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A study was conducted using Ford's nine standard CFD calibration models as described in SAE paper 940323. The models are identical from the B-pillar forward but have different back end configurations. These models were created for the purpose of evaluating the effect of back end geometry variations on aerodynamic lift and drag. Detailed experimental data is available for each model in the form of surface pressure data, surface flow visualization, and wake flow field measurements in addition to aerodynamic lift and drag values. This data is extremely useful in analyzing the accuracy of the numerical simulations. The objective of this study was to determine the capability of a digital physics based commercial CFD code, PowerFLOW ® to accurately simulate the physics of the flow field around the car-like benchmark shapes.

Self-piercing rivet (SPR) joints are a key joining technology for lightweight materials, and they have been widely used in automobile manufacturing. Manual visual crack inspection of SPR joints could be time-consuming and relies on high-level training for engineers to distinguish features subjectively. This paper presents a novel machine learning-based crack detection method for SPR joint button images. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges and smooth regions) as training samples. Then, the Artificial Neural Network (ANN) is chosen as the classification algorithm for sub-images. In the training of ANN, three pattern descriptors are proposed as feature extractors of sub-images, and compared with validation samples. Lastly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images.

To assess the fuel consumption of vehicles, three sets of input data are required; drive cycles, vehicle parameters, and environmental conditions. As the first part of a series of studies on real-world fuel consumption, this study focuses on the drive cycles. In principle, drive cycles should represent real-world usage. Some of them aim at a specific usage such as a city driving condition or an aggressive driving style. However, the definition of city or aggressive driving is very subjective and difficult to quantitatively correlate with the real-world usage. This study proposes a methodology to quantify the speed and dynamics of drive cycles, or vehicle speed traces in general, against the real-world usage. After reviewing parameter sets found in other studies, relative cubic speed (RCS) and positive kinetic energy (PKE) are selected to represent the speed and dynamics through energy flow balance at the wheels.

Transient automotive sounds often possess a complex internal structure resulting from one or more impacts combined with mechanical and acoustic cavity resonances. This structure can be revealed by a new technique for obtaining translation-invariant scalograms from orthogonal discrete wavelet transforms. These scalograms are particularly well suited to the visualization of complex sound transients which span a wide dynamic range in time (ms to s) and frequency (∼100Hz to ∼10kHz). As examples, scalograms and spectrograms of door latch closing events from a variety of automotive platforms are discussed and compared in light of the subjective rankings of the sounds.

Lane detection is one of the most important part in ADAS because various modules (i.e., LKAS, LDWS, etc.) need robust and precise lane position for ego vehicle and traffic participants localization to plan an optimal routine or make proper driving decisions. While most of the lane detection approaches heavily depend on tedious pre-processing and great amount of assumptions to get reasonable result, the robustness and efficiency are deteriorated. To address this problem, a novel framework is proposed in this paper to realize robust and real-time lane detection. This framework consists of two branches, where canny edge detection and Progressive Probabilistic Hough Transform (PPHT) are introduced in the first branch for efficient detection.

The objective of this paper is to develop a test capable of ranking lift-gates based on strain concentration levels reflected in fringe characteristics in the known stress/strain concentration and fracture vicinity. First, the system (lift gate glass, adhesive and appliqué) is chosen as test sample since the subsystem (local appliqué) does not exhibit the failure mode observed in the field test. Subsequently, it has been identified that the thermal component (rather than mechanical) is the predominant load by laser scanning vibrometry and confirmed via field test data. Next, digital shearography has been selected as the measurement and visualization tool of strain distribution due to its various advantages such as full field view and non-contact advantages. Finally, the test method has been applied to rank and optimize the structural configuration around appliqués' to reduce / eliminate failure.

As one of the most challenging driving tasks, parking is a common but particularly troublesome problem in large cities. Recently, an excellent solution-automated valet parking (AVP) has become a hot research topic, which allows the driver to leave the vehicle in a drop-off area, while the vehicle driving into the parking slot by itself. For AVP, the precise localization is an indispensable module. However, the global positioning system (GPS) cannot be used in the underground parking lot and the localization method based on lidar is too expensive. In response to solve this problem, we propose a simultaneous localization and mapping system with the semantic information of parking slots (PS-SLAM), which is based on visual-inertial and around view images. First, the calibration of multi-sensors is conducted to obtain their intrinsic and extrinsic parameters. In this way, the around view image and transformation matrices between sensors can be acquired.

A feasible method was developed to reconstruct the three-dimensional flame surface of SI combustion based on 2D images. A double-window constant volume vessel was designed to simultaneously obtain the side and bottom images of the flame. The flame front was reconstructed based on 2D images with a slicing model, in which the flame characteristics were derived by slicing flame contour modeling and flame-piston collision area analysis. The flame irregularity and anisotropy were also analyzed. Two different principles were used to build the slicing model, the ellipse hypothesis modeling and deep learning modeling, in which the ellipse hypothesis modeling was applied to reconstruct the flame in the optical SI engine. And the reconstruction results were analyzed and discussed. The reconstruction results show that part of the wrinkled and folded structure of the flame front in SI engines can be revealed based on the bottom view image.

High-speed video of combustion processes and cylinder pressure traces were obtained from a single-cylinder optical-accessible engine with a production four-valve cylinder head to study the mixture formation and flame propagation characteristics at near-stoichiometric start condition. Laser-sheet Mie-scattering images were collected for liquid droplet distributions inside the cylinder to correlate the mixture formation process with the combustion results. A dual-stream (DS) injector and a quad-stream (QS) injector were used to study the spray dispersion effect on engine starting, under different injection timings, throttle valve positions, engine speeds, and intake temperatures. It was found that most of the fuel under open-valve injection (OVI) conditions entered the cylinder as droplet mist. A significant part of the fuel droplets hit the far end of the cylinder wall at the exhaust-valve side.

The unitized construction Aerostar compact van and wagon models have been engineered to meet a variety of consumer transportation needs. The broad range of functional and image objectives have been attained by traditional design and development programs augmented by new developmental methods and isolation components. State-of-the-art development methodologies applied early in the Aerostar program enabled prediction of the effects of design revisions intended to improve subsystem response characteristics and isolation. Developmental methods used included finite element analysis, modal analysis and synthesis, transmissibility measurements, torsional powertrain measurements, continuous wave laser holography, acoustical mode determination, acoustical intensity mapping and sensitivity studies used to project production ranges of quality.

The around view monitor (AVM) system for vehicles usually suffers from the distortion of surrounding objects caused by incomplete rectification and stitching, which seriously affects the driver's judgment of the surrounding environment during the reversing process. In response to solve this problem, an augmented around view monitor (AAVM) system fusing image and depth information is proposed, which highlights the point clouds of persons or vehicles at the rear of the vehicle. First, an around view image is generated from four fisheye cameras. Then, the calibration of multi TOF cameras is conducted to improve their accuracy of depth estimation and obtain extrinsic camera positions. Next, the 2D-driven object point cloud detection method is proposed to localize and segment object point clouds like vehicles or persons.

A better understanding is needed of the electrostatic rotating bell (ESRB) application of metallic basecoat paint to automobile exteriors in order to exploit their high transfer efficiency without compromising the coating quality. This paper presents the initial results from experimental investigation of sprays from an ESRB which is designed to apply water-borne paint. Water was used as paint surrogate for simplicity. The atomization and transport regions of the spray were investigated using laser light sheet visualizations and phase Doppler particle analyzer (PDPA). The experiments were conducted at varying levels of the three important operating parameters: liquid flow rate, shaping-air flow rate, and bellcup rotational speed. The results show that bellcup speed dominates atomization, but liquid and shaping-air flow rate settings significantly influence the spray structure. The visualization images showed that the atomization occurs in ligament breakup regime.

Polyoxymethylene dimethyl ethers (PODEn) have high cetane number, high oxygen content and high volatility, therefore can be added to gasoline to optimize the performance and soot emission of Gasoline Compression Ignition (GCI) combustion. High speed imaging was used to investigate the spray and combustion process of gasoline/PODEn blends (PODEn volume fraction 0%-30%) under various ambient conditions and injection strategies in a constant volume vessel. Results showed that with an increase of PODEn proportion from 10% to 30%, liquid-phase penetration of the spray increased slightly, ignition delay decreased from 3.8 ms to 2.0 ms and flame lift off length decreased 29.4%, causing a significant increase of the flame luminance. For blends with 20% PODEn, when ambient temperature decreased from 893 K to 823 K, the ignition delay increased 1.3 ms and the flame luminance got lower.

Intelligent driving, aimed for collision avoidance and self-navigation, is mainly based on environmental sensing via radar, lidar and/or camera. While each of the sensors has its own unique pros and cons, camera is especially good at object detection, recognition and tracking. However, unpredictable environmental illumination can potentially cause misdetection or false detection. To investigate the influence of illumination conditions on detection algorithms, we reproduced various illumination intensities in a photo-realistic virtual world, which leverages recent progress in computer graphics, and verified vehicle detection effect there. In the virtual world, the environmental illumination is controlled precisely from low to high to simulate different illumination conditions in the driving scenarios (with relative luminous intensity from 0.01 to 400). Sedan cars with different colors are modelled in the virtual world and used for detection task.

Hardware benchmarking of body overall stiffness and joint stiffness of the best-in-class competitive vehicles is a common practice in the automobile industry. However, this process does not provide design insights of competitive body structures, which relate stiffness performance to key architectural designs. To overcome this drawback, a CAD body-in-prime model of a competitive vehicle is developed using laser/optical scanning technology and a corresponding CAE model is built based on the CAD data. A deep-dive structural efficiency study is conducted using this model and “pros” and “cons” of the architectural design of each individual joint and each section of major load-carrying members of this body structure are identified. This analytical benchmarking (or reverse engineering) process enables a company to adopt best-in-class design practices and achieve competitive advantages in vehicle designs.

Today, automated vehicles mostly rely on ego vehicle sensors such as cameras, radar or LiDAR sensors that are limited in their sensing capability and range. Vehicle-to-everything (V2X) communication has the potential to appropriately complement these sensors and even allow for a cooperative, proactive interaction of vehicles. As such, V2X communication might play a vital role on the way to smart and efficient traffic solutions. In the public funded research project UK Autodrive, we are currently investigating and experimentally evaluating V2X-based applications based on dedicated short range communication (DSRC). Moreover, the novel application intersection priority management (IPM) is part of the research project. IPM aims at automating intersections in such a way that vehicles can pass safely and even more efficiently without the use of traffic lights or signs.

A collaborative robot or cobot is a robot that can safely and effectively interact with human workers while performing industrial tasks. The ability to work alongside humans has increased the importance of collaborative robots in the automation industry, as this unique feature is a much needed property among robots nowadays. Rethink Robotics has pioneered this unique discipline by building many robots including the Baxter Robot which is exclusive not only because it has collaborative properties, but because it has two arms working together, each with 7 Degrees Of Freedom. The main goal of this research is to validate the kinematic equations for the Baxter collaborative robot and develop a unified reconfigurable kinematic model for the Left and Right arms so that the calculations can be simplified.

The around view monitor (AVM) system for the long-body road vehicle with multiple articulated carriages usually suffers from the incomplete distortion rectification of fisheye cameras and the irregular image stitching area caused by the change of relative position of the cameras on different carriages while the vehicle is in motion. In response to these problems, a set of calibration and stitching methods of AVM are proposed. First, a radial-distortion-based rectification method is adopted and improved. This method establishes two lost functions and solves the model parameters with the two-step optimization method. Then, AVM system calibration is conducted, and the perspective transformation matrix is calculated. After that, a static basic look-up table is generated based on the distortion rectification model and perspective transformation matrix.

The direct injection gasoline spray-wall interaction was characterized inside a heated pressurized chamber using various visualization techniques, including high-speed laser-sheet macroscopic and microscopic movies up to 25,000 frames per second, shadowgraph, and doublespark particle image velocimetry. Two hollow cone high-pressure swirl injectors having different cone angles were used to inject gasoline onto a heated plate at two different impingement angles. Based on the visualization results, the overall transient spray impingement structure, fuel film formation, and preliminary droplet size and velocity were analyzed. The results show that upward spray vortex inside the spray is more obvious at elevated temperature condition, particularly for the wide-cone-angle injector, due to the vaporization of small droplets and decreased air density. Film build-up on the surface is clearly observed at both ambient and elevated temperature, especially for narrow cone spray.

Double-hole nozzle and multiple injections have the potential for better fuel atomization and mixing in DI engine. In order to evaluate the behavior of the spray for the double-hole nozzles against traditional single-hole ones, high-speed spray visualization was carried out using a streak film camera and a copper vapor laser, and in combination with a long-distance camera when taking microscopic movies. The spray penetration and the cone angle were measured based on the images and compared for variable injection pressures, and for single and split injections, under ambient and elevated chamber pressure conditions. The results showed that the spray of the double-hole nozzle has comparable penetration but smaller cone angle when viewed from the nozzle end, compared to the single-hole nozzle with the same total hole discharge cross-sectional area. For microscopic view, it was observed that the interaction between the dual sprays is very dynamic.