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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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MAIN FEATURES of FORMING the TRANSMISSION of an ACTIVE MULTI-LINK ROAD TRAIN

Belarusian National Technical University-Sergey Haritonchik
Moscow Bauman State Technical University-Boris Belousov
  • Technical Paper
  • 2020-01-0427
To be published on 2020-04-14 by SAE International in United States
The development of the economy and the associated growth in trade both within the country and international transport, the associated construction and development of transport routes using elements of intelligent transport systems constantly require increasing the efficiency of trunk transportation. In addition, the development of new economic regions with an undeveloped road network is impossible without high-capacity motor vehicles and cross-country ability. To achieve these goals, the creation of active road trains, including multi-link ones, based on non-traditional technical solutions, is required. The idea of using multi-link trains in the system of intercity and international transportation is not new. However, at the present stage of development of automotive technology requires rethinking and use of new achievements of science and technology. At present, the process of changing the design of land vehicles, qualitatively changing their structure and composition of the main power devices based on the integration of electronic, electrical, hydraulic, pneumatic and mechanical elements and significantly increasing the role of electronics and control systems, i.e. widespread introduction of mechatronic systems and modules in the design…
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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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Artificial Neural Network Based Predictive Approach in Vehicle Thermal Systems Applications

FCA Canada Inc.-Pooya Mirzabeygi, Shankar Natarajan
  • Technical Paper
  • 2020-01-0148
To be published on 2020-04-14 by SAE International in United States
In automotive industry, there is an abundance of test data collected at different stages of vehicle’s development. Heavy reliance on testing can lead to significant increase in vehicle program’s design costs and further delay in the development timing as vehicle instrumentation and testing is costly and time-consuming. This paper focuses on an alternative approach using the Artificial Neural Network (ANN). The ANNs are computing systems inspired by the brain’s biological networks that can learn by considering examples. The “trained” network can then be used to predict the system’s performance in a reliable and efficient manner. This is particularly useful in automotive industry as there exists a considerable amount of test data in the system, sub-system or component level that can be used to train the ANN. The trained ANN can then be used as an alternative for performance prediction and reduce the reliance on additional physical testing. The study focuses on thermal and climate control systems and the application of ANNs to predict the thermal performance. It is shown that ANNs are very robust in…
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Evaluation of Methods for Identification of Driving Styles and Simulation-Based Analysis of their Influence on Energy Consumption on the Example of a Hybrid Drive Train

Gregor Pucher
Graz University of Technology-Marko Domijanic, Mario Hirz
  • Technical Paper
  • 2020-01-0443
To be published on 2020-04-14 by SAE International in United States
Due to current progresses in the field of driver assistance systems and the continuously growing electrification of vehicle drive trains, the evaluation of driver behavior has become an important part in the development process of modern vehicles. Findings from driver analyses are used for the creation of individual profiles, which can be permanently adapted due to ongoing data processing. A benefit of data-based, dynamic control systems lies in the possibility to individually configure the vehicle behavior for a specific driver, which can contribute to increasing customer acceptance and satisfaction. In this way, an optimization of the control behavior between driver and vehicle and the resulting mutual learning and adjustment holds great potential for improvements in driving behavior, safety and energy consumption. The submitted paper deals with the analysis of different methods and measurement systems for the identification and classification of driver profiles as well as with their potential to optimize both vehicle driving behavior and energy consumption on the example of a hybrid drive train. A literature research results in a number of different approaches…
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Design Optimization of Sandwich Composite Armors for Blast Mitigation using Bayesian Optimization with Single and Multi-fidelity Data

Indiana University Purdue University Indianapolis-Andres Tovar
Purdue University-Homero Valladares
  • Technical Paper
  • 2020-01-0170
To be published on 2020-04-14 by SAE International in United States
The most common and lethal weapon against military vehicles is the improvised explosive device (IED). In an explosion, large cabin’s penetrations injure the lower body, and high accelerations harm the spine. Critical penetrations and accelerations can cause the death of the vehicle’s occupants. In the military industry, there is an increasing interest to improve the blastworthiness of their vehicles. This investigation presents a multi-fidelity Bayesian optimization (BO) approach to design sandwich composite armors for blast mitigation. BO is an efficient methodology to solve optimization problems that involve black-box functions. The black-box functions of this work are the responses of the finite element (FE) simulations of the composite armor. The main two components of BO are the surrogate model of the black-box function and the acquisition function that guides the searching process. In this investigation, the surrogate models are Gaussian Process (GP) regressions and the acquisition function is the multi-objective expected improvement (MEI) function. Information from low and high fidelity FE models trains the GP surrogates. The low fidelity FE model assumes elastic behavior of the…
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Improving Rough Road NVH by Hydraulic Mount Design Optimization

Maruti Suzuki India, Ltd.-Vivek Singh, Gokulram Seenivasan, Gaurav Gupta, Adheesh Agrawal
  • Technical Paper
  • 2020-01-0422
To be published on 2020-04-14 by SAE International in United States
Vehicle cabin comfort creates or emphasizes a specific image of a brand and its product quality. Low frequency power train induced noise and vibration levels are a major contributor affecting comfort inside passenger cabin. Thus, using hydraulic mount is a natural choice. Introduction of lighter body panels coupled with cost effective hydraulic mounts has resulted in some additional noises on rough road surfaces which are challenging to identify during design phase. This paper presents a novel approach to identify two such noises i.e. Cavitation noise and Mount membrane hitting noise based on component level testing which are validated at vehicle experimentally. These noises are encountered at 20~30 kmph on undulated road surfaces. Sound quality aspect of such noises is also studied to evaluate the solution effectiveness.
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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.
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Adaptive Test Feedback LoopA Modelling Approach for Checking Side Effects During Test Executionfor Advised Explorative Testing

Bremer Inst. Für Produktion Und Logistik-Marco Franke, K. A. Hribernik
University of Bremen-K. D. Thoben
  • Technical Paper
  • 2020-01-0017
To be published on 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…