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During the launch of a car, severe torsional vibration sometimes may occur in its driveline due to somewhat the slipping of the clutch, its intuitive sense for an occupant is the longitudinal vibration of the vehicle, referred to as the launch shudder whose characteristic frequency is from 5 to 25 Hz generally. As the main vibration sources of the driveline and its crucial nonlinear components, the variable stiffness and backlash of the gear meshing are considered, their impacts on the launch shudder are analyzed in this paper. Conformal mapping, finite element method and regression method etc. are the main approaches to calculate the variable meshing stiffness of a gear pair. If this stiffness is get, it can usually be substituted for its approximate analytical expression, just with finite harmonic terms, in Fourier Series form into Ordinary Differential Equations(ODEs) to calculate the vehicle responses with its nonlinearity considered.

Environmental perception is a crucial subsystem in autonomous vehicles. In order to build safe and efficient traffic transportation, several researches have been proposed to build accurate, robust and real-time perception systems. Camera and LiDAR are widely equipped on autonomous self-driving cars and developed with many algorithms in recent years. The fusion system of camera and LiDAR provides state-of the-art methods for environmental perception due to the defects of single vehicular sensor. Extrinsic parameter calibration is able to align the coordinate systems of sensors and has been drawing enormous attention. However, differ from spatial alignment of two sensors’ data, joint calibration of multi-sensors (more than two sensors) should balance the degree of alignment between each two sensors.

This work presents a method for estimating human body orientation using a combination of convolutional neural network (CNN) and stereo camera in real time. The approach uses the CNN model to predict certain human body keypoints then transforms these points into a 3D space using the stereo vision system to estimate the body orientations. The CNN module is trained to estimate the shoulders, the neck and the nose positions, detecting of three points is required to confirm human detection and provided enough data to translate the points into 3D space.

Lithium-ion batteries (LIBs) with relatively high energy, power density and eco-friendly characteristic are considered as a vital energy source in consumer market of portable electronics and transportation sector especially in electric vehicles (EVs). To meet the higher capacity requirements, the nickel-rich LIBs with higher capacity has been used as the commercial power batteries. However, the battery with higher energy density is more destructive, which could result in thermal runaway accidents and make the battery safety issues become more and more prominent. Thermal runaway of LIBs is one of the key scientific problems in safety issues. Until now, the inducement of thermal runaway process is complicated which perplex researchers and industry a lot. On the one hand, the internal mechanism about thermal runaway should be deeply studied. On the other hand, in-situ monitoring should be developed to supply the mechanism study and early warning.

As vacuum suction cups are widely used in stamping plants, it becomes urgent and important to understand their performance and failure mode. Vacuum suction cups are employed to lift, move, and place sheet metal instead of human hands. Occasionally the vacuum cups would fail and drop parts, even it would cause expensive delays in the production line. In this research, several types of vacuum cups have been studies and compared experimentally. A new tensile device and test method was developed to measure the pulling force and deformation of vacuum cups. The digital image correlation technique has been adopted to capture and analyze the contour, deformation and strain of the cups under different working conditions. The experimental results revealed that the relevant influential parameters include cup type, pulling force angles, vacuum levels, sheet metal curvatures, etc.

Fatigue testing is a specialized form of mechanical testing that is performed by applying cyclic loading to a coupon or structure. Two common forms of fatigue testing are load controlled high cycle and strain controlled low cycle fatigue. Some strain measurement device, such as extensometers, strain gage, that are often used as a feedback sensor on strain controlled fatigue test. However, in applications where strain controlled fatigue testing could face some extreme conditions as well as high temperature and unusual sizing which requires the strain measurement to be nondestructive and full field. While digital image correlation (DIC), an advanced optical measurement technique, has a decent solution on challenges of fatigue testing measurement. The problem is how to turn DIC from a measurement system to a feedback controller unit. Due to the developments in camera and computation techniques, the sequential process can now be performed as a parallel process.

In this research, ball-on-plate reciprocating sliding wear tests were utilized on austempered and quench-tempered gray cast iron samples with and without shot-peening treatment. The wear volume loss of the gray cast iron samples with different heat treatment designs was compared under equivalent hardness. The phase transformation in the matrix was studied using metallurgical evaluation and hardness measurement. It was found that thin needle-like ferrite became coarse gradually with increasing austempering temperature and was converted into feather-like shape when using the austempering temperatures of 399°C (750°F). The residual stress on the surface and sub-surface before and after shot-peening treatment was analyzed using x-ray diffraction. Compressive residual stress was produced after shot-peening treatment and showed an increasing trend with austempering temperature.

In order to explore approaches for improving object detection accuracy in intelligent vehicle system, we exploit super-resolution techniques. A novel method is proposed to confirm the conjecture whether some popular super-resolution networks used for environmental perception of intelligent vehicles and robots can indeed improve the detection accuracy. COCO dataset which contains images from complex ordinary environment is utilized for the verification experiment, due to it can adequately verify the generalization of each algorithm and the consistency of experimental results. Using two representative object detection networks to produce the detection results, namely Faster R-CNN and YOLOv3, we devise to reduce the impact of resizing operation. The two networks allow us to compare the performance of object detection between using original and super-resolved images. We quantify the effect of each super-resolution techniques as well.

A comprehensive formulation is presented for the dynamics of a rotating flexible crankshaft coupled with the dynamics of an engine block through a finite difference elastohydrodynamic main bearing lubrication algorithm. The coupling is based on detailed equilibrium conditions at the bearings. The component mode synthesis is employed for modeling the crankshaft and block dynamic behavior. A specialized algorithm for coupling the rigid and flexible body dynamics of the crankshaft within the framework of the component mode synthesis has been developed. A finite difference lubrication algorithm is used for computing the oil film elastohydrodynamic characteristics. A computationally accurate and efficient mapping algorithm has been developed for transferring information between a high - density computational grid for the elastohydrodynamic bearing solver and a low - density structural grid utilized in computing the crankshaft and block structural dynamic response.

Wall temperature in GDI engine is influenced by both water jacket and gas heat source. In turn, wall temperature affects evaporation and mixing characteristics of impingement spray as well as combustion process and emissions. Therefore, in order to accurately simulate combustion process, accurate wall temperature is essential, which can be obtained by conjugate heat transfer (CHT) and piston heat transfer (PHT) models based on mapping combustion results. This CHT model considers temporal interaction between solid parts and cooling water. This paper presents an integrated methodology to reliably predict in-cylinder combustion process and temperature field of a 2.0L GDI engine which includes engine head/block/gasket and water jacket components. A two-way coupling numerical procedure on the basis of this integrated methodology is as follows.

For sensing system, the trustworthiness of the variant sensors is the crucial point when dealing with advanced driving assistant system application. In this paper, an approach to a hybrid camera-radar application of vehicle tracking is presented, able to meet the requirement of such demand. Most of the time, different types of commercial sensors available nowadays specialize in different situations, such as the ability of offering a wealth of detailed information about the scene for the camera or the powerful resistance to the severe weather for the millimeter-wave (MMW) radar. The detection and tracking in different sensors are usually independent. Thus, the work here that combines the variant information provided by different sensors is indispensable and worthwhile. For the real-time requirement of merging the measurement of automotive MMW radar in high speed, this paper first proposes a fast vehicle tracking algorithm based on image perceptual hash encoding.

A specially designed generator has been developed to produce poly-disperse droplet streams: A liquid fuel (n-heptane) is metered to an ultrasonic atomizer to produce droplets, which are then carried and accelerated vertically upwards through a nozzle tube by carrier-air flow. Conditions of the streams at the nozzle exit are modulated by varying the length of nozzle tubes, the fuel and carrier-air flow rate. Optical measurement techniques such as direct photography method, schlieren photography and particle image velocimetry (PIV) are employed to characterize its performance characteristics. Effects of the nozzle tube length, the carrier-air and fuel flow rate are investigated to evaluate the performance of the generator. Longer nozzle tubes provide a better flow guidance for the carrier-air, and tend to generate streams with less and smaller droplets due to the transporting losses.

A multi-sensor Digital Image Correlation (DIC) system is employed to measure the deformation of metal specimens during tensile tests. The multi-sensor DIC system is capable of providing high quality contour and deformation data of a 3D object. Methodology and advantages of the multi-sensor DIC system is introduced. Tests have been done on steel and aluminum specimens to prove the performance of the system. With the help of the multi-sensor DIC system, we proposed our approaches to determine the forming limit based on shape change around the necking area instead of calculate the FLD based on the in-plane strains. With the employed system, all measurements are done post-deformation, no testing controlling mechanism, such as load force control or touching control, is required. The extracted data is analyzed and the result shows a possibility that we may be able to improve current technique for Forming Limit Diagram (FLD) measurement.

In this work, a multi-camera Digital Image Correlation (DIC) system is applied to measure the material properties of aluminum (5754) specimens. Such tests are usually done using 2D (one-camera) or 3D (two-camera) DIC systems. A multi-camera DIC system includes three or more cameras and inherits all the advantages of a conventional 3D DIC system (with two cameras) such as, full-field measurement, high accuracy and high speed. In addition, this system further improves the measured results by including redundant data. In this work, we will show the potential of this system to measure a variety of material properties at one time.

In this study, effects of altitude on free diesel spray morphology, macroscopic spray characteristics and air-fuel mixing process were investigated. The diesel spray visualization experiment using high-speed photography was performed in a constant volume chamber which reproduced the injection diesel-like thermodynamic conditions of a heavy-duty turbocharged diesel engine operating at sea level and 1000 m, 2000 m, 3000 m and 4500 m above sea level. The results showed that the spray morphology became narrower and longer at higher altitude, and small vortex-like structures were observed on the downstream spray periphery. Spray penetration increased and spray angle decreased with increasing altitude. At altitudes of 0 m, 1000 m, 2000 m, 3000 m and 4500 m, the spray penetration at 1.45 ms after start of injection (ASOI) were 79.54 mm, 80.51 mm, 81.49 mm, 83.29 mm and 88.92 mm respectively, and the spray angle were 10.9°, 10.8°, 10.7°, 10.4°and 9.8° respectively.

Automotive embedded DCS is widely used to solve automotive control problems. This paper presents a model-driven development technology for such systems. Models of automotive embedded DCS are built up strictly complying with the four-layer-model architecture, which is presented by Model-Driven Architecture (MDA). Three kinds of models are used to describe the protocol data structure, the algorithm process and visualization aspects of automotive embedded DCS. Corresponding XML databases are created based upon these models. As a single data source, these databases play key roles in further development phases, including generating the protocol specification, MC&D systems and embedded programming, etc. Some demonstrative applications are presented in this paper.

This paper proposes a direct nanometer measurement method using the technique of high magnification diffraction imaging. The principles of employing both gratings and speckles are described, in which the illumination distances that generate high magnified and focused images are discussed along with demonstration test results. In addition, the potential extension to sub-nanometer displacement measurement is discussed.

Lane marking detection plays a crucial role in Autonomous Driving Systems or Advanced Driving Assistance System. Vision based lane marking detection technology has been well discussed and put into practical application. LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. We propose a lane marking detection algorithm based on solid-state LiDARs. First a series of data pre-processing operations were done for the solid-state LiDARs with small field of view, and the needed ground points are extracted by the RANSAC method. Then, based on the OTSU method, we propose an approach for extracting lane marking points using intensity information.

Lane line detection is a very critical element for Advanced Driver Assistance Systems (ADAS). Although, there has been significant amount of research dedicated to the detection and localization of lane lines in the past decade, there is still a gap in the robustness of the implemented systems. A major challenge to the existing lane line detection algorithms stems from coping with bad weather conditions (e.g. rain, snow, fog, haze, etc.). Snow offers an especially challenging environment, where lane marks and road boundaries are completely covered by snow. In these scenarios, on-board sensors such as cameras, LiDAR, and radars are of very limited benefit. In this research, the focus is on solving the problem of improving robustness of lane line detection in adverse weather conditions, especially snow. A framework is proposed that relies on using Vehicle-to-Infrastructure (V2I) communication to access reference images stored in the cloud.

Digital Image Correlation (DIC) technology is a powerful tool in the field of experimental mechanics to obtain the full-field deformation/strain information of an object. It has been rapidly applied in industry in recent years. However, for the large-angle full-field strain measurement of small-sized cylindrical objects, it’s still a challenge to the DIC accurate measurement due to its small size and curved surface. In this paper, a measurement method based on the multi-camera DIC system is proposed to study the compressive performance of small-sized cylindrical materials. Three cameras form two stereo DIC measurement systems (1 and 2 cameras, and 2 and 3 cameras), each of which measures a part of the object. By calibrating three cameras at the same time, two stereos DIC coordinate systems can be unified to one coordinate system. Then match the two sets of DIC measurement data together to achieve large-angle measurement of the cylindrical surface.