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Seat Design Studies

Kettering University-Santhosh Sivan Kathiresan, Raghu Echempati
  • Technical Paper
  • 2020-01-1101
To be published on 2020-04-14 by SAE International in United States
In this paper, further studies have been carried out on the analysis and effect of certain design modifications on the structural integrity of an automotive seating rail structure. Automotive seating is one of the important component in the automotive industry due to their main function to carry the weight of passenger as well as to sustain the vibrations from the road. The seat structures are assembled to carry other important components such as side airbag and seatbelt systems. The entire seating is supported firmly and attached to the bottom bodywork of the vehicle through the linkage assembly called the seat rails. Seat rails are adjustable in their longitudinal motion which plays an important role in giving the passengers enough leg room to make them feel comfortable. Therefore, seat rails under the various operating conditions such as forward and normal positions should be able to withstand the complete weight of the human and the associated loads due to vibrations. In this paper, some of the functional requirements such as strength, stiffness, durability and crash performance are…
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ROBUST SENSOR FUSED OBJECT DETECTION USING CONVOLUTIONAL NEURAL NETWORKS FOR AUTONOMOUS VEHICLES

Kettering University-Jungme Park, Sriram Jayachandran Raguraman, Aakif Aslam, Shruti Gotadki
  • Technical Paper
  • 2020-01-0100
To be published on 2020-04-14 by SAE International in United States
Nowadays, the proliferation of research on the autonomous vehicles and the Advanced Driver Assistance System (ADAS) has resulted from the need for intelligent and safer mobility. Environmental perception is considered as an essential module for autonomous driving and ADAS. In the object detection problem, deep Convolutional Neural Networks (CNNs) become the State-of-the-Art with various different architectures. However, the performances of the existing CNNs have dropped when detecting small objects in distance. To deploy the environmental perception system in real world applications, it is important that the perception system achieves the high accuracy regardless the obstacle sizes, the distances, and weather conditions. In this paper, a sensor fused system for object detection, tracking and classification is proposed by utilizing the advantages of both vision sensor and automotive radar sensor. Data from on-vehicle radar sensor and camera sensor are processed in real time simultaneously. The proposed system consists of three modules: 1) the Coordinate Conversion module converts the radar coordinates into the image coordinate system. 2) Multi Level-Multi Region detection system based on the deep CNNs. The…
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Deep Forward Collision Detector in Autonomous Driving Vehicles

Kettering University-Mehrdad Zadeh
Concordia University-Javad Dargahi
  • Technical Paper
  • 2020-01-0138
To be published on 2020-04-14 by SAE International in United States
Forward collision is one of the most challenging concern in the safety of autonomous vehicle. Cooperation between many sensors such as LIDAR, Radar and camera helps to enhance the safety. Apart from the significance of being aware of objects on the drivable area, making an apt decision in the moment is noticeable. In this study, we concentrate on detecting front vehicle of autonomous car using a sensor fusion method, beyond only a detection method. In fact, we devise a solution which provides forward collision warning signal by discriminating between the vehicles moving in and opposite direction of autonomous vehicle, without lane check. Then, the result of classification is combined by the speed of autonomous vehicle as well as the size of detected front vehicle in the images. As a sensor fusion method, this data is utilized to determine whether the front detected car is an obstacle with a potential collision hazard or not. For this reason, we implement a deep neural network with two main parts. The first part is a faster regional convolutional neural…
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Design and Analysis of Kettering University’s New Proving Ground, the GM Mobility Research Center

Kettering University-Jennifer M. Bastiaan, Craig J. Hoff, Randall S. Beikmann, Scott LaForest
  • Technical Paper
  • 2020-01-0213
To be published on 2020-04-14 by SAE International in United States
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. This work describes the history of the proving ground development and outlines its design. Special emphasis is placed on a detailed…
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Experimentation for design improvements for coil spring in the independent suspension

Kettering University-Yaomin Dong
Automann Inc.-Viraj Dave
  • Technical Paper
  • 2020-01-0503
To be published on 2020-04-14 by SAE International in United States
The objective of this project is to analyze potential design changes that can improve the performance of helical spring in an independent suspension. The performance of the helical spring was based upon the result measure of maximum value of stress acting on it and the amount displacement caused when the spring undergoes loading. The design changes in the spring were limited to coil cross section, spring diameter (constant & variable), pitch and length of the spring. Using all the possible combinations of these design parameters linear stress analysis was performed on different spring designs and their Stress and displacement results were evaluated. Based on the results, the spring designs were classified as over designed or under designed springs. Next in this process, it was checked if the under designed springs can be optimized and classified according to a relevant application of the vehicles (racing cars or luxurious cars) and can they satisfy the requirements of fatigue life and vibration that helical spring suspension should under normal working conditions. The driving factor for this project was…
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Autonomous Lane Change Control Using Proportional-Integral-Derivative Controller And Bicycle Model

Kettering University-Ajinkya A. Joshi, Diane L. Peters, Jennifer M. Bastiaan
  • Technical Paper
  • 2020-01-0215
To be published on 2020-04-14 by SAE International in United States
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 a 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 simulation indicated that the lane change control system performed well for low speeds and at moderate steering wheel angles. After the simulation phase, the model was converted to implementable vehicle code and integrated into a vehicle for on-road testing of…
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A Robust Failure Proof Driver Drowsiness Detection System estimating Blink and Yawn

Kettering University-Yogesh Tony Jesudoss, Jungme Park
  • Technical Paper
  • 2020-01-1030
To be published on 2020-04-14 by SAE International in United States
Road safety and precautions against accidents have gained increased importance in the past decade. The fatal automobile accidents can be attributed to fatigued and distracted driving by drivers. Driver Monitoring Systems aid in alerting the distracted or non-attentive driver 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, blink and yawn sequentially. A robust Multi-Task Convolutional Neural Network (MTCNN) with the capability of face alignment and Detection 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. The eye and…
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Noise, Vibration, and Harshness Considerations for Autonomous Vehicle Perception Equipment

Kettering University-Charlie Gates, Jennifer Bastiaan, Prashant Jadhav, Javad Baqersad, Diane Peters
  • Technical Paper
  • 2020-01-0482
To be published on 2020-04-14 by SAE International in United States
With automakers looking to remake their traditional vehicle line-up into autonomous vehicles, Noise, Vibration, and Harshness (NVH) considerations for autonomous vehicles are soon to follow. While traditional NVH considerations still must be applied to carry-over systems, additional components are required for an autonomous vehicle to operate in addition to the basic vehicle itself. These additional components needed for autonomy also require NVH analysis and optimization. Autonomous vehicles rely on a suite of sensors, including RADAR, LiDAR, and cameras, placed at optimal points on the vehicle for maximum coverage and utilization. In this study, the NVH considerations of autonomous vehicles are examined, focusing on the additional perception equipment installed in autonomous vehicles. In particular, the nature of modifications to existing vehicles to increase the level of autonomy, and the associated NVH characteristics of these alterations, are reviewed with suggestions for future application to autonomous vehicles. A case study in the design of an original autonomous vehicle based on a production all-electric car, a 2017 Chevrolet Bolt, is outlined. A detailed description of the NVH design and…
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Automated 3-D Printing Machine Bed Clearing Mechanism

Kettering University-Dalton Whitten, Jason Dietrich, Matt Donar, Raghu Echempati, Colin Donar
  • Technical Paper
  • 2020-01-1301
To be published on 2020-04-14 by SAE International in United States
The work presented in this paper is based on the senior capstone class project undertaken by the student authors at Kettering University. The main aim of the project was to design an automated bed clearing mechanism for the Anet brand A8 3-D printer. The concept behind the idea was to allow everyone with this brand of printer to be able to print multiple prints without human interaction. The idea started out as a universal bed clearing mechanism, for most brands of 3-D printers. Upon researching into the many different styles and designs of printers, it was apparent that the designs differ too much from each other in order to create a universal product. The student team decided to aim for the most common style of 3-D printer, which the team also had a model to test the design. Due to the size of our team (number of members), they were split in to two sub-teams in order to explore two separate designs and develop the design and testing on both of the designs. Their designs…
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Source Noise Isolation During Electric Vehicle Pass-By Noise Testing Using Multiple Coherence

Kettering University-Manuj Dindgur, Jennifer Bastiaan
Bruel & Kjaer North America Inc.-Edward Green
  • Technical Paper
  • 2020-01-1268
To be published on 2020-04-14 by SAE International in United States
Due to the nearly silent operation of an electric motor, it is difficult for pedestrians to detect an approaching electric vehicle. To address this safety concern, the National Highway Traffic Safety Administration issued the Federal Motor Vehicle Safety Standard (FMVSS) No. 141, “Minimum Sound Requirements for Hybrid and Electric Vehicles”. This FMVSS 141 standard requires the measurement of electric vehicle noise according to certain test protocols; however, performing these tests can be difficult since inconsistent results can occur in the presence of transient background noise. Methods to isolate background noise during static sound measurements have already been established, though these methods are not directly applicable to a pass-by noise test where neither the background noise nor the vehicle itself as it travels past the microphone produce stationary sound signals. In this work, a 2017 Chevrolet Bolt electric vehicle is used for physical testing of pass-by noise at the Kettering University Mobility Research Center (MRC), an inner-city proving ground in Flint, Michigan. Sound signal processing is performed using a multiple coherence approach including discrete time intervals…