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Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

With the evolution of telemetry technology in vehicles, Advanced Automatic Collision Notification (AACN), which detects occupants at risk of serious injury in the event of a crash and triages them to the trauma center quickly, may greatly improve their treatment. An Injury Severity Prediction (ISP) algorithm for AACN was developed using a logistic regression model to predict the probability of sustaining an Injury Severity Score (ISS) 15+ injury. National Automotive Sampling System Crashworthiness Data System (NASS-CDS: 1999-2015) and model year 2000 or later were filtered for new case selection criteria, based on vehicle body type, to match Subaru vehicle category. This new proposed algorithm uses crash direction, change in velocity, multiple impacts, seat belt use, vehicle type, presence of any older occupant, and presence of any female occupant.

As new technology is added to vehicles and traffic congestion increases, there is a concern that drivers will be overloaded. As a result, there has been considerable interest in measuring driver workload. This can be achieved using many methods, with subjective assessments such as the NASA Task Loading Index (TLX) being most popular. Unfortunately, the TLX is unanchored, so there is no way to compare TLX values between studies, thus limiting the value of those evaluations. In response, a method was created to anchor overall workload ratings. To develop this method, 24 subjects rated the workload of clips of forward scenes collected while driving on rural, urban, and limited-access roads in relation to 2 looped anchor clips. Those clips corresponded to Level of Service (LOS) A and E (light and heavy traffic) and were assigned values of 2 and 6 respectively.

Nyquist plots are a classical means to visualize a complex vibration frequency response function. By graphing the real and imaginary parts of the response, the dynamic behavior in the vicinity of resonances is emphasized. This allows insight into how modes are coupling, and also provides a means to separate the modes. Mathematical models such as Nyquist analysis are often embedded in frequency analysis hardware. While this speeds data collection, it also removes this visually intuitive tool from the engineer’s consciousness. The behavior of a single degree of freedom system will be shown to be well described by a circle on its Nyquist plot. This observation allows simple visual examination of the response of a continuous system, and the determination of quantities such as modal natural frequencies, damping factors, and modes shapes. Vibration test data from an auto rickshaw chassis are used as an example application.

Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance.

Knee airbags (KABs) are one countermeasure in newer vehicles that could influence lower extremity (LEX) injury, the most frequently injured body region in frontal crashes. To determine the effect of KABs on LEX injury for drivers in frontal crashes, the analysis examined moderate or greater LEX injury (AIS 2+) in two datasets. Logistic regression considered six main effect factors (KAB deployment, BMI, age, sex, belt status, driver compartment intrusion). Eighty-five cases with KAB deployment from the Crash Injury Research and Engineering Network (CIREN) database were supplemented with 8 cases from the International Center for Automotive Medicine (ICAM) database and compared to 289 CIREN non-KAB cases. All cases evaluated drivers in frontal impacts (11 to 1 o’clock Principal Direction of Force) with known belt use in 2004 and newer model year vehicles. Results of the CIREN/ICAM dataset were compared to analysis of a similar dataset from NASS-CDS (5441 total cases, 418 KAB-deployed).

The article solves the problem of reducing electric power losses of the traction induction machine rotor to prevent its overheating in nominal and high-load modes. Electric losses of the rotor power are optimized by the stabilization of the main magnetic flow of the electric machine at a nominal level with the amplitude-frequency control in a wide range of speeds and increased loads. The quasi-independent excitation of the induction machine allows us to increase the rigidity of mechanical characteristics, decrease the rotor slip at nominal loads and overloads and significantly decrease electrical losses in the rotor as compared to other control methods. The article considers the technology of converting the power of individual phases into a single energy flow using a three-phase electric machine equivalent circuit and obtaining an energy model in the form of equations of instantaneous active and reactive power balance.

In order to improve the steering stability of the dual-motor drive electric vehicle, Taking the yaw rate and the sideslip angle as the control variables, Using the improved two degree of freedom linear dynamic model and seven degree of freedom nonlinear vehicle dynamics model, The hierarchical structure is used to establish the dual-motor drive electric vehicle steering stability control strategy which consist of the upper direct yaw moment decision-making layer based on the sliding mode controller and the lower additional yaw moment distribution layer based on the optimization theory. The Matlab/Simulink-Carsim joint simulation platform was built. The control strategy proposed in this paper was simulated and verified under the snake test condition and double-line shift test condition.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

Optimizing the trade-off between reliability and cost of operating a microgrid, including vehicles as both loads and sources, can be a challenge. Optimal energy management is crucial to develop strategies to improve the efficiency and reliability of microgrids, as well as new communication networks to support optimal and reliable operation. Prior approaches modeled the grid using MATLAB, but did not include the detailed physics of loads and sources, and therefore missed the transient effects that are present in real-time operation of a microgrid. This article discusses the implementation of a physics-based detailed microgrid model including a diesel generator, wind turbine, photovoltaic array, and utility. All elements are modeled as sources in Simulink. Various loads are also implemented including an asynchronous motor. We show how a central control algorithm optimizes the microgrid by trying to maximize reliability while reducing operational cost.

The objective of this study was to optimize the occupant restraint systems for a light tactical vehicle in frontal crashes. A combination of sled testing and computational modeling were performed to find the optimal seatbelt and airbag designs for protecting occupants represented by three size of ATDs and two military gear configurations. This study started with 20 sled frontal crash tests to setup the baseline performance of existing seatbelts, which have been presented previously; followed by parametric computational simulations to find the best combinations of seatbelt and airbag designs for different sizes of ATDs and military gear configurations involving both driver and passengers. Then 12 sled tests were conducted with the simulation-recommended restraint designs. The test results were further used to validate the models. Another series of computational simulations and 4 sled tests were performed to fine-tune the optimal restraint design solutions.

The purpose of this study was to use detailed medical information to evaluate thoracic injuries in elderly patients in real world frontal crashes. In this study, we used analytic morphomics to predict the effect of torso geometry on rib fracture, a major source of injury for the elderly. Analytic morphomics extracts body features from computed tomography (CT) scans of patients in a semi-automated fashion. Thoracic injuries were examined in front row occupants involved in frontal crashes from the International Center for Automotive Medicine (ICAM) database. Among these occupants, two age groups (age < 60 yr. [Nonelderly] and age ≥ 60 yr. [Elderly]) who suffered severe thoracic injury were analyzed. Regression analyses were conducted to investigate injury outcomes using variables for vehicle, demographics, and morphomics. Compared to the nonelderly group, the elderly group sustained more rib fractures.

Drivers often engage in secondary in-vehicle activity that is not related to vehicle control. This may be functional and/or to relieve monotony. Regardless, drivers believe they can safely do so when their perceived workload is low. In this paper, we describe a data acquisition system and machine learning based algorithms to determine perceived workload. Data collected were from on-road driving in light and heavy traffic, and individual physiological measures were recorded while the driver also performed in-vehicle tasks. Initial results show how the workload function can be personalized to an individual, and what implications this may have for vehicle design.

Mercury is a high-fidelity, physics-based object-oriented software for conducting simulations of vehicle performance evaluations for requirements and engineering metrics. Integrating cutting-edge, massively parallel modeling techniques for soft, cohesive and dry granular soil that will integrate state-of-the-art soil simulation with high-fidelity multi-body dynamics and powertrain modeling to provide a comprehensive mobility simulator for ground vehicles. The Mercury implements the Chrono::Vehicle dynamics library for vehicle dynamics, which provides multi-body dynamic simulation of wheeled and tracked vehicles. The powertrain is modeled using the Powertrain Analysis Computational Environment (PACE), a behavior-based powertrain analysis based on the U.S. Department of Energy’s Autonomie software. Vehicle -terrain interaction (VTI) is simulated with the Ground Contact Element (GCE), which provides forces to the Chrono-vehicle solver.

ADAS features development involves multidisciplinary technical fields, as well as extensive variety of different sensors and actuators, therefore the early design process requires much more resources and time to collaborate and implement. This paper will demonstrate an alternative way of developing prototype ADAS concept features by using remote control car with low cost hobby type of controllers, such as Arduino Due and Raspberry Pi. Camera and a one-beam type Lidar are implemented together with Raspberry Pi. OpenCV free open source software is also used for developing lane detection and object recognition. In this paper, we demonstrate that low cost frame work can be used for the high level concept algorithm architecture, development, and potential operation, as well as high level base testing of various features and functionalities. The developed RC vehicle can be used as a prototype of the early design phase as well as a functional safety testing bench.

This paper proposes a method to make diagnostic/prognostic judgment about the health of a tire, in term of its wear, using existing on-board sensor signals. The approach focuses on using an estimate of the effective rolling radius (ERR) for individual tires as one of the main diagnostic/prognostic means and it determines if a tire has significant wear and how long it can be safely driven before tire rotation or tire replacement are required. The ERR is determined from the combination of wheel speed sensor (WSS), Global Positioning sensor (GPS), the other motion sensor signals, together with the radius kinematic model of a rolling tire. The ERR estimation fits the relevant signals to a linear model and utilizes the relationship revealed in the magic formula tire model. The ERR can then be related to multiple sources of uncertainties such as the tire inflation pressure, tire loading changes, and tire wear.

The efficiency of power transmission through a Van Doorne type Continuously Variable Transmission (CVT) can be improved by allowing a small amount of relative slip between the engine and driveline side pulleys. However, excessive slip must be avoided to prevent transmission wear and damage. To enable fuel economy improvements without compromising drivability, a CVT control system must ensure accurate tracking of the gear ratio set-point while satisfying pointwise-in-time constraints on the slip, enforcing limits on the pulley forces, and counteracting driveline side and engine side disturbances. In this paper, the CVT control problem is approached from the perspective of Model Predictive Control (MPC). To develop an MPC controller, a low order nonlinear model of the CVT is established. This model is linearized at a selected operating point, and the resulting linear model is extended with extra states to ensure zero steady-state error when tracking constant set-points.

Among all the vehicle rollover test procedures, the SAE J2114 dolly rollover test is the most widely used. However, it requires the test vehicle to be seated on a dolly with a 23° initial angle, which makes it difficult to test a vehicle over 5,000 kg without a dolly design change, and repeatability is often a concern. In the current study, we developed and implemented a new dynamic rollover test methodology that can be used for evaluating crashworthiness and occupant protection without requiring an initial vehicle angle. To do that, a custom cart was designed to carry the test vehicle laterally down a track. The cart incorporates two ramps under the testing vehicle’s trailing-side tires. In a test, the cart with the vehicle travels at the desired test speed and is stopped by a track-mounted curb.

Recent developments in time-dependent reliability have introduced the concept of a composite limit state. The composite limit state method can be used to calculate the time-dependent probability of failure for dynamic systems with limit-state functions of input random variables, input random processes and explicit in time. The probability of failure can be calculated exactly using the composite limit state if the instantaneous limit states are linear, forming an open or close polytope, and are functions of only two random variables. In this work, the restriction on the number of random variables is lifted. The proposed algorithm is accurate and efficient for linear instantaneous limit state functions of any number of random variables. An example on the design of a hydrokinetic turbine blade under time-dependent river flow load demonstrates the accuracy of the proposed general composite limit state approach.