declines. In the 10 year period from 1999 through 2009, the total fatalities have dropped from 41,611 to 33,963 and the fatality rate has dropped from 1.5 fatalities per 100MVM to 1.16 fatalities per 100MVM, a compound annual drop of 2.01% and 2.54% respectively. The large number of traffic fatalities, and the slowing down of the fatality rate decline, compared to the decade before, continues to remain a cause of concern for regulators.