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CAE method Development for the Seat latch effort calculation in 2nd row Bench seats and optimization

Ford Motor Co., Ltd.-Ravi Purnoo Munuswamy
Ford Motor Co., Pvt., Ltd.-Arunachalam Muthupandian
  • Technical Paper
  • 2020-01-1103
To be published on 2020-04-14 by SAE International in United States
There are factors that can make installation of LATCH-equipped seats difficult or, in some cases, impossible. Customers are raising complaints on latching issues to automotive industry and in turn warranty issue cost more to the company. Therefore, automotive industries are spending lot of money on physical test and method development. At present, there is no such proven virtual test available for testing seat latch effort (passenger apply effort to do the latching). Since many industries concentrating more on developing new method using CAE approach for evaluate seat latching effort with less cost. So in this paper, authors are elaborating research on new method using CAE method LS dyna solver with Hypermesh preprocessor. Further deep dive on physical test data correlation with CAE method(virtual test) to verify the design verification efficiency. Also, from this test can able to estimate the effort easily and optimize seat latching performance.
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Hardware-in-the-Loop and Road Testing of RLVW and GLOSA Connected Vehicle Applications

Ford Motor Co., Ltd.-Alexander Katriniok
Camp LLC-Jayendra Parikh
  • Technical Paper
  • 2020-01-1379
To be published on 2020-04-14 by SAE International in United States
This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections. This same approach of HIL testing followed by testing in real intersections using our experimental vehicle is later extended to the GLOSA application. The GLOSA application that is tested in this paper has both an optimal speed advisory for passing at the green light and also includes a…
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Effect of Fuel Type and Tip Deposits on End of Injection Spray Characteristics of Gasoline Direct Injection Fuel Injectors

Ford Motor Co., Ltd.-Mark Meinhart
Michigan Technological University-Robert A. Schroeter, Jeffrey Naber, Seong-Young Lee
Published 2019-10-22 by SAE International in United States
There has been a great effort expended in identifying causes of Hydro-Carbon (HC) and Particulate Matter (PM) emissions resulting from poor spray preparation, leading to characterization of fueling behavior near nozzle. It has been observed that large droplet size is a primary contributor to HC and PM emission. Imaging technologies have been developed to understand the break-up and consistency of fuel spray. However, there appears to be a lack of studies of the spray characteristics at the End of Injection (EOI), near nozzle, in particular, the effect that tip deposits have on the EOI characteristics. Injector tip deposits are of interest due to their effect on not only fuel spray characteristics, but also their unintended effect on engine out emissions. Using a novel imaging technique to extract near nozzle fuel characteristics at EOI, the impact of tip deposits on Gasoline Direct Injection (GDI) fuel injectors at the EOI is being examined in this work. Additionally, the impact of the test fuel used will also be evaluated. This work will present the large influence of fuel…
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Automotive Applications of Hardware-in-the-Loop (HIL) Simulation

Ford Motor Co., Ltd.-Adit Joshi
  • Progress In Technology (PT)
  • PT-209
Published 2019-08-13 by SAE International in United States
Automotive Applications of Hardware-in-the-Loop (HIL) Simulation shines a light on HIL simulation testing methodology commonly used in the automotive industry for conventional, electrification and autonomy applications and can serve as an introductory resource for college students looking to join the automotive industry or experienced technical professionals who need a deeper understanding on what is HIL simulation, what are its benefits and how can it be used in their respective organizations.
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Machine Learning with Decision Trees and Multi-Armed Bandits: An Interactive Vehicle Recommender System

Ford Motor Co., Ltd.-Dominique Meroux, Zhen Jiang
Carnegie Mellon University-Tong Yu, Ole Mengshoel
Published 2019-04-02 by SAE International in United States
Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study recommender systems for vehicles. Compared to previous research on recommender systems in other domains (e.g., movies or music), there are two major challenges associated with recommending vehicles. First, typical customers purchase fewer cars than movies or pieces of music. Thus, it is difficult to obtain rich information about a customer’s vehicle purchase history. Second, content information obtained about a customer (e.g., demographics, vehicle preferences, etc.) is also difficult to acquire during a relatively short stay in a dealership. To address these two challenges, we propose an interactive vehicle recommender system based a novel machine learning method that integrates decision trees and multi-armed bandits. Decision tree learning effectively selects important questions to ask the customer and encodes the customer's key preferences. With these preferences as prior information, the multi-armed bandit algorithm, using Thompson sampling, efficiently leverages the user’s feedback to improve the recommendations in an online fashion. The empirical results show that…
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Innovative Knee Airbag (KAB) Concept for Small Overlap and Oblique Frontal Impacts

Ford Motor Co., Ltd.-Pardeep Jindal
Detroit Engineered Products (DEP) Inc.-Rahul Makwana
Published 2019-04-02 by SAE International in United States
Considerable research has been conducted in terms of attempting to reduce lower leg injury risk in full frontal impacts, in some cases by the use of a knee airbag (KAB). However, there has been limited research into the performance of KAB systems during a crash test with increased oblique loading, such as the IIHS small overlap frontal test, an oblique moving deformable barrier test (OI) being researched by NHTSA, and a mobile progressive deformable barrier test (MPDB) that is expected to be implemented by Euro NCAP in the next few years. The objective of the current numerical study was concentrated on the evaluation of an innovative KAB concept design intended to reduce ATD right inboard lower leg/foot responses under small overlap and oblique loading conditions. A novel appendage KAB concept design was developed with the help of morphing and computational studies which were performed with different ATD sizes. In the study, one of the lower leg/foot responses was monitored and compared over a conventional KAB design. Cases investigated in the study showed that the novel…
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Security in Wireless Powertrain Networking through Machine Learning Localization

Ford Motor Co., Ltd.-Ben Tabatowski-Bush
Published 2019-04-02 by SAE International in United States
This paper demonstrates a solution to the security problem for automotive wireless powertrain networking. That is, the security for wireless automotive networking requires a localization function before we allow a node to join the network. We explain why for powertrain wireless networking, this ability of identifying the precise location of a communicating wireless node is critical. In this paper, we explore existing methods that others have used to implement localization for wireless networking. Then, we apply machine-learning techniques to a dataset that has localization information associated with received signal strength indication. We reveal insights provided by our dataset though an exploration with statistics and visualization. We then present our problem in terms of pattern recognition via multiple techniques, including Naïve Bayes Classifier and Artificial Neural Networks. Through these techniques, we use our exemplary vehicle network and dataset to demonstrate a simple solution that has excellent performance in determining localization. This is an elegant solution to the security problem for wireless automotive networking.
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Integrating SOTIF and Agile Systems Engineering

Ford Motor Co., Ltd.-Kyle Post, Christopher K. Davey
Published 2019-04-02 by SAE International in United States
Autonomous vehicles and advanced driver assistance systems have functionality realized across numerous distributed systems that interact with a dynamic cyber-physical environment. This complexity raises the potential for emergent behaviours which are not intended for the system’s operational use. The need to analyze the intended functionality of these emergent behaviours for potential hazards, which may occur in absence of faults, are aspects of the ISO PAS 21448, Safety of the Intended Functionality (SOTIF) [1]. The Safety of the Intended Functionality or SOTIF is a framework for developing systems which are free from unreasonable risk due to the intended functionality or performance limitations of a system which is free from faults. This is meant to complement Functional Safety which is covered in ISO 26262 [2]. The major focus of SOTIF is to aid in the functional development of a system. The major areas are focused on analyzing the system as specified, verify that any known hazardous scenarios meet the expected behaviour, identify any hazardous scenarios which were previously not known, and iterate the functional design accordingly.This paper…
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Extended Kalman Filter Based Road Friction Coefficient Estimation and Experimental Verification

Ford Motor Co., Ltd.-Arlene Fang
APTIV PLC-Bin Li, Guobiao Song
Published 2019-04-02 by SAE International in United States
Accurate road friction coefficient is crucial for the proper functioning of active chassis control systems. However, road friction coefficient is difficult to be measured directly. Using the available onboard sensors, a model-based Extended Kalman filter (EKF) algorithm is proposed in this paper to estimate road friction coefficient. In the development of estimation algorithm, vehicle motion states such as sideslip angle, yaw rate and vehicle speed are first estimated. Then, road friction coefficient estimator is designed using nonlinear vehicle model together with the pre-estimated vehicle motion states. The proposed estimation algorithm is validated by both simulations and tests on a scaled model vehicle.
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Analyzing and Preventing Data Privacy Leakage in Connected Vehicle Services

SAE International Journal of Advances and Current Practices in Mobility

Ford Motor Co., Ltd.-Yu Seung Kim, Pramita Mitra
University of Michigan-Huaxin Li, Di Ma, Brahim Medjahed
  • Journal Article
  • 2019-01-0478
Published 2019-04-02 by SAE International in United States
The rapid development of connected and automated vehicle technologies together with cloud-based mobility services are revolutionizing the transportation industry. As a result, huge amounts of data are being generated, collected, and utilized, hence providing tremendous business opportunities. However, this big data poses serious challenges mainly in terms of data privacy. The risks of privacy leakage are amplified by the information sharing nature of emerging mobility services and the recent advances in data analytics. In this paper, we provide an overview of the connected vehicle landscape and point out potential privacy threats. We demonstrate two of the risks, namely additional individual information inference and user de-anonymization, through concrete attack designs. We also propose corresponding countermeasures to defend against such privacy attacks. We evaluate the feasibility of such attacks and our defense strategies using real world vehicular data.
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