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A Machine Learning based Multi-objective Multidisciplinary Design Optimization (MMDO) for Lightweighting the Automotive Structures

Mahindra and Mahindra, Ltd.-Ranga Srinivas Gunti
  • Technical Paper
  • 2019-28-2424
Published 2019-11-21 by SAE International in United States
The present work involves Machine Learning (ML) based Multi-objective Multidisciplinary Design Optimization (MMDO) for lightweighting the automotive structures. The challenge in deployment of MMDO algorithms in solving real-world automotive structural design problems is the enormous time involved in solving full vehicle finite element models that involve large number of design variables and multiple performance constraints pertaining to vehicle dynamics, durability, crash and NVH domains. With the availability of powerful workstations and using the advanced Computer Aided Engineering (CAE) tools, it has become possible to generate huge sets of simulation data pertaining to multiple domains. In the present work, lightweigting of the vehicle structure is achieved, considered the vehicular hardpoint locations and the gages of the vehicle structures as the design variables and performance parameters pertaining to vehicle dynamics, structural durability, front-end intrusions during an IIHS offset impact test and the modal frequencies of few critical structural members as the constraint variables. Artificial Neural Networks (ANN) based algorithms were used for developing the predictive models of various performance parameters. The predictive models were then used to…
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Machine Learning considerations in the context of Automotive Functional Safety Requirements for Autonomous Vehicles

General Motors LLC-Vijai K Gopalakrishnan
  • Technical Paper
  • 2019-28-2519
Published 2019-11-21 by SAE International in United States
We are currently in the age of developing Autonomous Vehicles (AV). Never before in history, the environment has been as conducive as today for these developments to come together to deliver a mass produced autonomous car for use by general public on the roads. Several enhancements in hardware, software, standards and even business models are paving the way for rapid development of AVs, bringing them closer to production reality. Safety is an indispensable consideration when it comes to transportation products, and ground vehicle development is no different. We have several established standards. When it comes to Autonomous Vehicle development, an important consideration is ISO 26262 for, Automotive Functional Safety. Going from generic frameworks such as Failure Mode and Effects Analyses (FMEA) and Hazard and operability study (HAZOP) to Functional Safety, Safety of Intended Functionality, and Automotive Safety Integrity Levels specific is a natural progression. This, in specific to AV development context with a renewed perspective is the need of the hour. The fundamental assumption of a human driver being part of the vehicle, considered in…
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Self-Expressive & Self-Healing Closures Hardwares for Autonomous & Shared Mobility

General Motors Technical Center India-Vijayasarathy Subramanian, Biju Kumar, Masani Sivakrishna, Anandakumar Marappan
  • Technical Paper
  • 2019-28-2525
Published 2019-11-21 by SAE International in United States
Shared Mobility is changing mobility trends of Automotive Industry and its one of the Disruptions. The current vehicle customer usage and life of components are designed majorly for personal vehicle and with factors that comprehend usage of shared vehicles. The usage pattern for customer differ between personal vehicle, shared vehicle & Taxi. In the era of Autonomous and Shared mobility systems, the customer usage and expectation of vehicle condition on each & every ride of vehicle will be a vehicle in good condition on each ride. The vehicle needs systems that will guide or fix the issues on its own, to improve customer satisfaction. We also need a transformation in customer behavior pattern to use shared mobility vehicle as their personal vehicle to improve the life of vehicle hardwares & reduce warranty cost. We will be focusing on Vehicle Closure hardware & mechanisms as that will be the first and major interaction point for customers in vehicle. This gives us an opportunity to improve product life and customer experience in ride share and shared mobility…
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Digital Twins for Prognostic Profiling

Altair Engineering India Pvt Ltd.-Painuri Thukaram, Sreeram Mohan
  • Technical Paper
  • 2019-28-2456
Published 2019-11-21 by SAE International in United States
Ability to have least failures in products on the field with minimum effort from the manufacturers is a major area of focus driven by Industry 4.0 initiatives. Amidst traditional methods of performing system/subsystem level tests often does not enable the complete coverage of a machine health performance predictions. This paper highlights a workable workflow that could be used as a template while considering system design especially employing Digital Twins that help in mimicking real-life scenarios early in the design cycle to increase product’s reliability as well as tend to near zero defects.With currently available disruptive technologies, systems integrated multi-domain 'mechatronics' systems operating in closed-loop/close-interaction. This poses great challenge to system health monitoring as failure of any component can trigger catastrophic system failures. It may be the reason that component failures, as per some aerospace reports, are found to be major contributing factors to aircraft loss-of-control. Essentially, it is either too expensive or impossible to monitor every component or subsystem of a complex machine and the current state of the Integrated Health Monitoring Systems seem to…
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Enhanced Road condition monitoring for developing countries

STMicroelectronics-Saurabh Rawat, Prashant Pandey
  • Technical Paper
  • 2019-28-2462
Published 2019-11-21 by SAE International in United States
"According to Data on Road accidents in India by Transport Research Wing of Ministry of Road Transport & Highways, more than 4 Lakhs road accidents happened every year from year 2003 to 2017. Poor road conditions and badly designed roads are the common cause of road accidents besides the driver's negligence. Poor roads and badly designed speed breakers are common in developing countries. Apart from accidents, poor road conditions can cause excessive fuel consumption & damage to vehicles. Road condition monitoring solutions aim to warn the drivers of upcoming bad patch on the road and optionally report road conditions to authorities. There are multiple existing solutions that use motion sensors and GPS to detect a bad patch on the road. The presented solution builds over capability of existing solutions by adding useful features making it more practical and useful. The presented scheme is able to differentiate between a pothole and a speed breaker using a machine learning based approach. It also employs additional sensors like gyroscope and optionally a camera to detect on which side…
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A Self-Intelligent Traffic Light Control System based on Traffic Environment using Machine Learning

Maharaja Agrasen Institute of Technology-Ananya Bansal, Shubham Upadhyaya
  • Technical Paper
  • 2019-28-2459
Published 2019-11-21 by SAE International in United States
In this paper, we will detect and track vehicles on a video stream and count those going through a defined line and to ultimately give an idea of what the real-time on street situation is across the road network. Our major objective is to optimize the delay in transit of vehicles in odd hours of the day. It uses YOLO object detection technique to detect objects on each of the video frames And SORT (Simple Online and Realtime Tracking algorithm) to track those objects over different frames. Once the objects are detected and tracked over different frames a simple mathematical calculation is applied to count the intersections between the vehicles previous and current frame positions with a defined line. At present, the traffic control systems in India, lack intelligence and act as an open-loop control system, with no feedback or sensing network. Present technologies use Inductive loops and sensors to detect the number of vehicles passing by. This is a very inefficient and expensive way to make traffic lights adaptive. Using a simple CCTV camera…
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Development of a Novel Machine Learning Methodology for the Generation of a Gasoline Surrogate Laminar Flame Speed Database under Water Injection Engine Conditions

SAE International Journal of Fuels and Lubricants

NAIS S.r.l., Italy-Claudio Forte
University of Bologna, Italy-Leonardo Pulga, Gian Marco Bianchi, Matteo Ricci, Giulio Cazzoli
  • Journal Article
  • 04-13-01-0001
Published 2019-11-19 by SAE International in United States
The water injection is one of the technologies assessed in the development of new internal combustion engines fulfilling new emission regulation and policy on Auxiliary Emission Strategy assessment. Besides all the positive aspects about the reduction of mixture temperature at top dead center and exhaust gases temperature at turbine inlet, it is well known that the water vapor acts as a mixture diluter, thus diminishing the reactants burning rate. A common methodology employed for the Reynolds-Averaged Navier-Stokes Computational Fluid Dynamics (RANS CFD) simulation of the reciprocating internal combustion engines’ turbulent combustion relies on the flamelet approach, which requires knowledge of the Laminar Flame Speed (LFS) and thickness. Typically, these properties are calculated by means of correlation laws, but they do not keep into account the presence of water mass fraction. A more precise methodology for the definition of both the LFS and thickness is thus required. The interrogation of a previously computed look-up table of such properties during run time seems to be a suitable and more accurate method than using correlations. In order to…
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A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling

SAE International Journal of Transportation Safety

Jianghan University, China-Jun Gao, Jiangang Yi
University of Michigan-Dearborn, USA-Yi Lu Murphey
  • Journal Article
  • 09-07-02-0009
Published 2019-11-14 by SAE International in United States
Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components. Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data. Secondly, the LCM has an ability of learning the global spatial-temporal information via Temporal Modeling Blocks (TMBs). Finally, a two-layer Long Short-Term Memory (LSTM) network is used to learn video contextual features combined with lane boundary based distance features in lane change events. The experimental results on a -world driving dataset show that the LCM is capable of learning the latent features of lane-changing behaviors and achieving significantly better performance than other prevalent models.
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Virtual Co-Simulation Platform for Test and Validation of ADAS and Autonomous Driving

Hunan CRRC Times Electric Vehicle CO., LTD-Tian Zhu
Tongji University-Ke Song, Bin Wei, Zhen Song, Huan Chen
Published 2019-11-04 by SAE International in United States
Vehicles equipped with one or several functions of Advanced Driver Assistant System (ADAS) and autonomous driving (AD) technology are more mature and prevalent nowadays. Vehicles being smarter and driving being easier is an unstoppable trend. In the near future, intelligent vehicles will be mass produced and running on the road. However, before the mass-production of intelligent vehicles, a lot of experimental tests and validations need to be carried out to insure the safety and reliability of ADAS and AD technology. Although the road test of real vehicles is the most reliable and accurate test method, it cannot meet the need of rapid development of technology research due to high time and financial cost. Therefore, a high-efficient design and evaluation methodology for ADAS and AD development and test is a must. In this paper, a virtual co-simulation platform based on MATLAB/Simulink, OpenModelica and Unity 3D game engine (MOMU) is proposed. Simulink is used for vehicle control software modeling. OpenModelica simulates vehicle dynamic models written in Modelica. Unity provides visualized display of ADAS simulation and a virtual…
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Optical Neural Network Could Lead to Intelligent Cameras

  • Magazine Article
  • TBMG-35479
Published 2019-11-01 by Tech Briefs Media Group in United States

UCLA engineers have made major improvements on their design of an optical neural network — a device inspired by how the human brain works — that can identify objects or process information at the speed of light. The development could lead to intelligent camera systems that figure out what they are seeing simply by the patterns of light that run through a 3D engineered material structure. The new design takes advantage of the parallelization and scalability of optical-based computational systems.