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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.

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.

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.

As automated driving becomes more common, simulation of vehicle dynamics and control scenarios are increasingly important for investigating motion control approaches. In this work, a study of the differences between the Pure Pursuit and Stanley autonomous vehicle controllers, based on vehicle dynamics responses, is presented. Both are geometric controllers that use only immediate vehicle states, along with waypoint data, to control a vehicle’s future direction as it proceeds from point to point, and both are among the most popular lateral controllers in use today. The MATLAB Automated Driving Toolbox is employed to implement and virtually test the Pure Pursuit and Stanley lateral controllers in different driving scenarios. These include low intensity scenarios such as city driving, and emergency maneuvers such as the moose test.

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.

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.

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.

Data-driven modeling can help improve understanding of the governing equations for systems that are challenging to model. In the current work, the Sparse Identification of Nonlinear Dynamical systems (SINDy) is used to predict the dynamic behavior of dynamic problems for NVH applications. To show the merit of the approach, the paper demonstrates how the equations of motions for linear and nonlinear multi-degree of freedom systems can be obtained. First, the SINDy method is utilized to capture the dynamic behavior of linear systems. Second, the accuracy of the SINDy algorithm is investigated with nonlinear dynamic systems. SINDy can output differential equations that correspond to the data. This method can be used to find equations for dynamical systems that have not yet been discovered or to study current systems to compare with our current understanding of the dynamical system.

Rapid changes in the automotive industry, including the growth of advanced vehicle controls and autonomy, are driving the need for more dedicated proving ground spaces where these systems can be developed safely. To address this need, Kettering University has created the GM Mobility Research Center, a 21-acre proving ground located in Flint, Michigan at the former “Chevy in the Hole” factory location. Construction of a proving ground on this site represents a beneficial redevelopment of an industrial brownfield, as well as a significant expansion of the test facilities available at the campus of Kettering University. Test facilities on the site include a road course and a test pad, along with a building that has garage space, a conference room, and an indoor observation platform. All of these facilities are available to the students and faculty of Kettering University, along with their industrial partners, for the purpose of engaging in advanced transportation research and education.

There are many opportunities in a current automotive HVAC case for improved performance, and cost savings. Based on these opportunities, a new HVAC case design has been developed. This new design is smaller and lighter than current cases while meeting many of the performance requirements. The case also features a unique plenum design for air distribution to the three modes, panel, floor, and defrost. The results of simulation and laboratory testing confirmed the concept of the new HVAC design.

A comprehensive finite element methodology is developed to predict the compressible flow performance of a non-symmetric 7-blade axial flow fan, and to quantify the source strength and sound pressure levels at any location in the system. The acoustic and flow performances of the fan are predicted simultaneously using a computational aero-acoustic technique combining transient flow analysis and noise propagation. The calculated sound power levels compare favorably with the measured sound power data per AMCA 300-96 code.

The kinematics and kinetics of a seven degree of freedom vehicle ride model with independent front and rear suspension are developed. Lagrange's equation is used to obtain the mathematical model of the vehicle. The equations of motion are transformed to state space equations in Linear Time Invariant (LTI) form. The effect of Chassis Design Factors (CDF) such as stabilizer bars, stiffness', Dynamic Index in Pitch (DIP) and mass ratio on the vehicle ride quality are investigated. The ride quality of the 3 dimensional vehicle that includes bounce, pitch, roll and unsprung masses motion is demonstrated in time domain response. The vehicle is considered as a Multi-Input-Multi-Output System (MIMO) subjected to deterministic ground inputs. Outputs of interest for the ride quality investigation are vertical and angular displacement and vertical accelerations. Numerical computer simulation analysis is performed using MATLAB® software.

The combustion process in an IC engine is of significant importance for its noise and vibration characteristics in the vehicle. Describing the combustion process with thermodynamic metrics typically demands extensive instrumentation of the engine to obtain the cylinder pressure from the combustion chamber. This time consuming task often requires, that the engine be removed from the vehicle, instrumented with pressure transducers, and then either reinstalled in the vehicle and tested or installed in a test cell and evaluated. This paper describes a new relatively simple approach towards examining important combustion parameters. The technique is based on statistical analysis of the crankshaft's speed fluctuation. This approach requires relatively simple instrumentation of the engine and is therefore more applicable for vehicle level investigations.

A vehicle’s performance and fuel economy plays an important role in obtaining a larger market share in the segment. This can be best achieved by optimizing the aerodynamics of the vehicle. Aerodynamics can be improved by altering the bodylines on a vehicle. Its drag coefficient can be maintained at a minimum value by properly designing various component profiles. The stability of a vehicle and Passenger comfort are affected by wind noise that is related to the aerodynamics of a vehicle. To study the effects of the above-mentioned parameters, the vehicle is tested inside a wind tunnel. In this paper, the authors study the body profile for different vehicles and analyze them using Computational Fluid Dynamics software - FLUENT. To study the influence of different body profiles on drag coefficient, 3 different vehicle segments are considered.

For virtual simulation of the vehicle attributes such as handling, durability, and ride, an accurate representation of pneumatic tire behavior is very crucial. With the advancement in autonomous vehicles as well as the development of Driver Assisted Systems (DAS), the need for an Intelligent Tire Model is even more on the increase. Integrating sensors into the inner liner of a tire has proved to be the most promising way in extracting the real-time tire patch-road interface data which serves as a crucial zone in developing control algorithms for an automobile. The model under development in Kettering University (KU-iTire), can predict the subsequent braking-traction requirement to avoid slip condition at the interface by implementing new algorithms to process the acceleration signals perceived from an accelerometer installed in the inner liner on the tire.

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.