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Heterogeneous Machine Learning on High Performance Computing for End to End Driving of Autonomous Vehicles

National Renewable Energy Laboratory-Xiangyu Zhang, Peter Graf
Oak Ridge National Laboratory-Robert Patton, Shang Gao, Spencer Paulissen, Nicholas Haas, Brian Jewell
  • Technical Paper
  • 2020-01-0739
To be published on 2020-04-14 by SAE International in United States
Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving. For machines though, this gap is guarded by at least two challenges: 1) machine learning techniques remain brittle and unable to generalize to a wide range of scenarios, and 2) effective training data that enhances generalization and generates the desired driving behavior. Further, each challenge can be computationally intensive on its own thereby exasperating the gap. Moreover, is has…
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RouteE: A Vehicle Energy Consumption Prediction Engine

National Renewable Energy Laboratory-Jacob Holden, Nicholas Reinicke, Jeff Cappellucci
  • Technical Paper
  • 2020-01-0939
To be published on 2020-04-14 by SAE International in United States
The emergence of Connected and Automated Vehicles and Smart Cities technologies create the opportunity for new mobility mode and routing decision tools, among many others. In order to achieve maximal mobility and minimal energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy Prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data is unavailable. Applications include vehicle route selection, energy accounting/optimization in transportation simulation, and corridor energy analyses, among others. The software is an open-source Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own datasets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are trained using NREL’s Future Automotive Systems Technology Simulator (FASTSim) paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center (TSDC) resulting…
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Parameter Sensitivity Study of Self-piercing Rivet Insertion Process using Finite Element and Machine Learning Method

Chongqing University-Yudong Fang, Zhenfei Zhan
Ford Motor Company-Li Huang, Shiyao Huang
  • Technical Paper
  • 2020-01-0219
To be published on 2020-04-14 by SAE International in United States
Self-piercing rivets (SPR) are efficient and economical joining methods for lightweight automotive body structure manufacturing. Finite element method (FEM) is a potential effective way to assess joining process while some uncertain parameters can be employed in the simulation based on the prior knowledge, which could lead to significant mismatches between CAE predictions and physical tests. Thus, a sensitivity study on critical CAE parameters is important to guide the high-fidelity modeling of SPR insertion Process. In this paper, a 2-D symmetrical CAE model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are trained using machine learning methods to build the linkage between selected inputs (e.g. material properties, interfacial frictions, clamping force) and outputs (cross-section dimensions). It is found that it is feasible to train surrogate models with high accuracy to replace the time-consuming CAE simulations with a limited sampling volume. Based on trained surrogate models, an extensive sensitivity study is conducted to thoroughly understand the impact of a collection of CAE parameters. This research provides a solid foundation…
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A New Approach to Understanding Planetary Gear Train Efficiency and Powerflow

FCA US LLC-Pradeep Attibele
  • Technical Paper
  • 2020-01-0432
To be published on 2020-04-14 by SAE International in United States
Understanding planetary gear efficiency is more involved than understanding efficiency of external gears because of the recirculating power that is inherent in planetary gear operation. There have been several publications going back to several decades on this topic. However, many of these publications are mathematical in their approach and tend to be overlooked by practicing engineers. This paper takes a new, more visual and intuitive approach to the problem. It uses lever diagrams, which have been a standard tool in the transmission engineer’s arsenal for almost four decades, to visualize the powerflow and develop analytical expressions for the efficiency of simple and compound planetary gears. It then extends the approach to more complex gear trains.
new

Defect Detection of Railway Fasteners Based on Improved PHOG Characteristics

SAE International Journal of Transportation Safety

China-Jiaming Hu
Northeast Electric Power University, China-Chun-Ming Wu
  • Journal Article
  • 09-08-01-0002
Published 2020-03-23 by SAE International in United States
Aiming at the problem of low recognition rate and slow speed caused by the small proportion of key area information in feature vectors of original Pyramid Histogram of Gradients (PHOG) features, an improved feature extraction method of PHOG is proposed. The PHOG feature extraction method is combined with edge feature enhancement method based on Census transform to extract feature vectors of fasteners, and dimensionality reduction is processed by Kernel Principal Component Analysis (KPCA) method to reduce the interference of redundant information. The vector is inputted into the support vector machine for training in order to get the classifier model and realize the automatic identification of the fastener’s state. The simulation results show that compared with the traditional PHOG method, this feature extraction method improves the false detection rate by 2.7%, and the complexity of the algorithm is greatly reduced.
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Adaptive Test Feedback LoopA Modelling Approach for Checking Side Effects During Test Executionfor Advised Explorative Testing

BIBA GmbH-Karl Anthony Hribernik
Bremer Inst. Für Produktion Und Logistik-Marco Franke, Klaus-Dieter Thoben
  • Technical Paper
  • 2020-01-0017
Published 2020-03-10 by SAE International in United States
The main objective of testing is to evaluate the functionality, reliability, and operational safety of products. However, this objective makes testing a complex and expensive stage in the development process. This is particularly true for complex and large systems, such as trains or aircrafts, which require maximum operational safety. From the perspective of an aircraft manufacturer, the checks are carried out via test cases on the integration, system and application levels. Thus, they certify the products against the requirements using black box testing approach. In doing so, a test plan defines a sequence of test cases whereby it sets up the environment, stimulates the fault, and then observes the system under test for each case. Subsequently, the post processing of the test execution classifies the test plan in passed or failed. The ongoing digitization and interconnectedness between aircraft systems is leading to a high number of test cases and a multitude of reasons why a specific test-case fails. A corresponding error analysis and adaptation of the test plan is a complex and lengthy process, which…
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Electric Vehicle Power Transfer System Using Conductive Automated Connection Devices Vehicle-Mounted Pantograph (Bus-Up)

Hybrid - EV Committee
  • Ground Vehicle Standard
  • J3105/2_202001
  • Current
Published 2020-01-20 by SAE International in United States
This document details one of the connections of the SAE J3105 document. The connections are referenced in the scope of the main document SAE J3105. SAE J3105/2 details the vehicle-mounted pantograph, or the bus-up connection. All the common requirements are defined in the main document; the current document provides the details of the connection. This document covers the connection interface relevant requirements for an electric vehicle power transfer system using a conductive automated charging device based on a conventional rail vehicle pantograph design. To allow interoperability for on-road vehicles (in particular, buses and coaches), one configuration is described in this document. Other configurations may be used for non-standard applications (for example, mining trucks or port vehicles).
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A Recommended Method of Analytically Determining the Competence of Hydraulic Telescopic Cantilevered Crane Booms

Cranes and Lifting Devices Committee
  • Ground Vehicle Standard
  • J1078_202001
  • Current
Published 2020-01-15 by SAE International in United States
This analysis applies to crane types as covered by ASME B30.5.
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Performance Levels and Methods of Measurement of Electromagnetic Compatibility of Vehicles, Boats (up to 15 m), and Machines (16.6 Hz to 18 GHz)

Electromagnetic Compatibility (EMC) Standards
  • Ground Vehicle Standard
  • J551/1_202001
  • Current
Published 2020-01-10 by SAE International in United States
This SAE Standard covers the measurement of radio frequency radiated emissions and immunity. Each part details the requirements for a specific type of electromagnetic compatibility (EMC) test and the applicable frequency range of the test method. The methods are applicable to a vehicle, boat, machine or device powered by an internal combustion engine or battery powered electric motor. Operation of all engines or motors (main and auxiliary) of a vehicle, boat, machine or device is included. All equipment normally operating when the vehicle, boat, machine or device is in operation is included. Operator controlled equipment is included or excluded as specified in the individual document parts. As a special case, CISPR 12 applies to battery powered floor finishing equipment, but robot carpet sweepers are excluded. By reference, IEC CISPR 12 and CISPR 25 are adopted as the standards for the measurement of vehicle emissions. In the event that an amendment is made or a new edition is published, the new IEC document shall become part of this standard six months after the publication of the…
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Performance & Efficiency Improvement of Electric Vehicle Power Train

International Centre for Automotive Technology-Devesh Pareek
  • Technical Paper
  • 2019-28-2483
Published 2019-11-21 by SAE International in United States
Introduction: The advent of electric mobility is changing the conventional mobility techniques and their application in automobiles across all segments. This development comes with challenges ranging across varied sub -systems in a vehicle including Power Train, HVAC, Accessories, etc. Objective: This paper would concentrate on the Power train related sub systems & improvement of the same both in terms of Efficiency & Performance. Methodology: The electric power train consists of three major sub parts: 1. Motor Unit 2. Controller with Power electronics 3. Battery Pack with BMS We would concentrate on improving the overall efficiency and performance of all these subsystems while they perform in vehicle environment and work in tandem by deploying following techniques: a. Improved Regenerative Braking for converting vehicles Kinetic energy into electrical energy using specific algorithms and control techniques b. Optimization of Design Specs and duty cycle based on real world driving cycles. c. Innovative Heat dissipation techniques to minimize energy loss to heat. d. Efficient Electrical to Chemical Energy conversion and vice versa through use of optimization techniques based on…
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