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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.

Government accident statistics show that approximately 35% of all car accident victims suffer an injury to the head and face. Such injuries are common during frontal, side, and rollover accidents as the head may impact the steering wheel, side pillars, windshield, or roof. Further, non-threatening injuries (i.e abrasions) may be suffered due to contact with the deployed airbag, or, in the case of an out-of-position occupant, a deploying airbag. While the forces and accelerations measured internal to the head are known to correlate with serious head injury (i.e. concussion, skull fracture, diffuse axonal injury), it is currently not possible to record how the loads are distributed over the head and face with the current ATD. Ultimately, such data could eventually be used to provide improved resolution as to the probability of superficial, soft tissue damage since past cadaver studies show that the distribution of contact pressures are related to such injuries.

Pending reductions in light duty vehicle PM emissions standards from 10 to 3 mg/mi and below will push the limits of the gravimetric measurement method. At these levels the PM mass collected approaches the mass of non-particle gaseous species that adsorb onto the filter from exhaust and ambient air. This introduces an intrinsic lower limit to filter based measurement that is independent of improvements achieved in weighing metrology. The statistical variability of back-up filter measurements at these levels makes them an ineffective means for correcting the adsorption artifact. The proposed subtraction of a facility based estimate of the artifact will partially alleviate the mass bias from adsorption, but its impact on weighing variability remains a problem that can reach a significant fraction of the upcoming 3 and future 1 mg/mi standards. This paper proposes an improved PM mass method that combines the gravimetric filter approach with real time aerosol measurement.

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.

With many advantages of GDI technology, one major disadvantage is high HC emissions. The primary goal of this study is to determine the optimum values of engine parameters that would result in maximum power output from a GDI engine, with complete combustion, minimum hydrocarbon (HC) emissions, and minimum specific fuel consumption. A two-dimensional engine geometry with a piston-bowl was selected for faster engine CFD simulations. The first part involves a study of the affect of engine parameters on performance and HC emissions. The parameters considered were, equivalence ratio (mass of injected fuel), injection timing, ignition timing, engine RPM, spray cone angle, and velocity of fuel injection. The second part of the study involves determining the optimum values of fuel mass injected, injection timing, and ignition timing in order to maximize power output while limiting the amount of fuel left unburned after the end of the expansion process.

A numerical and experimental study of the use of air motion control, piston bowl shape, and injector configuration on combustion and emissions in diesel engines has been conducted. The objective of this study is to investigate the use of flow control within the piston bowl during compression to enhance fuel air mixing to achieve a uniform air-fuel mixture to reduce soot and NO emissions. In addition to flow control different piston bowl geometries and injector spray angles have been considered and simulated using three-dimensional computational fluid dynamics and experiments. The results include cylinder pressure and emissions measurements and contour plots of fuel mass fraction, soot, and NO. The results show that soot and NO emissions can be reduced by proper flow control and piston bowl design.

Impinging jets are an established technique for obtaining high local heat transfer coefficients between a fluid and a surface. Factors such as jet attachment, surface angle, jet angle, separation distance between jet orifice and surface of impingement, and trajectory influence heat transfer dramatically. In the current study, the specific application of interest is air issuing from the defroster's nozzles of a vehicle and impinging on a glass windshield. The current work is aimed at studying the flow patterns off a vehicle windshield as a result of air issuing from various nozzle configurations. The effects of openings' geometry (circular vs rectangular), number of openings, angle that the windshield makes with the horizontal plane and angle of impinging jet, on windshield heat transfer is examined. An optimal configuration will be recommended for better heat transfer.

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.

Prior experimental and field studies have demonstrated an increased risk of upper extremity fracture due the deployment of frontal airbags. The experimental studies provide valuable insight as to likely injury mechanisms; namely, increasing proximity increases the risk of forearm fracture. Still, field data is needed to validate these experimental findings. The available field data has largely been derived from direct case study analysis or a review of government accident statistics. In both cases, the datasets were comprised solely of pre-1995 era vehicles. Such data represents early generation airbag designs and there has been little additional study in this area. In addition, there has been an absence of fracture pattern analyses as a function of airbag deployment and non-deployment. Such an analysis would help elucidate the role of the deploying airbag on upper extremity fracture in the current fleet.

A hardware and software architecture suitable for a safety-critical steer-by-wire systems is presented. The architecture supports three major failure modes and features several safety protocols and mechanisms. Failures due to component failures, software errors, and human errors are handled by the architecture and safety protocols. A test implementation using replicated communication channels, controllers, sensors, and actuators has been performed. The test implementation uses the CAN protocol, Motorola S12 microcontrollers, and Microchip MCP250XX components with a steering wheel and road wheel simulator. The focus of the paper is on the application level, using system engineering principles which incorporate a holistic approach to achieve safety at various levels.

Analysis of the National Automotive Sampling System (NASS) data reveals that vehicle rollover accidents account for a relatively a small number of accidents, but the associated frequency of serious injury is high compared to frontal or side impact. These data demonstrate the apparently elevated probability of head and neck injury during rollover, with head injury occurring more frequently, injured 4.5 times more frequently than the neck when considering all injuries. Automotive industry researchers have performed numerous rollover tests with instrumented ATD's and have predicted an elevated probability of neck injury with little chance of head injury. This contradicts field data (NASS-CDS) which suggests a high frequency of head injury with little chance of neck injury. This difference may be explained in part, through the different volumes of data presented in the literature.

Bond graph modeling is a powerful technique to study the complex interactions occurring between various components in a system. A few investigations were carried out to study vehicle dynamics using Bondgraphs, but are limited to 2 degree of freedom systems [1,2&3]. In this work, a 4-DOF-vehicle model was developed using bond graphs. A frequency response analysis was also carried out to study the natural frequencies. This model was later validated using Lagrangian principles. The results correlated well for a typical passenger car using the manufacturer supplied information available in the public domain.

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.

Vehicle Dynamics play an important role in responsiveness of a vehicle. The performance of a vehicle depends on its ride and handling characteristics [1]. Handling is a measure of the directional response of a vehicle and one of the important characteristics from the vehicle dynamics point of view. The directional response of a vehicle depends on the dynamics of the steering system. A good steering control provides an accurate feedback about how the vehicle reacts to the road. In this paper, the powerful techniques of Bond graphs and state equations [2] are used to design and analyze the dynamics of a manual rack and pinion steering system. The author obtains the transfer function between the Angle of rotation of front tire and the Angle of rotation of steering wheel. The overall steering ratio of the bond graph modeled steering system is compared with the overall ratio of a similar vehicle to validate the model.

Numerous studies have documented that lower extremity injury is second only to the head and face in automotive accidents. Such injuries are common because the lower extremity is typically the first point of contact between the occupant and the car interior. Of all lower extremity injuries, the knee is the most common site of trauma. This typically results from high speed contact with the instrument panel which can produce fracture and subfracture (contusions, lacerations, abrasions) level injuries. Current Federal safety guidelines use a bone fracture criterion which is based solely on a peak load. The criterion states that loads exceeding 10 kN will likely result in gross bone fracture. However, cadaver experiments have shown that increased contact area (via padding) over the knee can significantly increase the amount of load that can be tolerated before fracture or subfracture injury.

The objective of this work was to design an automated bed clearing mechanism for the Anet brand A8 3D printer, which uses Fused Deposition Modeling (FDM) process. This work has been carried out as a capstone course. Many OEMs are focusing on using functional 3D printed parts to replace metal parts that otherwise require complex assemblies or to reduce weight. The concept behind the work presented in this paper was to allow every user to be able to print multiple parts without human interaction. This saves time to load and unload one part at a time. The idea was to develop a universal bed clearing mechanism that can be used for most brands of 3D printers. Upon researching into the many different styles and designs of printers, it became clear that the designs are different and complex to create a universal product. It was decided to aim for the most common style of 3D printers for which easy modeling and testing should be possible.

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.

Steer-by-wire is a system where there are no mechanical connections between the steering wheel and the tires. With the inception of electric and hybrid cars, steer-by-wire is becoming more common. A steer-by-wire car opens many opportunities for additional feedback on the steering wheel. Providing haptic feedback through the steering wheel will add additional depth and capabilities to make the driving experience safer. In this paper we investigated the effects of force feedback on the steering wheel in order to detect and/or avoid blind spot collisions. Two types of force feedback are examined using a driving simulator: a rumble and a counter steering force. A rumble on the steering wheel can avoid blind-spot accidents by providing feedback to drivers about vehicles in their blind spots. Providing counter steering force feedback can help in the reduction in blind-spot accidents. The results show that adding counter steering force feedback did reduce blind-spot related collisions.

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.

The “Range-of Motion of the Cervical Spine of Children” study is a collaboration between Kettering University and McLaren Regional Medical Center in Flint, Michigan to quantify and establish benchmarks of “normal” range of motion (ROM) in children. The results will be analyzed to determine mean and standard deviation of degrees of rotation and used to improve the occupant protection in motor vehicles, sports equipment and benefits of physical therapy. The data will be invaluable in the development of computational models to analyze processes involving children in motion.