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Digital twin technology has become impactful in Industry 4.0 as it enables engineers to design, simulate, and analyze complex systems and products. As a result of the synergy between physical and virtual realms, innovation in the “real twin” or actual product is more effectively fostered. The availability of verified computer models that describe the target system is important for realistic simulations that provide operating behaviors that can be leveraged for future design studies or predictive maintenance algorithms. In this paper, a digital twin is created for an offroad tracked vehicle that can operate in either autonomous or remote-control modes. Mathematical models are presented and implemented to describe the twin track and vehicle chassis governing dynamics. These components are interfaced through the nonlinear suspension elements and distributed bogies.

Modeling uncertainties pose a significant challenge in the development and deployment of model-based vehicle control systems. Most model- based automotive control systems require the use of a well estimated vehicle dynamics prediction model. The ability of first principles-based models to represent vehicle behavior becomes limited under complex scenarios due to underlying rigid physical assumptions. Additionally, the increasing complexity of these models to meet ever-increasing fidelity requirements presents challenges for obtaining analytical solutions as well as control design. Alternatively, deterministic data driven techniques including but not limited to deep neural networks, polynomial regression, Sparse Identification of Nonlinear Dynamics (SINDy) have been deployed for vehicle dynamics system identification and prediction.

Design of internal combustion (IC) engine pistons is dependent on accurate prediction of the temperature field in the component. Experimental temperature measurements can be taken but are costly and typically limited to a few select locations. High-fidelity computer simulations can be used to predict the temperature at any number of locations within the model, but the models must be calibrated for the predictions to be accurate. The largest barrier to calibration of piston thermal models is estimating the backside boundary conditions, as there is not much literature available for these boundary conditions. Bayesian model calibration is a common choice for model calibration in literature, but little research is available applying this method to piston thermal models. Neural networks have been shown in literature to be effective for calibration of piston thermal models.

Semiconductor devices in electric vehicle (EV) motor drive systems are considered the most fragile components with a high occurrence rate for open circuit fault (OCF). Various signal-based and model-based methods with explicit mathematical models have been previously published for OCF diagnosis. However, this proposed work presents a model-free machine learning (ML) approach for a single-switch OCF detection and localization (DaL) for a two-level, three-phase inverter. Compared to already available ML models with complex feature extraction methods in the literature, a new and simple way to extract OCF feature data with sufficient classification accuracy is proposed. In this regard, the inherent property of active thermal management (ATM) based model predictive control (MPC) to quantify the conduction losses for each semiconductor device in a power converter is integrated with an ML network.

Access control enforces security policies for controlling critical resources. For V2X (Vehicle to Everything) autonomous military vehicle fleets, network middleware systems such as ROS (Robotic Operating System) expose system resources through networked publisher/subscriber and client/server paradigms. Without proper access control, these systems are vulnerable to attacks from compromised network nodes, which may perform data poisoning attacks, flood packets on a network, or attempt to gain lateral control of other resources. Access control for robotic middleware systems has been investigated in both ROS1 and ROS2. Still, these implementations do not have mechanisms for evaluating a policy's consistency and completeness or writing expressive policies for distributed fleets. We explore an RBAC (Role-Based Access Control) mechanism layered onto ROS environments that uses local permission caches with precomputed truth tables for fast policy evaluation.

Image segmentation has historically been a technique for analyzing terrain for military autonomous vehicles. One of the weaknesses of image segmentation from camera data is that it lacks depth information, and it can be affected by environment lighting. Light detection and ranging (LiDAR) is an emerging technology in image segmentation that is able to estimate distances to the objects it detects. One advantage of LiDAR is the ability to gather accurate distances regardless of day, night, shadows, or glare. This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles. The volume of points generated by LiDAR provides the vehicle with distance and spatial data surrounding the vehicle.

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).

The integration of sensors, actuators, and real-time control in transportation systems enables intelligent system operation to minimize energy consumption and maximize occupant safety and vehicle reliability. The operating cycle of military ground vehicles can be on- and off-road in harsh weather and adversarial environments, which demands continuous subsystem functionality to fulfill missions. Onboard diagnostic systems can alert the operator of a degraded operation once established fault thresholds are exceeded. An opportunity exists to estimate vehicle maintenance needs using model-based predicted trends and eventually compiled information from fleet operating databases. A digital twin, created to virtually describe the dynamic behavior of a physical system using computer-mathematical models, can estimate the system behavior based on current and future operating scenarios while accounting for past effects.

Strategies for evaluating the impact of mission requirements on the design of mission-specific vehicles are needed to enable project managers to assess potential benefits and associated costs of changes in requirements. Top-level requirements that cause significant cascaded difficulties on lower-level requirements should be identified and presented to decision-makers. This paper aims to introduce formal methods and computational tools to enable the analysis and allocation of mission requirements.

Substantial fuel economy improvements for light-duty automotive engines demand novel combustion strategies. Low temperature combustion (LTC) demonstrates potential for significant fuel efficiency improvement; however, control complexity is an impediment for real-world transient operation. Spark-assisted compression ignition (SACI) is an LTC strategy that applies a deflagration flame to generate sufficient energy to trigger autoignition in the remaining charge. Operating a practical engine with SACI combustion is a key modeling and control challenge. Current models are not sufficient for control-oriented work such as calibration optimization, transient control strategy development, and real-time control. This work describes the process and results of developing a fast-running control-oriented model for the autoignition phase of SACI combustion. A data-driven model is selected, specifically artificial neural networks (ANNs).

Over the years, the complexity of autonomous vehicle development (and concurrently the verification and validation) has grown tremendously in terms of component-, subsystem- and system-level interactions between autonomy and the human users. Simulation-based testing holds significant promise in helping to identify both problematic interactions between component-, subsystem-, and system-levels as well as overcoming delays typically introduced by the default full-scale on-road testing. Software in Loop (SiL) simulation is utilized as an intermediate step towards software deployment for autonomous vehicles (AV) to make them reliable. SiL efforts can help reduce the resources required for successful deployment by helping to validate the software for millions of road miles. A key enabler for accelerating SiL processes is the ability to use Simulation as a Service (SaaS) rather than just isolated instances of software.

Driving inattention caused by drowsiness has been a significant reason for vehicle crash accidents, and there is a critical need to augment driving safety by monitoring driver drowsiness behaviors. For real-time drowsy driving awareness, we propose a vision-based driver drowsiness monitoring system (DDMS) for driver drowsiness behavior recognition and analysis. First, an infrared camera is deployed in-vehicle to capture the driver’s facial and head information in naturalistic driving scenarios, in which the driver may or may not wear glasses or sunglasses. Second, we propose and design a multi-modal features representation approach based on facial landmarks, and head pose which is retrieved in a convolutional neural network (CNN) regression model. Finally, an extreme learning machine (ELM) model is proposed to fuse the facial landmark, recognition model and pose orientation for drowsiness detection. The DDMS gives promptly warning to the driver once a drowsiness event is detected.

This paper presents an approach for performing software in the loop testing of autonomous vehicle software developed in the Autoware framework. Autoware is an open source software for autonomous driving that includes modules such as localization, detection, prediction, planning and control [8]. Multitudes of autonomous driving frameworks exist today, each having its own pros and cons. Often, MATLAB-Simulink is used for rapid prototyping, system modeling and testing, specifically for the lower-level vehicle dynamics and powertrain control features. For the autonomous software, the Robotic Operating System (ROS) is more commonly used for integrating distributed software components so that they can easily share information through a publish and subscribe paradigm. Thorough testing and evaluation of such complex, distributed software, implemented on a physical vehicle poses significant challenges in terms of safety, time, and cost, especially when considering rare edge cases.

Unmanned ground vehicles (UGVs) may encounter difficulties accommodating environmental uncertainties and system degradations during harsh conditions. However, human experience and onboard intelligence can may help mitigate such cases. Unfortunately, human operators have cognition limits when directly supervising multiple UGVs. Ideally, an automated decision aid can be designed that empowers the human operator to supervise the UGVs. In this paper, we consider a connected UGV platoon under cyber attacks that may disrupt safety and degrade performance. An observer-based resilient control strategy is designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. In addition, each UGV generates both internal and external evaluations based on the platoons performance metrics. A cloud-based trust-based information management system collects these evaluations to detect abnormal UGV platoon behaviors.

Automotive drive-by-wire systems have enabled greater mobility options for individuals with physical disabilities. To further expand the driving paradigm, a need exists to consider an alternative vehicle steering mechanism to meet specific needs and constraints. In this study, a cellphone steering controller was investigated using a fixed-base driving simulator. The cellphone incorporated the direction control of the vehicle through roll motion, as well as the brake and throttle functionality through pitch motion, a design that can assist disabled drivers by excluding extensive arm and leg movements. Human test subjects evaluated the cellphone with conventional vehicle control strategy through a series of roadway maneuvers. Specifically, two distinctive driving situations were studied: a) obstacle avoidance test, and b) city road traveling test. A conventional steering wheel with self-centering force feedback tuning was used for all the driving events for comparison.

Non-linear model predictive engine control (nMPC) systems have the ability to reduce calibration effort while improving transient engine response. The main drawback of nMPC for engine control is the computational power required to realize real-time operation. Most of this computational power is spent linearizing the non-linear plant model at each time step. Additionally, the effectiveness of the nMPC system relies heavily on the accuracy of the model(s) used to predict the future system behavior, which can be difficult to model physically. This paper introduces a hybrid modeling approach for internal combustion engines that combines physics-based and machine learning techniques to generate accurate models that can be linearized with low computational power. This approach preserves the generalization and robustness of physics-based models, while maintaining high accuracy of data-driven models. Advantages of applying the proposed model with nMPC are discussed.

Learning only from a small set of examples remains a huge challenge in machine learning. Despite recent breakthroughs in the applications of neural networks, the applicability of these techniques has been limited by the requirement for large amounts of training data. What’s more, the standard supervised machine learning method does not provide a satisfactory solution for learning new concepts from little data. However, the ability to learn enough information from few samples has been demonstrated in humans. This suggests that humans may make use of prior knowledge of a previously learned model when learning new ones on a small amount of training examples. In the area of autonomous driving, the model learns to drive the vehicle with training data from humans, and most machine learning based control algorithms require training on very large datasets. Collecting and constructing training data set takes a huge amount of time and needs specific knowledge to gather relevant information.

The advancements in automation, big data computing and high bandwidth networking has expedited the realization of Industrial Internet of Things (IIoT). IIoT has made inroads into many sectors including automotive, semiconductors, electronics, etc. Particularly, it has created numerous opportunities in the automotive manufacturing sector to realize the new aura of platform concepts such as smart material flow control. This paper provides a thought provoking application of IIoT in automotive composites body shop. By creating a digital twin for every physical part, we no longer need to adhere to the conventional manufacturing processes and layouts, thus opening up new opportunities in terms of equipment and space utilization. The century-old philosophy of the assembly line might not be the best layout for vehicle manufacturing, thus proposing a novel assembly grid layout inspired from a colony of ants working to accomplish a common goal.

Accurate in-cylinder air charge estimation is important for engine torque determination, controlling air-to-fuel ratio, and ensuring high after-treatment efficiency. Spark ignition (SI) engine technologies like variable valve timing (VVT) and exhaust gas recirculation (EGR) are applied to improve fuel economy and reduce pollutant emissions, but they increase the complexity of air charge estimation. Increased air-path complexity drives the need for cost effective solutions that produce high air mass prediction accuracy while minimizing sensor cost, computational effort, and calibration time. A large number of air charge estimation techniques have been developed using a range of sensors sets combined with empirical and/or physics-based models. This paper provides a technical review of research in this area, focused on SI engines.

A safe driver program has been established through a public-private partnership. This program targets novice drivers and uses a combination of classroom and in-vehicle training exercises to address critical driver errors known to lead to crashes. Students participate in four modules: braking to learn proper stopping technique, obstacle avoidance / reaction time to facilitate proper lane selection and collision avoidance, tailgating to learn about following distances, and loss of control to react appropriately when a vehicle is about to become laterally unstable. Knowledge pre and posttests are also administered at the start and end of the program. Students' in-vehicle driving performance are evaluated by instructors as well as recorded by onboard data acquisition units. The data has been evaluated with objective and subjective grading rubrics. The 70 participants in three classes used as a case study achieved an average skill score of 83.93/100.