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Technical Paper

Variational Autoencoders for Dimensionality Reduction of Automotive Vibroacoustic Models

2022-06-15
2022-01-0941
In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models. In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system.
Technical Paper

A Framework for Teaching Safety Critical Artificially Intelligent Control Systems to Undergrads

2022-05-26
2022-26-0028
There is an increasing demand to educate students on systems thinking and systems approaches at undergrad and graduate levels in colleges in India. Efforts are being made by industry, academia, and professional societies to join hands to bridge the gap. Specifically, there is significant emphasis on providing wholistic “live” case studies and examples to students to get their “hands dirty” on actual systems. One of the inhibitors on this aspect being faced, in the aerospace domain, is that actual examples are not available in the open literature as they are considered proprietary and/or confidential. This paper illustrates a framework for educating students on systems approaches and systems thinking in a near “live” scenario through a case of safety critical control system embedded with Artificial Intelligence (AI). With the recent advances in AI and increasing demands on embedding AI in complex aerospace systems, certification of such systems poses many hurdles and challenges.
Event

Sponsor - ADAS to Automated Driving Digital Summit

2022-05-24
The ADAS to Automated Driving Digital Summit will virtually bring together engineering professionals from OEMs, suppliers, technology and corporate and academic research. And now, with no travel required, the digital summit will reach an even broader international audience from North America, Europe, and Asia.
Event

2022-05-24
Event

Request Info - Sponsor - ADAS to Automated Driving Digital Summit

2022-05-24
Contact our Sales Team! The ADAS to Automated Driving Digital Summit will virtually bring together engineering professionals from OEMs, suppliers, technology and corporate and academic research. And now, with no travel required, the digital summit will reach an even broader international audience from North America, Europe, and Asia.
Technical Paper

Instantaneous Brain Strain Estimation for Automotive Head Impacts via Deep Learning

2022-05-20
2021-22-0006
Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (vrot) and the corresponding rotational acceleration (arot) profiles were concatenated as static images to serve as CNN input.
Journal Article

Procedural Generation of High-Definition Road Networks for Autonomous Vehicle Testing and Traffic Simulations

2022-05-10
Abstract Effective simulation-based testing of autonomous vehicles requires the exploration of vehicle performance against a wide variety of rare and unusual road and intersection geometries. We present a high-definition road and intersection generator called JunctionArt. JunctionArt takes as input a series of control lines and generates a series of roads and intersections which conforms to them. Roads exhibit different types of lane types such as turn lanes, one-way streets, and multiple lanes, while intersections feature a range of incident roads (three to seven incident roads), leading to a variety of geometries and interior connecting lanes. These roads are output in the OpenDRIVE format and, hence, are interoperable with a wide range of tools and simulation environments.
Journal Article

Anticipation-Based Autonomous Platoon Control Strategy with Minimum Parameter Learning Adaptive Radial Basis Function Neural Network Sliding Mode Control

2022-04-25
Abstract This article investigates the headway and optimal velocity tracking of autonomous vehicles (AVs), considering their predictive driving for the stability and integrity of spatial vehicle formation in the platoon. First, the human-like anticipation car-following model is used for modeling the autonomous system. Second, an adaptive radial basis function neural network (ARBF-NN)-based sliding mode control (SMC) is proposed for the control purpose. The control objective is to regulate traffic perturbation during entire road operations. To enable the controller to experience less computational burden and adaptation complexity, a minimum parameter learning (MPL) has also been integrated with ARBF-NN-based SMC. Third, an illustrative simulation example has been performed for two scenarios, i.e., constant headway and time-varying headway of vehicles.
Journal Article

Research on Transient Thermal-Structural Coupling Characteristics and Thermal Error Prediction of Ball Screw Feed System

2022-04-21
Abstract Catalytic converters have been effectively controlling the harmful exhaust gases to meet stringent emission norms. This article presents a new three-way catalyst developed using natural zeolite for effective emission reduction. The step-by-step preparation of the material for the developed catalyst is followed by its characterization using an energy dispersive X-ray (EDX), X-ray diffraction (XRD), and scanning electron microscope (SEM). The testing performed on a synthetic gas test bench (SGTB) shows substantial carbon monoxide (CO), hydrocarbon (HC), and nitric oxide (NO) reduction. Results show a 100% conversion for NO above 280°C, 54.8% for CO at 315°C, and 52% for HC at 500°C. The developed natural zeolite-based catalyst stands out from among current catalysts and can be endorsed for three-way conversions than the synthetic zeolite catalyst.
Journal Article

Thermal Management Optimization of Prismatic Lithium-Ion Battery Using Phase Change Material

2022-04-21
Abstract High technology expertise and strong advancement in electric vehicles and Lithium (Li)-ion battery devices and systems have increased the speed of development and application of new equipment. It is reported that Li-ion battery life reduces almost by 60 days per degree temperature rise in an operational temperature of 30°C to 40°C, which makes cooling a high priority. The current study focuses on cooling the battery system using Phase Change Material (PCM) placed as bands of different dimensions around the prismatic battery. Eight novel designs of varying dimensions were constructed for three-volume scenarios. The heat generations considered in this study are 6,855 W/m3, 12,978 W/m3, 19,100 W/m3, and 63,970 W/m3. The data obtained was trained using an artificial neural network (ANN), and an equation was attained to fit the data. The optimum placement of PCM with respect to the number of bands and dimensions was achieved through a Genetic Algorithm.
Journal Article

An Improved, Autonomous, Multimodal Estimation Algorithm to Estimate Intent of Other Agents on the Road to Identify Most Important Object for Advanced Driver Assistance Systems Applications Using Model-Based Design Methodology

2022-04-21
Abstract Advanced Driver Assistance Systems (ADAS) are playing a significant role in enhancing driver safety and occupant comfort in modern vehicles. The primary research focus in this domain includes the precise perception of the current state and the prediction of the future states of dynamic agents. To perform these tasks an intelligent agent capable of operating in the stochastic environment is implemented in the form of various ADAS features. A trajectory prediction problem can be defined using either a model-based or data-driven approach. The current article addresses the problem of trajectory prediction in the stochastic environment using a model-based approach with a quintic polynomial as a function approximator to ensure smooth acceleration trajectory for the left and right lane-change maneuvers. The task of trajectory prediction also considers the information about the vehicle dynamics, the concept of Receding Time Horizon (RTH), and the variable curvature model of the road.
Journal Article

A Comprehensive Rule-Based Control Strategy for Automated Lane Centering System

2022-04-18
Abstract To address the comfort and safety concerns related to driving vehicles, the Advanced Driver Assistance System (ADAS) is gaining huge popularity. The general architecture of autonomous vehicles includes perception, planning, control, and actuation. This article aims mainly at the controls aspect of one of the emerging ADAS features Lane Centering System (LCS). Limitations in deploying this feature from a controls point of view include maintaining the lane center with winding curvatures, dealing with the dynamic environment, optimizing controls where the perception of lane boundaries is erroneous, and, finally, concurring with the driver’s preferences. Although some research is available on LCS controls, most works are related only to the lateral controls by actuating steering. To increase the robustness, a comprehensive control strategy that involves lateral control, as well as longitudinal control along with a novel strategy to select the mode of driving, is proposed.
Technical Paper

The Virtual Boosted DISI Engine Model Development Based on Artificial Neural Networks

2022-03-29
2022-01-0383
To efficiently reduce the required experimental data and improve the prediction accuracy, a virtual engine model has been built by integrating an artificial neural network (ANN) system consisting of multiple subnets with the genetic algorithm (GA). The GA algorithm could reduce the risk of local minima and lead to a more efficient training process. The engine model has been adopted to predict the combustion phases (including CA10, CA50 and CA90), exhaust gas temperature, brake specific fuel consumption rate (be) and engine emissions which are un-burnt hydrocarbon (UBHC), NOx and CO. The results are then compared with the experimental data from around 5000 operating points of a boosted DISI engine running at universal performance map and conditions with various valve timing configurations. The mean absolute errors of combustion phases are all below 1.0 crank angle degree. The averaged errors of the exhaust gas temperature and be are 10.1 K and 1.1%, respectively.
Technical Paper

Selection of Surrogate Models with Metafeatures

2022-03-29
2022-01-0364
Modeling and simulation of ground vehicles can be a computationally expensive problem due to the complexity of high-fidelity vehicle models. Often to determine mobility metrics, multiple stochastic simulations need to be evaluated. Surrogate models, or models of models, offer a means to reduce the computational cost of these simulation efforts. Since various types of surrogate models are available to the user, choosing the best surrogate model for a simulation is mostly the challenging process. In this paper, the process of selecting surrogate models and its uses based on model metafeatures is presented. The approach formulates this decision as a trade-off among three main drivers, required dataset size (how much information is necessary to compute the surrogate model), surrogate model accuracy (how accurate the surrogate model must be) and total computational time (how much time is required for the surrogate modeling process).
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