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Driving comfort and automotive product quality are strongly associated with the vibration that is transmitted to the occupants of a vehicle at the points of contact to the human body, including the seat, steering wheel, and pedals. Of these three contact locations, the seats have the most general importance, as all occupants of a vehicle experience seat vibration. Particularly relevant to driving comfort is the way in which vehicle occupants perceive seat vibration, which may be different than expected considering sensor measured vibration levels. Much of the interest in seat vibration has been focused on internal combustion engine powertrain vibration, especially idle vibration. However, electrification of vehicles changes the focus from low frequency idle vibration to higher frequency vibration sources.

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.

Forward collision warning is one of the most challenging concerns in the safety of autonomous vehicles. A cooperation between many sensors such as LIDAR, Radar and camera helps to enhance the safety. Apart from the importance of having a reliable object detector, the safety system should have requisite capabilities to make reasonable decisions in the moment. In this work, we concentrate on detecting front vehicles of autonomous cars using a monocular camera, beyond only a detection method. In fact, we devise a solution based on a cooperation between a deep object detector and a reinforcement learning method to provide forward collision warning signals. The proposed method models the relation between acceleration, distance and collision point using the area of the bounding box related to the front vehicle. An agent of learning automata as a reinforcement learning method interacts with the environment to learn how to behave in eclectic hazardous situations.

As advanced vehicle controls and autonomy become mainstream in the automotive industry, the need to employ traditional mathematical models and control strategies arises for the purpose of simulating autonomous vehicle handling maneuvers. This study focuses on lane change maneuvers for autonomous vehicles driving at low speeds. The lane change methodology uses PID (Proportional-Integral-Derivative) controller to command the steering wheel angle, based on the yaw motion and lateral displacement of the vehicle. The controller was developed and tested on a bicycle model of an electric vehicle (a Chevrolet Bolt 2017), with the implementation done in MATLAB/Simulink. This simple mathematical model was chosen in order to limit computational demands, while still being capable of simulating a smooth lane change maneuver under the direction of the car’s mission planning module at modest levels of lateral acceleration.

The fatal automobile accidents can be attributed to fatigued and distracted driving by drivers. Driver Monitoring Systems alert the distracted drivers by raising alarms. Most of the image based driver drowsiness detection systems face the challenge of failure proof performance in real time applications. Failure in face detection and other important part (eyes, nose and mouth) detections in real time cause the system to skip detections of blinking and yawning in few frames. In this paper, a real time robust and failure proof driver drowsiness detection system is proposed. The proposed system deploys a set of detection systems to detect face, blinking and yawning sequentially. A robust Multi-Task Convolutional Neural Network (MTCNN) with the capability of face alignment is used for face detection. This system attained 97% recall in the real time driving dataset collected. The detected face is passed on to ensemble of regression trees to detect the 68 facial landmarks.

Lean applications in product development usually start with manufacturing due to the relative experience of measuring improvements and identifying wastes in physical settings. The full potential of lean implementation in any product development, however, can only be realized when applied throughout the process, starting with early process. Considering that the first and most essential principle in lean implementation is the characterization of value from the customer's perspective, it is imperative that the proper definition of value is realized at the beginning of the process. In addition, streaming and flowing of this customer's specified value should be realized throughout the process from start to finish. This paper discusses the application of lean principles to integrated design and manufacturing phases of the Product Development Process.

Typically, when someone needs to perform occasional towing tasks, such as towing a boat on a trailer, they have two choices. They can either purchase a larger, more powerful vehicle than they require for their regular usage, or they can rent a larger vehicle when they need to tow something. In this project, we propose a third alternative: a trailer with an on-board power supply, which can be towed by a small vehicle. This system requires a means of sensing how much power the trailer's power supply should provide, and an appropriate control system to provide this power. In this project, we design and model the trailer, a standard small car, and the control system, and evaluate the concept's feasibility. We have selected a suitable power source for the trailer, a DC motor, coupled directly to the trailer's single drive wheel, which allow us to dispense with the need for a differential.

The objective of this study was to determine risk of injury to the driver during a frontal impact in a Formula SAE vehicle. Formula SAE is a collegiate student design competition where every year universities worldwide build and compete with open-wheel formula-style race cars. Formula SAE 2006 rules stipulate the use of an impact attenuator to absorb energy in the event of a frontal impact. These rules mandated an average deceleration not to exceed 20-g from a speed of 7.0 m/s (23 ft/s), but do not specify a specific time or pulse shape of the deceleration. The pulse shapes tested in this study included an early high-g, constant-g, and late high-g pulse. The tests were performed using the deceleration sled at the Kettering University Crash Safety Center. Using industry standard practices, this study examined the driver's risk of injury with regard to neck and femur loads, head and chest accelerations, as well as kinematic analysis using high speed video.

FMVSS 223 and 224 set standards for “Rear Impact Protection” for trailers and semi-trailers with a gross weight rating greater than 10000 pounds. A limited amount of experimental data is available for evaluating the different performance attributes of rear impact guards. The crash tests are usually limited to fixed parameters such as impact speed, guard height, strength and energy absorption, etc. There also seems to be some misunderstanding of the interdependence of guard strength and energy absorption, and their combined effect on the guard's ability to limit underride while keeping occupant acceleration forces in a safe range. In this paper, we validated the Finite Element (FE) model of an existing rear impact guard against actual FMVSS 223 tests. We also modified a previously evaluated FE model of a 1990 Ford Taurus by updating its hood geometry and material properties.

Rollover crashes are responsible for a significant proportion of traffic fatalities each year, while they represent a relatively small proportion of all motor vehicle collisions. The purpose of this study was to focus on rollover events from an occupant's perspective to understand what type of industry test method, ATD, computer based model, and injury assessment measures are required to provide occupant protection during rollovers. Specific injuries most commonly experienced in rollovers along with the associated injury sources were obtained by review of 1998-2000 NASS-CDS records. These data suggest that models capable of predicting the likelihood of brain injuries, specifically subarachnoid and subdural hemorrhage, are desirable. Ideally, the model should also be capable of predicting the likelihood of rib fractures, lung contusions and shoulder (clavicular and scapular) fractures, and facet, pedicle, and vertebral body fractures in the cervical spine.

A variety of test methodologies currently exist to assess the propensity of a vehicle to roll laterally, the vehicle performance during a rollover event, and the associated risk of injury to the occupant. There are indications as to which tests are appropriate when attempting to replicate rollover events observed in the field. Due to the complexity of a rollover, test repeatability is a concern as well as cost, and field relevance. Since revisions to governmental rollover regulations are currently being considered, an assessment of currently available rollover test methodologies would provide a context to compare the different experimental designs. Additionally, the design of injury prevention strategies such as side air curtains, 4-point belts, etc. will also require the establishment of repeatable, robust, and economical test methods.

While many current vehicle network systems for body bus applications use event triggered analysis processes, the deterministic point of view raises concerns about system timing due to message latency. This paper studies the latency performance characteristics of a typical body bus vehicle network using event triggered analysis over the CAN bus.

This paper documents the electrical infrastructure design of a Hybrid Go Kart competition vehicle which includes a dual Fuel Cell power system, Ultra Capacitors for energy storage, and a dual AC induction motor capable of independent drive. The Kart was built primarily to compete in the 2009 Formula Zero international event. This paper emphasized the vehicle model and control strategy as a result of three (3) graduate student research projects. The vehicle was fabricated and tested but did not participate in the race competition since the race organization folded. The vehicle model was developed in Simulink to determine whether the fuel cell and ultra-capacitor combination will be sufficient for peak transient power requirement of 14 kW. The vehicle’s functional description and performance specifications are documented including the integration of the fuel cell power modules, energy storage system, power converters, and AC motor and motor controllers.

Doors inside an automotive HVAC module are essential components to ensure occupant comfort by controlling the cabin temperature and directing the air flow. For temperature control, the function of a door is not only to close/block the airflow path via the door seal that presses against HVAC wall, but also control the amount of hot and cold airflow to maintain cabin temperature. To meet the stringent OEM sealing requirement while maintaining a cost-effective product, a “V-Shape” soft rubber seal is commonly used. However, in certain conditions when the door is in the position other than closed which creates a small gap, this “V-Shape” seal is susceptible to the generation of objectionable whistle noise for the vehicle passengers. This nuisance can easily reduce end-customer satisfaction to the overall HVAC performance.

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.

Lane marks are an important aspect for autonomous driving. Autonomous vehicles rely on lane mark information to determine a safe and legal path to drive. In this paper an approach to estimate lane lines on straight or slightly curved roads using a LiDAR unit for autonomous vehicles is presented. By comparing the difference in elevation of LiDAR channels, a drivable region is defined. The presented approach used in this paper differs from previous LiDAR lane line detection methods by reducing the drivable region from three to two dimensions exploring only the x-y trace. In addition, potential lane markings are extracted by filtering a range of intensity values as opposed to the traditional approach of comparing neighboring intensity values. Further, by calculating the standard deviation of the potential lane markings in the y-axis, the data can be further refined to specific points of interest.

This paper describes experiments conducted to determine the effect of multiple spark discharge ignition systems and spark plug electrode design on cold start performance of a dedicated E85 fueled vehicle. Tests were conducted using three different ignition configurations: OEM ignition and spark plugs, multiple spark discharge ignition with OEM spark plugs, and multiple spark discharge ignition with large gap circular electrode spark plugs. The multiple spark discharge ignition with OEM spark plugs showed a significant improvement in cold start performance over the OEM ignition, but the addition of the circular electrode spark plugs caused a decrease in cold start performance. The circular ground spark plugs did produce a higher ending coolant temperature than either of the other configurations.

Cylinder air/fuel ratio distribution is an important factor affecting the economy, power, vibration, and emissions of an internal combustion engine. Currently, production automobiles utilize an exhaust gas sensor located in the main exhaust stream in order to regulate air/fuel mixtures. By measuring the oxygen content of the exhaust gas for each cylinder independently, the degree of air/fuel variation between cylinders can be determined. This information can be used to determine the mixture quality of specific cylinders. Knowing these variances can lead to design changes in the intake and exhaust manifolds as well as better control of fuel metering which will improve the output of the engine. This study was carried out using a 1991 3.8L Buick V-6 engine with customized exhaust manifolds utilizing exhaust gas oxygen sensors for each cylinder in addition to the sensor located in the main combined exhaust gas stream. Production level, ZrO2 sensors were used for this experimental study.

This paper describes the experiments conducted to determine the effect of high energy multiple spark discharge (MSD) ignition systems and spark plug electrode design, on the cold start performance of a vehicle which was converted for dedicated operation on E85 (a blend of 85% ethanol and 15% gasoline) fuel. Tests were conducted using three different ignition configurations; original equipment manufacturer (OEM) ignition and spark plugs, high energy multiple spark discharge (MSD) ignition with OEM, J-type spark plugs, and high energy MSD ignition with surface gap electrode spark plugs. The high energy MSD ignition with OEM spark plugs showed a significant improvement in cold start performance over the OEM ignition. The addition of the surface gap spark plugs caused a decrease in cold start performance. Despite this, the surface gap spark plugs produced higher ending coolant temperature than the other configurations.

The safety of ground vehicles is a matter of critical importance. Vehicle safety is enhanced with the use of control systems that mitigate the effect of unachievable demands from the driver, especially demands for tire forces that cannot be developed. This paper presents the results of a smart tire prototyping and validation study, which is an investigation of a smart tire system that can be used as part of these mitigation efforts. The smart tire can monitor itself using in-tire sensors and provide information regarding its own tire forces and moments, which can be transmitted to a vehicle control system for improved safety. The smart tire is designed to estimate the three orthogonal tire forces and the tire aligning moment at least once per wheel revolution during all modes of vehicle operation, with high accuracy. The prototype includes two in-tire piezoelectric deformation sensors and a rotary encoder.