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Forward Collision Alert (or Forward Collision Warning) systems provide alerts intended to assist drivers in avoiding or mitigating the harm caused by rear-end crashes. These systems currently use front-grille mounted, forward-looking radar devices as the primary sensor. In contrast, Lane Departure Warning (LDW) systems employ forward-looking cameras mounted behind the windshield to monitor lane markings ahead and warn drivers of unintended lane violations. The increasing imaging sensor resolution and processing capability of forward-looking cameras, as well recent important advances in machine vision algorithms, have pushed the state-of-the-art for camera-based features. Consequently, camera-based systems are emerging as a key crash avoidance system component in both a primary and supporting sensing role. There are currently no production vehicles with cameras used as the sole FCA sensing device.

In recent years, there has been a sustained effort by the automotive OEMs and suppliers to improve the vehicle driveline efficiency. This has been in response to customer demands for greater vehicle fuel economy and increasingly stringent government regulations. The automotive rear axle is one of the major sources of power loss in the driveline, and hence represents an area where power loss improvements can have a significant impact on overall vehicle fuel economy. Both the friction induced mechanical losses and the spin losses vary significantly with the operating temperature of the lubricant. Also, the preloads in the bearings can vary due to temperature fluctuations. The temperatures of the lubricant, the gear tooth contacting surfaces, and the bearing contact surfaces are critical to the overall axle performance in terms of power losses, fatigue life, and wear.

Liquid and vapor envelopes of sprays from a multi-hole fuel injector operating under closely-spaced double-injection conditions were investigated using a combination of high-speed schlieren and Mie scattering imaging. The effects of mass split ratio and dwell time between injections on liquid and vapor penetration have been investigated under engine-like pressures and temperatures. For the conditions evaluated, the results indicate that closely-spaced double-injection generally reduces liquid and vapor penetration.

This study is presenting a comparative spray study of modern large bore medium speed diesel engine common rail injectors. One subject of paper is to focus on nozzles with same nominal flow rate, but different machining. The other subject is penetration velocity measurements, which have a new approach when trying to understand the early phase of transient spray. A new method to use velocimetry for spray tip penetration measurements is here introduced. The length where spray penetration velocity is changed is found. This length seems to have clear connection to volume fraction of droplets at gas. These measurements also give a tool to divide the development of spray into acceleration region and deceleration region, which is one approach to spray penetration. The measurements were performed with backlight imaging in pressurized injection test rig at non-evaporative conditions. Gas density and injection pressure were matched to normal diesel engine operational conditions.

Non-evaporating diesel sprays have been simulated utilizing the ETAB and the WAVE atomization and breakup models and have been compared with experimental data. The experimental penetrations and widths were determined from back-lit spray images and the droplet sizes have been measured by means of a Malvern particle sizer. The model evaluation criteria include the spray penetration, the spray width and the local droplet size. The comparisons have been performed for variations of the injection pressure, the gas density and the fuel viscosity. The fuel nozzle exit velocities used in the simulations have been computed with a special code that considers the effect of in-nozzle cavitation. The simulations showed good overall agreement with experimental data. However, the capabilities of the models to predict the droplet size for different fuels could be improved.

The engineering of electric propulsion systems requires time and cost efficient methodologies to determine system characteristics as well as potential component integration issues. A significant part of this analysis is the identification of the electromagnetic fields present in the propulsion system. Understanding of the electromagnetic fields during system operation is a significant design consideration due to the use of components that require large current(s) and high voltage(s) in the proximity of other control system items (such as sensors) that operate with low current(s) and voltage(s). Therefore, it is critical to quantify the electromagnetic fields produced by these components within the design and how they may interact with other system components. Often overlooked (and also extremely important) is an evaluation of how the overall system architecture can generate or react to electromagnetic fields (which may be a direct result of packaging approaches).

General Motors (GM) and the Virginia Tech Transportation Institute (VTTI) have partnered to conduct a series of studies characterizing the use and effectiveness of technologies designed to assist drivers while backing. A major emphasis of this research has been on Rear Vision Camera (RVC) systems that provide drivers with an enhanced view of the area behind the vehicle. RVC systems are intended to aid in positioning the vehicle when executing low-speed parking and backing-related tasks and are not necessarily well suited for detecting unexpected in-path obstacles (particularly if the RVC image is not coupled with object detection alerts issued to the driver).

High-speed (12 kHz) imaging of combustion luminosity (enhanced by using a sodium fuel additive) has been analyzed and compared to crank angle resolved heat release rates and mass fraction burn profiles in a spray-guided spark-ignited direct-injection (SG-SIDI) optical single-cylinder engine. The addition of a sodium-containing additive to gasoline greatly increases the combustion luminosity, which allows unintensified high-speed (12 kHz) imaging of early partially premixed flame kernel growth and overall flame propagation with excellent signal-to-noise ratio for hundreds of consecutive engine cycles. Ignition and early flame kernel growth are known to be key to understanding and eliminating poor burn cycles in SG-SIDI engines.

In quasi-static tension and compression tests of thermoplastics, full-field strain distribution on the gage section of the specimen can be captured using the two-dimensional digital image correlation method. By loading the test specimens made of a talc-filled and impact-modified polypropylene up to tensile failure and large compressive strains, this study has revealed that inhomogeneous deformation within the gage section occurs quite early for both test types. This leads to the challenge of characterizing the mechanical properties - some mechanical properties such as stress-strain relationship and fracture strain could depend on the measured section length and location. To study this problem, the true stress versus true strain curves determined locally in different regions within the gage length are compared.

The projected frontal area of a vehicle has a significant impact on aerodynamic drag, and thus is an important parameter, for vehicle development, benchmarking, and modeling. However, determining vehicle frontal area can be tedious, time consuming, expensive, or inaccurate. Existing methods include analysis of engineering drawings, vehicle projections, 3D scanners, planimeter measurements from photographs, and estimations using vehicle dimensions. Currently accepted approximation methods can be somewhat unreliable. This study focuses on introducing a method to find vehicle frontal area using digital images and subtraction functions via MATLABs' Image Processing Toolbox. In addition to an overview of the method, this paper describes several variables that were examined to optimize and improve the process such as camera position, surface glare, and vehicle shadow effects.

Object avoidance for autonomous driving is a vital factor in safe driving. When a vehicle travels from any random start places to any target positions in the milieu, an appropriate route must prevent static and moving obstacles. Having the accurate depth of each barrier in the scene can contribute to obstacle prevention. In recent years, precise depth estimation systems can be attributed to notable advances in Deep Neural Networks and hardware facilities/equipment. Several depth estimation methods for autonomous vehicles usually utilize lasers, structured light, and other reflections on the object surface to capture depth point clouds, complete surface modeling, and estimate scene depth maps. However, estimating precise depth maps is still challenging due to the computational complexity and time-consuming process issues. On the contrary, image-based depth estimation approaches have recently come to attention and can be applied for a broad range of applications.

Vehicles are in contact with the road surface through tires, and the interaction at the tire-road interface is usually the major source of vibrations that is experienced by the passengers in the vehicle. Thus, it is critical to measure the vibrational characteristics of the tires in order to improve the safety and comfort of the passengers and also to make the vehicle quieter. The measurement results can also be used to validate numerical models. In this paper, Digital Image Correlation (DIC) as a non-contact technique is used to measure the dynamics of a racing tire in static and rolling conditions. The Kettering University FSAE car is placed on the dynamometer machine for this experiment. A pair of high-speed cameras is used to capture high-resolution images of the tire in a close-up view. The images are processed using DIC to obtain strain and displacement of the sidewall of the tire during rolling. The experiment is performed for various testing speeds.

The automotive and aerospace industries are increasingly using the light-weight material to improve the vehicle performance. However, using light-weight material can increase the airborne and structure-borne noise. A special attention needs to be paid in designing the structures and measuring their dynamics. Conventionally, the structure is excited using an impulse hammer or a mechanical shaker and the response is measured using uniaxial or multi-axial accelerometers to obtain the dynamics of the structure. However, using contact-based transducers can mass load the structure and provide data at a few discrete points. Hence, obtaining the true dynamics of the structure conventionally can be challenging. In the past few years, stereo-photogrammetry and three-dimensional digital image correlation have received special attention in collecting operating data for structural analysis. These non-contact optical techniques provide a wealth of distributed data over the entire structure.

Objection detection using a camera sensor is essential for developing Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) vehicles. Due to the recent advancement in deep Convolution Neural Networks (CNNs), object detection based on CNNs has achieved state-of-the-art performance during daytime. However, using an RGB camera alone in object detection under poor lighting conditions, such as sun flare, snow, and foggy nights, causes the system's performance to drop and increases the likelihood of a crash. In addition, the object detection system based on an RGB camera performs poorly during nighttime because the camera sensors are susceptible to lighting conditions. This paper explores different pedestrian detection systems at low-lighting conditions and proposes a sensor-fused pedestrian detection system under low-lighting conditions, including nighttime. The proposed system fuses RGB and infrared (IR) thermal camera information.