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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 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.

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.

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.

Since the popularization of the Electric Vehicle (EV) there has been a large movement of consumers, governments, and the automotive industry due to its environmentally friendly characteristics. Unlike an IC engine, the batteries use multitudes of rare earth minerals and complex manufacturing processes which in some cases have been shown to produce as many emissions as an ICE vehicle over its entire lifespan. Another unnoticed important environmental concern has been the final recycling and disposal of the power train after its use. Unlike an ICE engine, which can be melted down or re-used, recycling batteries are much more difficult. In most cases the recycling process and the byproducts produced can be very harmful to the environment. This paper aims to be a complete cradle-to-grave analysis of all emissions produced in the life of an EV battery.

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.

Clean snowmobile technology has been developed using methods which can be applied in the real world with a minimal increase in cost. Specifically, a commercially available snowmobile using a two cylinder, four-stroke engine has been modified to run on high-blend ethanol (E-85) fuel. Additionally, a new exhaust system which features customized catalytic converters and mufflers to minimize engine noise and exhaust emissions has developed. Finally, a number of additional improvements have been made to the track to reduce friction and diminish noise. The results of these efforts include emissions reductions of 94% when compared with snowmobiles operating at the 2012 U.S. Federal requirements.

Kettering University's Clean Snowmobile Challenge student design team has developed a new robust and innovative snowmobile for the 2005 competition. This snowmobile dramatically reduces exhaust and noise emissions and improves fuel economy compared with a conventional snowmobile. Kettering University has utilized a modified snowmobile in-line four cylinder, four-stroke, engine. The team added an electronically-controlled fuel-injection system with oxygen sensor feedback to this engine. This engine has been installed into a 2003 Yamaha RX-1 snowmobile chassis. Exhaust emissions have been further minimized through the use of a customized catalytic converter and an electronically controlled closed-loop fuel injection system. A newly designed and tuned exhaust as well as several chassis treatments have aided in minimizing noise emissions.

Kettering University's entry for the 2006 Clean Snowmobile challenge utilizes a Polaris FST Switchback. This snowmobile having a two cylinder, four-stroke engine has been modified to run on ethanol (E-85). The student team has designed and built a new exhaust system which features customized catalytic converters to minimize engine out emissions. A number of improvements have been made to the track to reduce friction and diminish noise.

Kettering University has developed a cleaner and quieter snowmobile using technologies and innovative methods which can be applied in the real world with a minimal increase in cost. Specifically, a commercially available snowmobile using a two cylinder, four-stroke engine has been modified to run on high-blend ethanol (E-85) fuel. Further, a new exhaust system which features customized catalytic converters and mufflers to minimize engine noise and exhaust emissions has developed. A number of additional improvements have been made to the track to reduce friction and diminish noise. This paper provides details of the snowmobile development the results of these efforts on performance and emissions. Specifically, the Kettering University snowmobile achieved reductions of approximately 72% in CO, and 98% in HC+NOx when compared with the 2012 standard. Further, the snowmobile achieved a drive by noise level of 73 dbA while operating on hard packed snow.

Much development in the automotive industry relates to the use of high-content ethanol blended fuels to reduce the emissions produced by on-road engines/vehicles. However, less research has been done on the effect of operating small off-road engines (SORE) on high-blend ethanol fuels without substantial modifications. Most manufacturers of such engines only certify proper operation on low content ethanol blends such as E10 (10% ethanol, 90% gasoline by volume). This paper focuses on the use of E77 fuel in a small off-road engine which is speed-governed. Such engines are commonly used in lawn mowers, small recreational vehicles, or other equipment. The exhaust emissions and performance of the engine were evaluated using the EPA 6-mode duty cycle for small recreational engines where testing and analysis followed the recommendations of SAE J1088. This test cycle consisted of operating the engine at steady state load points using a fixed engine speed.

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.

Aerodynamics of trucks and other high sided vehicles is of significant interest in reducing road side accidents due to wind loading and in improving fuel economy. Recognizing the limitations of conventional wind tunnel testing, considerable efforts have been invested in the last decade to study vehicle aerodynamics computationally. In this paper, a three-dimensional near field flow analysis has been performed for axial and cross wind loading to understand the airflow characteristics surrounding a truck-like bluff body. Results provide associated drag for the truck geometry including the exterior rearview mirror. Modifying truck geometry can reduce drag, improving fuel economy.

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.

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.

Kettering University's entry in the 2003 Clean Snowmobile Challenge entails the installation of a fuel injected four-stroke engine into a conventional snowmobile chassis. Exhaust emissions are minimized through the use of a catalytic converter and an electronically controlled closed-loop fuel injection system, which also maximizes fuel economy. Noise emissions are minimized by the use of a specifically designed engine silencing system and several chassis treatments. Emissions tests run during the SAE collegiate design event revealed that a snowmobile designed by Kettering University produces lower unburned hydrocarbon (1.5 to 7 times less), carbon monoxide (1.5 to 7 times less), and oxides of nitrogen (and 5 to 23 times less) levels than the average automobile driven in Yellowstone National Park. The Kettering University entry also boasted acceleration performance better than the late-model 500 cc two-stroke snowmobile used as a control snowmobile in the Clean Snowmobile testing.