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With the advancement of simulation software development, the efficiency of vehicle and powertrain controls research and development can be significantly improved. Traditionally, during the development of a new control algorithm, dyno or on-road testing is necessary to validate the algorithm. Physical testing is not only costly, but also time consuming. In this study, a virtual platform is developed to reduce the effort of testing. To improve the simulation accuracy, co-simulation of multiple software is suggested as each software specializes in certain area. The Platform includes Matlab Simulink, PTV Vissim, Tass Prescan and AVL Cruise. PTV Vissim is used to provide traffic environment to PreScan. PreScan is used for ego vehicle simulation and visualization. Traffic, signal and road network are synchronized in Vissim and PreScan. Powertrain system is simulated in Cruise. MATALB/Simulink serves as master of this co-simulation, and integrates the different software together.

As the technology in autonomous vehicle and smart city infrastructure is developing fast, the idea of smart city and automated driving has become a present and near future reality. Both Highway Chauffeur and low speed shuttle applications are tested recently in different research to test the feasibility of autonomous vehicles and automated driving. Based on examples available in the literature and the past experience of the authors, this paper proposes the use of a unified computing, sensing, communication and actuation architecture for connected and automated driving. It is postulated that this unified architecture will also lead to a scalable and replicable approach. Two vehicles representing a passenger car and a small electric shuttle for smart mobility in a smart city are chosen as the two examples for demonstrating scalability and replicability.

This research focuses on the development and performance of analytical models to simulate a tractor-semitrailer in straight-ahead braking. The simulations were modified and tuned to simulate full-treadle braking with all brakes functioning correctly, as well as the behavior of the tractor-semitrailer rig under full braking with selected brakes disabled. The models were constructed in TruckSim and based on a tractor-semitrailer used in dry braking performance testing. The full-scale vehicle braking research was designed to define limits for engineering estimates on stopping distance when Class 8 air-braked vehicles experience partial degradation of the foundation brake system. In the full scale testing, stops were conducted from 30 mph and 60 mph, with the combination loaded to 80,000 lbs (gross combined weight or GCW), half payload, and with the tractor-semitrailer unladen (lightly loaded vehicle weight, or LLVW).

For realistic traffic modeling, real-world traffic calibration data is needed. These data include a representative road network, road users count by type, traffic lights information, infrastructure, etc. In most cases, this data is not readily available due to cost, time, and confidentiality constraints. Some open-source data are accessible and provide this information for specific geographical locations, however, it is often insufficient for realistic calibration. Moreover, the publicly available data may have errors, for example, the Open Street Maps (OSM) does not always correlate with physical roads. The scarcity, incompleteness, and inaccuracies of the data pose challenges to the realistic calibration of traffic models. Hence, in this study, we propose an approach based on spatial interpolation for addressing sparsity in vehicle count data that can augment existing data to make traffic model calibrations more accurate.

This paper outlines an improved methodology to perform calculations to verify the compliance of automotive door latch systems to minimum legal requirements as well as to perform additional due diligence calculations necessary to comprehend special cases such as roll over crashes and locally high inertial loadings. This methodology builds on the calculation method recommended by SAE J839 and provides a robust and clear approach for application of this method to cable release systems, which were not prevalent at the time J839 was originally drafted. This method is useful in and of itself but its utility is further increased by the application of the method to a Computer Aided Design (CAD) template (in this case for Catia V5), that allows some automation of the calculation process for a given latch type. This will result in a savings of time, fewer errors and allows for an iterative concurrent analysis during the design process.

With the emergence of Driving Automation Systems (SAE levels 1-5), the necessity arises for methods of evaluating these systems. However, these systems are much more challenging to evaluate than traditional safety features (SAE level 0). This is because an understanding of the Driving Automation system’s response in all possible scenarios is desired, but prohibitive to comprehensively test. Hence, this paper attempts to evaluate one such system, by modeling its behavior. The model generated parameters not only allow for objective comparison between vehicles, but also provide a more complete understanding of the system. The model can also be used to extrapolate results by simulating other scenarios without the need for conducting more tests. In this paper, low speed automated driving (also known as Traffic Jam Assist (TJA)) is studied. This study focused on the longitudinal behavior of automated vehicles while following a lead vehicle (LV) in traffic jam scenarios.

Topology optimization is a branch of structural optimization which solves an optimal material distribution problem. The resulting structural topology, for a given set of boundary conditions and constraints, has an optimal performance (e.g. minimum compliance). Conventional 3D topology optimization algorithms achieve quality optimized results; however, it is an extremely computationally intensive task which is, in general, impractical and computationally unachievable for real-world structural optimal design processes. Therefore, the current development of rapid topology optimization technology is experiencing a major drawback. To address the issues, a new approach is presented to utilize the powerful abilities of large deep learning models to replicate this design process for 3D structures. Adversarial models, primarily Wasserstein Generative Adversarial Networks (WGAN), are constructed which consist of 2 deep convolutional neural networks (CNN) namely, a discriminator and a generator.

In the last decade, the use of Driver-in-the-Loop (DiL) simulators has significantly increased in research, product development, and motorsports. To be used as a verification tool in research, simulators must show a level of correlation with real-world driving for the chosen use case. This study aims to assess the validity of a low-cost, limited travel Vehicle Dynamics Driver-in-Loop (VDDiL) simulator by comparing on-road and simulated driving data using a statistical evaluation of longitudinal and lateral metrics. The process determines if the simulator is appropriate for verifying control strategies and optimization algorithms for longitudinal vehicle dynamics and evaluates consistency in the chosen metrics. A validation process explaining the experiments, choice of metrics, and analysis tools used to perform a validation study from the perspective of the longitudinal vehicle model is shown in this study.

The design and development of new biologically inspired technologies based on intelligent materials that are capable of sensing the levels of target biomolecules and, if needed, trigger appropriate countermeasures to regulate biological processes and rhythms of the astronauts is being undertaken in our laboratories. This is accomplished by coupling biologically inspired sensors that monitor the levels of the target biomolecules with intelligent polymeric materials that can regulate the release of a countermeasure. The technology developed here integrates sensors and artificial muscle material into a self-regulating device that can perform with minimal crew intervention. Further, it takes advantage of microfabrication technology to construct lightweight and robust responsive delivery systems. These “intelligent” devices address the need for the control and regulation of biological processes and rhythms under spaceflight conditions.

Battery packs used in automotive application experience high-power demands, fast charging, and varied operating conditions, resulting in temperature imbalances that hasten degradation, reduce cycle life, and pose safety risks. The development of proper simulation tools capable of capturing both the cell electrical and thermal response including, predicting the cell’s temperature rise and distribution, is critical to design efficient and reliable battery packs. This paper presents a co-simulation model framework capable of predicting voltage, 2-D heat generation and temperature distribution throughout a cell. To capture the terminal voltage and 2-D heat generation across the cell, the simulation framework employs a high-fidelity electrical model paired with a charge balance model based on the Poisson equation. The 2-D volumetric heat generation provided by the charge balance model is used to predict the temperature distribution across the cell surface using CFD software.

RESULTS of tests performed on a modified type F-4 CFR engine show that precombustion reactions in both the fired and motored engine gave the same carbonyl products. The maximum specific yields of these carbonyls were similar for a given fuel compressed with comparable pressure-time-temperature histories in both motored- and fired-engine tests. As the motored engine seems to duplicate precombustion reactions occurring in a fired engine under normal operating conditions, the authors of this paper conclude that the motored engine, offering ease of control and sampling, is a convenient and valid tool for combustion research.

Connected vehicle (CV) technology is among the most heavily researched areas in both the academia and industry. The vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to pedestrian (V2P) communication capabilities enable critical situational awareness. In some cases, these vehicle communication safety capabilities can overcome the shortcomings of other sensor safety capabilities because of external conditions such as 'No Line of Sight' (NLOS) or very harsh weather conditions. Connected vehicles will help cities and states reduce traffic congestion, improve fuel efficiency and improve the safety of the vehicles and pedestrians. On the road, cars will be able to communicate with one another, automatically transmitting data such as speed, position, and direction, and send alerts to each other if a crash seems imminent. The main focus of this paper is the implementation of Cooperative Collision Avoidance (CCA) for connected vehicles.

Intersections have a high risk of vehicle-to-vehicle conflicts because of the overlapping traffic flow from multiple roads. To understand the factors contributing to the crashes, this study examines the common characteristics in intersection-related crash and near- crash events, such as the existence of traffic control devices, the driver at fault, and occurrence of visual obstructions. The descriptive data of the crash and near-crash events recorded in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) database is used in categorization and statistical analysis in this study. First, the events are divided into seven categories based on trajectories of the conflicting vehicles. The categorization provides the basis for in-depth analysis of crash-contributing factors in specific confliction patterns. Subsequently, descriptive statistics are used to portray each of the categories.

Testing any new safety technology of Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) requires simulation-based validation and verification. The specific scenarios used for testing, outline incidences of accidents or near-miss events. In order to simulate these scenarios, specific values for all the above parameters are required including the ego vehicle model. The ‘criticality’ of a scenario is defined in terms of the difficulty level of the safety maneuver. A scenario could be over-critical, critical, or under-critical. In over-critical scenarios, it is impossible to avoid a crash whereas, for under-critical scenarios, no action may be required to avoid a crash. The criticality of the scenario depends on various parameters e.g. speeds, distances, road/tire parameters, etc. In this paper, we propose a definition of criticality metric and identify the parameters such that a scenario becomes critical.

The connectivity between vehicles, infrastructure, and other traffic participants brings a new dimension to automotive safety applications. Soon all the newly produced cars will have Vehicle to Everything (V2X) communication modems alongside the existing Advanced Driver Assistant Systems (ADAS). It is essential to identify the different sensor measurements for the same targets (Data Association) to use connectivity reliably as a safety feature alongside the standard ADAS functionality. Considering the camera is the most common sensor available for ADAS systems, in this paper, we present an experimental implementation of a Mahalanobis distance-based data association algorithm between the camera and the Vehicle to Vehicle (V2V) communication sensors. The implemented algorithm has low computational complexity and the capability of running in real-time. One can use the presented algorithm for sensor fusion algorithms or higher-level decision-making applications in ADAS modules.

To improve the biofidelity of the currently available Hybrid III 10-year-old (HIII-10C) Anthropomorphic Test Device (ATD), the National Highway Traffic Safety Administration (NHTSA) has developed the Large Omnidirectional Child (LODC) ATD. The LODC head is a redesigned HIII-10C head with mass properties and modified skin material required to match pediatric biomechanical impact response targets from the literature. A dynamic, nonlinear finite element (FE) model of the LODC head has been developed using the mesh generating tool Hypermesh based on the three-dimensional CAD model. The material data, contact definitions, and initial conditions are defined in LS-PrePost and converted to LS-Dyna solver input format. The aluminum head skull is stiff relative to head flesh material and was thus modeled as a rigid material. For the actual LODC, the head flesh is form fit onto the skull and held in place through contact friction.

Idle Stop-and-go (ISG), also known as Auto Stop/Start, is a fuel saving technology common to many modern vehicles that enables the engine to shut down when the vehicle comes to a stop. Although it may help with fuel efficiency, many drivers in the North American market find the feature to be an annoyance due to hesitation in vehicle re-launch and engine shudder during stop or restart. This paper introduces the usage of traffic signal phase and timing (SPaT) information for controlling the activation of ISG with the goal of reducing driver complaints and increasing acceptance of the function. Previous studies proposed the utilization of Advanced Driver Assistance System (ADAS) to introduce adaptability in powertrain controls to traffic situation changes.

With the advancement of the autonomous vehicle development, the different possibilities of improving fuel economy have increased significantly by changing the driver or powertrain response under different traffic conditions. Development of new fuel-efficient driving strategies requires extensive experiments and simulations in traffic. In this paper, a fuel efficiency simulator environment with existing simulator software such as Simulink, Vissim, Sumo, and CarSim is developed in order to reduce the overall effort required for developing new fuel-efficient algorithms. The simulation environment is created by combining a mid-sized sedan MATLAB-Simulink powertrain model with a realistic microscopic traffic simulation program. To simulate the traffic realistically, real roads from urban and highway sections are modeled in the simulator with different traffic densities.

Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models.

In the last decade, the accelerated advancements in manufacturing techniques and material science enabled the automotive industry to manufacture commercial vehicles at more affordable rates. This, however, brought about roadways having to accommodate an ever-increasing number of vehicles every day. However, some roadways, during specific hours of the day, had already been on the brink of reaching their capacity to withstand the number of vehicles travelling on them. Hence, overcrowded roadways create slow traffic, and sometimes, bottlenecks. In this paper, a Dynamic Speed Harmonization (DSH) algorithm that regulates the speed of a vehicle to prevent it from being affected by bottlenecks has been presented. First, co-simulations were run between MATLAB Simulink and CarSim to test different deceleration profiles.