Browse Topic: Machine learning
Dynamic Vehicle mass is one of the most critical parameters in automotive controls such as battery management, transmission shift scheduling, distance-to-empty predictions and most importantly, various active and passive safety systems. This work aims to find out dynamic Vehicle mass for Electric Vehicles in real time transient driving conditions. The work proposes a real-time approach in finding Dynamic vehicle mass where accumulated Energy based vehicle performance, an improvement to the vehicle dynamics equation, has been employed for consistent and accurate results. Factors affecting vehicle mass such as road grade, dynamic friction coefficient, driving pattern, wheel slip etc. have been considered for model optimization. Here recursive Bayesian state estimator has been used for finding vehicle mass as a constant state variable while time varying forgetting factors are used to nullify the impact of major losses. Algorithm is auto tuned using Machine Learning techniques to first
Cybersecurity, particularly in the automotive sector, is of paramount importance in today’s digital age. With the advent of connected commercial vehicles, which leverage telematics for efficient fleet management, the landscape of automotive cybersecurity is rapidly evolving. These vehicles, integral to logistics and transportation businesses, are becoming increasingly connected, thereby escalating the risks associated with cybersecurity threats. These commercial vehicles are becoming prime targets for cyber-attacks due to their connectivity and the valuable data they hold. The potential consequences of these cyber-attacks can range from data breaches to disruptions in fleet operations, and even safety risks. This paper analyses the unique challenges faced by the commercial vehicle sector, such as the need for robust telematics systems, secure communication channels, and stringent data protection measures. Case studies of notable cybersecurity incidents involving commercial vehicles are
ABSTRACT Future autonomous combat vehicles will need to travel off-road through poorly mapped environments. Three-dimensional topography may be known only to a limited extent (e.g. coarse height), but this will likely be noisy and of limited resolution. For ground vehicles, 3D topography will impact how far ahead the vehicle can “see”. Higher vantage points and clear views provide much more useful path planning data than lower vantage points and occluded views from trees and structures. The challenge is incorporating this knowledge into a path planning solution. When should the robot climb higher to get a better view or else continue moving along the shortest path predicted by current information? We investigated the use of Deep Q-Networks (DQN) to reason over this decision space, comparing performance to conventional methods. In the presence of significant sensor noise, the DQN was more successful in finding a path to the target than A* for all but one type of terrain. Citation: E
ABSTRACT An increasing pace of technology advancements and recent heavy investment by potential adversaries has eroded the Army’s overmatch and spurred significant changes to the modernization enterprise. Commercial ground vehicle industry solutions are not directly applicable to Army acquisitions because of volume, usage and life cycle requirement differences. In order to meet increasingly aggressive schedule goals while ensuring high quality materiel, the Army acquisition and test and evaluation communities need to retain flexibility and continue to pursue novel analytic methods. Fully utilizing test and field data and incorporating advanced techniques, such as, big data analytics and machine learning can lead to smarter, more rapid acquisition and a better overall product for the Soldier. Logistics data collections during operationally relevant events that were originally intended for the development of condition based maintenance procedures in particular have been shown to provide
ABSTRACT Simulation is a critical step in the development of autonomous systems. This paper outlines the development and use of a dynamically linked library for the Mississippi State University Autonomous Vehicle Simulator (MAVS). The MAVS is a library of simulation tools designed to allow for real-time, high performance, ray traced simulation capabilities for off-road autonomous vehicles. It includes features such as automated off-road terrain generation, automatic data labeling for camera and LIDAR, and swappable vehicle dynamics models. Many machine learning tools today leverage Python for development. To use these tools and provide an easy to use interface, Python bindings were developed for the MAVS. The need for these bindings and their implementation is described. Citation: C. Hudson, C. Goodin, Z. Miller, W. Wheeler, D. Carruth, “Mississippi State University Autonomous Vehicle Simulation Library”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium
ABSTRACT Recent advances in neuroscience, signal processing, machine learning, and related technologies have made it possible to reliably detect brain signatures specific to visual target recognition in real time. Utilizing these technologies together has shown an increase in the speed and accuracy of visual target identification over traditional visual scanning techniques. Images containing a target of interest elicit a unique neural signature in the brain (e.g. P300 event-related potential) when detected by the human observer. Computer vision exploits the P300-based signal to identify specific features in the target image that are different from other non-target images. Coupling the brain and computer in this way along with using rapid serial visual presentation (RSVP) of the images enables large image datasets to be accurately interrogated in a short amount of time. Together this technology allows for potential military applications ranging from image triaging for the image analyst
ABSTRACT This paper describes the use of neural networks to enhance simulations for subsequent training of anomaly-detection systems. Simulations can provide edge conditions for anomaly detection which may be sparse or non-existent in real-world data. Simulations suffer, however, by producing data that is “too clean” resulting in anomaly detection systems that cannot transition from simulated data to actual conditions. Our approach enhances simulations using neural networks trained on real-world data to create outputs that are more realistic and variable than traditional simulations. Citation: P.Feldman, “Training robust anomaly detection using ML-Enhanced simulations”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2020
ABSTRACT This paper will explore the opportunities for artificial intelligence (AI) in the system engineering domain, particularly in ways that unite the unique capabilities of the systems engineer with the AI. This collaboration of human and machine intelligence is known as Augmented Intelligence (AuI). There is little doubt that systems engineering productivity could be improved with effective utilization of well-established AI techniques, such as machine learning, natural language processing, and statistical models. However, human engineers excel at many tasks that remain difficult for AIs, such as visual interpretation, abstract pattern matching, and drawing broad inferences based on experience. Combining the best of AI and human capabilities, along with effective human/machine interactions and data visualization, offers the potential for orders-of-magnitude improvements in the speed and quality of delivered
ABSTRACT The Soar Cognitive Architecture is a reasoning system that enables knowledge-rich, mission focused reasoning including integration of bottom-up, sensor-driven reasoning and top-down, context-driven reasoning, and more intelligent use of existing sensors. This reasoning is a combination of deliberate (e.g., planning) and reactive (e.g., hard-coded) behaviors. We are applying Soar on a current effort to (1) increase autonomy and (2) achieve equivalent or superior performance while controlling weight, energy, and costs
ABSTRACT Analytical performance assessment of Active Protection Systems (APS) and the vulnerability assessment of ground vehicles using classical physics-based modeling and simulations has many challenges. Also, modeling many of the factors involved in the interaction during Hard-Kill (HK) of the incoming threat with a countermeasure and the resulting outcomes are quite complex and have varied effects on the survivability of the vehicle. Therefore, relying only on deterministic solutions, are time consuming and computationally cost prohibitive. This effort is focused on changing this paradigm by researching for a suitable machine learning algorithm which takes in simulation data from high fidelity physics-based models as training data. Through decomposition, interpolation and reconstruction techniques, surrogate models can be constructed using the simulation data. These surrogate models can then be used for a quick assessment (fraction of a second compared to a day per simulation
ABSTRACT Due to the high complexity of modern internal combustion engines and powertrain systems, the proper calibration of the electronic control unit’s (ECU) parameters has a strong impact on project targets like fuel consumption, emissions and drivability, as well as development costs and project duration. Simulation methods representing the system behavior with a model can support the calibration process considerably. However, standard physics-based models are often not able to describe all effects with sufficient accuracy, or the effort to set up a detailed model is too high. Physics-based models can also have a high computational demand, so that their simulation is not real-time capable. More suited for ECU calibration are data-driven models, combined with Design of Experiment (DoE). The system to be calibrated is identified with few specific test bench or vehicle measurements. From these measurements, a mathematical regression model is built. This paper describes recently
ABSTRACT Traditionally, the life cycle management of military vehicle fleets is a lengthy and costly process involving maintenance crews completing numerous and oftentimes unnecessary inspections and diagnostics tests. Recent technological advances have allowed for the automation of life cycle management processes of complex systems. In this paper, we present our process for applying artificial intelligence (AI) and machine learning (ML) in the life cycle management of military vehicle fleets, using a Ground Vehicle fleet. We outline the data processing and data mapping methodologies needed for generating AI/ML model training data. We then use AI and ML methods to refine our training sets and labels. Finally, we outline a Random Forest classification model for identifying system failures and associated root causes. Our evaluation of the Random Forest model results show that our approach can predict system failures and associated root causes with 96% accuracy
ABSTRACT Machine learning (ML), artificial intelligence (AI), and computational photography (CP) are pushing the boundaries of how electro-optical (EO) and infra-red (IR) sensors are being used. Especially within military environments, users are asking much more from EO and IR sensor suites. While hardware capability continues to advance the state of the art, software has become the true differentiator for how well these sensor platforms perform for the warfighter. This paper presents work that Consolidated Resource Imaging (CRI) has been developing in the areas of machine learning and computational photography. In this effort, we will discuss two areas of understanding: imagery meant for machine vision and imagery meant for human consumption. We will show how the intersection of machine learning and computational photography allow the symbiotic relationship between the human and the computer. Citation: A. Paul Skentzos, B. Stephen Pizzo, “Balancing Between Computer and Machine Vision
ABSTRACT We present the results of an exploratory investigation of applying a hybrid quantum-classical architecture to an off-road vehicle mobility problem, namely the generation of go/no-go maps posed as a machine learning problem. The premise of this work rests on two observations. First, quantum computing allows in principle for algorithms that provide a speedup over the best known classical counterparts. However, as it is to be expected of such novel and complex tools (both hardware and algorithmic) at this early developmental stage, current quantum algorithms do not always perform well on real-world problems. Second, complex physics-based vehicle and terramechanics models and simulations, currently advocated for high-fidelity high-accuracy ground vehicle–terrain interaction analyses, pose significant computational burden, especially when applied to mobility studies which may require numerous simulation runs. We describe the Quantum-Assisted Helmholtz Machine formulation, suitable
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