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Environmental perception is considered an essential module for autonomous driving and Advanced Driver Assistance System (ADAS). Recently, deep Convolutional Neural Networks (CNNs) have become the State-of-the-Art with many different architectures in various object detection problems. However, performances of existing CNNs have been dropping when detecting small objects at a large distance. To deploy any environmental perception system in real world applications, it is important that the system achieves high accuracy regardless of the size of the object, distance, and weather conditions. In this paper, a robust sensor fused object detection system is proposed by utilizing the advantages of both vision and automotive radar sensors. The proposed system consists of three major components: 1) the Coordinate Conversion module, 2) Multi level-Sensor Fusion Detection (MSFD) system, and 3) Temporal Correlation filtering module.

The torsional natural frequencies of axles equipped with limited slip differential clutches depend on whether or not the tires and clutches are slipping since the effective inertia at each end of the axle is different for slipping and non-slipping conditions. Limited slip axle vibrations are typically analyzed for one tire slipping and the other not since that is the case for which the limited slip clutches are used. Vibrations often arise, however, during normal turning when both drive tires have good traction.

Large hood mounted plastic trim components are subjected to complex and often extreme loading conditions. Typical loading conditions include solar and thermal cycling, as well as road and powertrain induced vibrations, aero lift and buffeting, and mechanical loads such as car wash. For the above components understanding and classifying the typical loading conditions is an essential and important step in achieving long term quality. This paper discusses different approaches to the design, analysis, development, and testing of plastic trim components. Samples of analysis and test results are presented to demonstrate how to identify and prevent the loss of the part function. Some useful guidelines and practices for addressing thermal expansion, dimensional variation, and redundancy in attachments are also discussed.

In automotive assembly facilities worldwide, many critical vehicle systems such as brakes, power steering, radiator, and air conditioning require the appropriate fluid to function. In order to insure that these critical vehicle systems receive the correct amount of properly treated fluid, automotive manufacturers employ a method called Evacuation and Fill. Due to their closed-loop design, many critical vehicle systems must be first exposed to vacuum prior to being flooded with fluid. Only after the evacuation and fill process is complete will the critical vehicle system be able to perform as specified. It has long been thought, but never proven, that humidity and entrenched fluid were major hindrances to the Evacuation and Fill process. Consequently, Ford Motor Company Advanced Manufacturing Technology Development, Sandalwood Enterprises, Kettering University, and Dominion Tool & Die conducted a detailed project on this subject.

A model and simulation results are presented for the torsional dynamics of a rear wheel driveline while the vehicle is coasting in a turn. The model includes the effects of road load and powertrain drag, limited slip differential clutch friction, the inertias of the vehicle, wheels, axles, differential carrier, and driveshaft, the final drive ratio, torsional stiffnesses of the axles and driveshaft, vehicle track width, and radius of the turn. The dynamics of coasting in a turn differ from powered driving due to changes in the inertia loading the driveshaft, the damping effect of the disengaged transmission, and nonlinearities in the clutch friction. Specific focus is given to vibration in the axles and driveshaft due to variations in the torque-speed slope of the clutches, which is determined by the slope of the friction coefficient ‘μ’ versus sliding speed ‘v’ in the limited slip clutches.

Data-driven modeling can help improve understanding of the governing equations for systems that are challenging to model. In the current work, the Sparse Identification of Nonlinear Dynamical systems (SINDy) is used to predict the dynamic behavior of dynamic problems for NVH applications. To show the merit of the approach, the paper demonstrates how the equations of motions for linear and nonlinear multi-degree of freedom systems can be obtained. First, the SINDy method is utilized to capture the dynamic behavior of linear systems. Second, the accuracy of the SINDy algorithm is investigated with nonlinear dynamic systems. SINDy can output differential equations that correspond to the data. This method can be used to find equations for dynamical systems that have not yet been discovered or to study current systems to compare with our current understanding of the dynamical system.

In September 1893, little could 23-year-old mechanic J. Frank Duryea dream of the changes that would be brought about by his creation -- a frail gasoline buggy that made its debut on the streets of Springfield, Massachusetts. Charles E. and J. Frank Duryea, two brothers from rural Illinois, were the founders of the American automobile industry. The Duryea Motor Wagon company was the first company organized in the United States for the manufacture of automobiles. The attention-getting, older brother Charles demanded - and to date has received - the principal credit for these pioneering accomplishments. A bitter family feud between the brothers, which was even carried on by their families after their deaths, further muddied the question about the individual brothers' contributions. However, in Carriages Without Horses: J. Frank Duryea and the Birth of the American Automobile Industry, historian and author Richard P. Scharchburg proves that the quiet, self-effacing younger brother J.