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Trucking fleets are increasingly installing video event recorders in their vehicles. The video event recorder system is usually mounted near the vehicle's rear view mirror, and consists of two cameras: one looking forward and one looking towards the driver. The system also contains accelerometers that record lateral and longitudinal g-loading, and some may record vehicle speed (in mph) based on GPS positions. The unit constantly monitors vehicle acceleration and speed, and also records video. However, the recorded data is only stored when a preset acceleration threshold is met. The primary use of the system is to assist fleets with driver training and education, but the recorded data is also being used as a tool to reconstruct accidents. By integrating the accelerometer data, the vehicle speed and distance traveled during the event can be calculated.

The Eaton VORAD Collision Warning System is utilized by many commercial trucking companies to improve and monitor vehicle and driver safety. The system is equipped with forward and side radar sensors that detect the presence and movements of vehicles around the truck to alert the driver of other vehicles' proximity. When the sensors detect that the host vehicle is closing on a vehicle ahead at a rate beyond a determined threshold, or that a nearby vehicle is located in a position that may be hazardous, the system warns the driver visually and audibly. The system also monitors parameters of the vehicle on which it is installed, such as the vehicle speed and turn rate, as well as the status of vehicle systems and controls. The monitored data is also recorded by the VORAD system and can be extracted in the event that the vehicle is involved in an accident.

Driver visibility from commercial vehicles is often an issue in post-accident litigation. While the visibility through the windows of most vehicles is restricted due to the required structure of the vehicle itself, most manufacturers and users incorporate a series of mirrors to enhance driver visibility and to reduce blind spots. The challenge for an engineer is to first demonstrate what the driver could see to a reasonable degree of engineering certainty, and then to convey this information in a form that is easy for the lay person to grasp. This paper outlines procedures for calculating and modeling the driver visibility from commercial vehicles. The primary techniques presented require access to the vehicle, although the paper also presents techniques by which visibility can be analyzed through photogrammetry and 3-D computer models, both for the vehicle and for any mirrors incorporated onto the vehicle.

In reconstructing any accident, the reconstructionist must properly account for uncertainty in their analysis. One popular method of examining and quantifying the uncertainty within an analysis is the use of Monte Carlo simulation techniques. The methods have been well established and published over the last several years by numerous authors. One of the key factors underlying the Monte Carlo analysis is the assumed probability distribution of the individual factors within the analysis. The literature has examples and recommendations for assuming normal, uniform, or custom distributions for input parameters. However, the literature to date has not examined how the assumption of a distribution affects the resulting probability distribution of the Monte Carlo analysis. This paper attempts to address this issue.

In the introduction of Automotive Engineering Fundamentals, Richard Stone and Jeffrey K. Ball provide a fascinating and often amusing history of the passenger vehicle, showcasing the various highs and lows of this now-indispensable component of civilized societies. The authors then provide an overview of the publication, which is designed to give the student of automotive engineering a basic understanding of the principles involved with designing a vehicle. From engines and transmissions to vehicle aerodynamics and computer modeling, the intelligent, interesting presentation of core concepts in Automotive Engineering Fundamentals is sure to make this an indispensable resource for engineering students and professionals alike.

Experimental data was obtained from a Rover K4 optical access engine and analyzed with a combustion analysis package. Cyclic NO values were calculated by mass averaging the measurements obtained by a fast NO analyzer. While the mass averaged results were used as the basis of comparison for the model, results indicate that mass averaging a fast NO signal is not nearly as critical as mass averaging a fast FID signal. A computer simulation (ISIS - Integrated Spark Ignition engine Simulation) was used to model the NO formation on a cyclic basis by means of the extended Zeldovich equations. The model achieves its cyclic variability through the input of experimentally derived burn rates and a completeness of combustion parameter, which is based on the Rassweiler and Withrow method of calculating mass fraction burned and is derived from the pressure-crank angle record of the engine.

This paper investigates a technique of calculating the completeness of combustion on a cycle-by-cycle basis. The technique introduces the normalized pressure rise due to combustion parameter (Ψ) to describe the completeness of combustion. This parameter is based on the Rassweiler and Withrow method of calculating mass fraction burned and is derived from the pressure-crank angle record of the engine. Experimental data were obtained from a Rover K4 optical access engine and analyzed with a combustion analysis package. A computer simulation was then used to model the data on a cyclic basis, both with and without the completeness of combustion parameter. The inclusion of completeness of combustion improved the simulation's ability to model the experimental data both in a statistical sense (COV of IMEP) and on a cycle-by-cycle basis.

This work examines the possibility of detecting misfires via measurements of the angular acceleration of the engine block. Measurements were taken on a production 4-cylinder engine which was modeled as a single degree of freedom torsional oscillator. The torque waveform was estimated and compared to the torque calculated via cylinder pressure measurements. Further analysis was conducted in the frequency domain. Results indicate that metrics based on low frequency information were most reliable, but this is impractical for vehicular applications. The accuracy of high frequency metrics was degraded due to the limitations of the model and the non-rigid behavior of the block at high engine speeds.