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This paper investigates the utilization of Low Earth Orbit (LEO) satellites for vehicle localization and conducts a comparative analysis with traditional Global Navigation Satellite Systems (GNSS)-based methods. With the rise of LEO satellite constellations, such as Starlink, LEO-based vehicle localization may offer solutions to GNSS-related challenges. With a large number of satellites and short communication distance, the LEO-based method has great potential to improve accuracy, reduce warm-up time, and provide a robust localization solution for vehicle applications. In this paper, a dedicated LEO satellite simulator is presented, adaptable to various LEO constellations, making it relevant for evolving technologies beyond older LEO systems like Orbcomm or Iridium. The simulator includes satellite trajectory generation, observable satellite identification, and vehicle localization.

Over the past decade, significant progress has been made in developing algorithms and improving hardware for automated driving. However, conducting research and deploying advanced algorithms on automated vehicles for testing and validation remains costly, especially for academia. This paper presents the efforts of our research team to integrate the newest version of the open-source Autoware software with the commercially available DataSpeed Drive-by-Wire (DBW) system, resulting in the creation of a versatile and robust automated vehicle research platform. Autoware, an open-source software stack based on the 2nd generation Robot Operating System (ROS2), has gained prominence in the automated vehicle research community for its comprehensive suite of perception, planning, and control modules. The DataSpeed DBW system directly communicates with the vehicle's CAN bus and provides precise vehicle control capabilities.

Data-driven driving safety assessment is crucial in understanding the insights of traffic accidents caused by dangerous driving behaviors. Meanwhile, quantifying driving safety through well-defined metrics in real-world naturalistic driving data is also an important step for the operational safety assessment of automated vehicles (AV). However, the lack of flexible data acquisition methods and fine-grained datasets has hindered progress in this critical area. In response to this challenge, we propose a novel dataset for driving safety metrics analysis specifically tailored to car-following situations. Leveraging state-of-the-art Artificial Intelligence (AI) technology, we employ drones to capture high-resolution video data at 12 traffic scenes in the Phoenix metropolitan area. After that, we developed advanced computer vision algorithms and semantically annotated maps to extract precise vehicle trajectories and leader-follower relations among vehicles.

The rise of Software-Defined Vehicles (SDV) has rapidly advanced the development of Advanced Driver Assistance Systems (ADAS), Autonomous Vehicle (AV), and Battery Electric Vehicle (BEV) technology. While AVs need power to compute data from perception to controls, BEVs need the efficiency to optimize their electric driving range and stand out compared to traditional Internal Combustion Engine (ICE) vehicles. AVs possess certain shortcomings in the current world, but SAE Level 2+ (L2+) Automated Vehicles are the focus of all major Original Equipment Manufacturers (OEMs). The most common form of an SDV today is the amalgamation of AV and BEV technology on the same platform which is prominently available in most OEM’s lineups. As the compute and sensing architectures for L2+ automated vehicles lean towards a computationally expensive centralized design, it may hamper the most important purchasing factor of a BEV, the electric driving range.

Driver Assistance and Autonomous Driving features are becoming nearly ubiquitous in new vehicles. The intent of the Driver Assistant features is to assist the driver in making safer decisions. The intent of Autonomous Driving features is to execute vehicle maneuvers, without human intervention, in a safe manner. The overall goal of Driver Assistance and Autonomous Driving features is to reduce accidents, injuries, and deaths with a comforting driving experience. However, different drivers can react differently to advanced automated driving technology. It is therefore important to consider and improve the adaptability of these advances based on driver behavior. In this paper, a human-centric approach is adopted to provide an enriching driving experience. We perform data analysis of the naturalistic behavior of drivers when performing lane change maneuvers by extracting features from extensive Second Strategic Highway Research Program (SHRP2) data of over 5,400,000 data files.

Shell-and-tube heat exchangers, commonly referred to as radiators, are the most prevalent type of heat exchanger within the automotive industry. A pivotal goal for automotive designers is to increase their thermal effectiveness while mitigating pressure drop effects and minimizing the associated costs of design and operation. Their design is a lengthy and intricate process involving the manual creation and refinement of computer-aided design (CAD) models coupled with iterative multi-physics simulations. Consequently, there is a pressing demand for an integrated tool that can automate these discrete steps, yielding a significant enhancement in overall design efficiency. This work aims to introduce an innovative automation tool to streamline the design process, spanning from CAD model generation to identifying optimal design configurations. The proposed methodology is applied explicitly to the context of shell-and-tube heat exchangers, showcasing the tool's efficacy.

With the development of vehicles equipped with automated driving systems, the need for systematic evaluation of AV performance has grown increasingly imperative. According to ISO 34502, one of the safety test objectives is to learn the minimum performance levels required for diverse scenarios. To address this need, this paper combines two essential methodologies - scenario-based testing procedures and scoring systems - to systematically evaluate the behavioral competence of AVs. In this study, we conduct comprehensive testing across diverse scenarios within a simulator environment following Mcity AV Driver Licensing Test procedure. These scenarios span several common real-world driving situations, including BV Cut-in, BV Lane Departure into VUT Path from Opposite Direction, BV Left Turn Across VUT Path, and BV Right Turn into VUT Path scenarios.

Ensuring the safety of vulnerable road users (VRUs) such as pedestrians, users of micro-mobility vehicles, and cyclists is imperative for the commercialization of automated vehicles (AVs) in urban traffic scenarios. City traffic intersections are of particular concern due to the precarious situations VRUs often encounter when navigating these locations, primarily because of the unpredictable nature of urban traffic. Earlier work from the Institute of Automated Vehicles (IAM) has developed and evaluated Driving Assessment (DA) metrics for analyzing car following scenarios. In this work, we extend those evaluations to an urban traffic intersection testbed located in downtown Tempe, Arizona. A multimodal infrastructure sensor setup, comprising a high-density, 128-channel LiDAR and a 720p RGB camera, was employed to collect data during the dusk period, with the objective of capturing data during the transition from daylight to night.

Rapid adoption of battery electric vehicles means improving the energy consumption and energy efficiency of these new vehicles is a top priority. One method of accomplishing this is regenerative braking, which converts kinetic energy to electrical energy stored in the battery pack while the vehicle is decelerating. Coasting is an alternative strategy that minimizes energy consumption by decelerating the vehicle using only road load. A battery electric vehicle model is refined to assess regenerative braking, coasting, and other deceleration strategies. A road load model based on public test data calculates tractive effort requirements based on speed and acceleration. Bidirectional Willans lines are the basis of a powertrain model simulating battery energy consumption. Vehicle tractive and powertrain power are modeled backward from prescribed linear velocity curves, and the coasting trajectory is forward modeled given zero tractive power.

In the past decades, automotive structure design has sought to minimize its mass while maintaining or improving structural performance. As such, topology optimization (TO) has become an increasingly popular tool during the conceptual design stage. While the designs produced by TO methods provide significant performance-to-mass ratio improvements, they require considerable computational resources when solving large-scale problems. An alternative for large-scale problems is to decompose the design domain into multiple scales that are coupled with homogenization. The problem can then be solved with hierarchical multiscale topology optimization (MSTO). The resulting optimal, homogenized macroscales are de-homogenized to obtain a high-fidelity, physically-realizable design. Even so MSTO methods are still computationally expensive due to the combined costs of solving nested optimization problems and performing de-homogenization.

Structural Health Monitoring (SHM), especially in the field of rotary machinery diagnosis, plays a crucial role in determining the defect category as well as its intensity in a machine element. This paper proposes a new framework for real-time classification of structural defects in a roller bearing test rig using time domain-based classification algorithms. Along with the bearing defects, the effect of eccentric shaft loading has also been analyzed. The entire system comprises of three modules: sensor module – using accelerometers for data collection, data processing module – using time-domain based signal processing algorithms for feature extraction, and classification module – comprising of deep learning algorithms for classifying between different structural defects occurring within the inner and outer race of the bearing.

The operational safety of Automated Driving System (ADS)-Operated Vehicles (AVs) are a rising concern with the deployment of AVs as prototypes being tested and also in commercial deployment. The robustness of safety evaluation systems is essential in determining the operational safety of AVs as they interact with human-driven vehicles. Extending upon earlier works of the Institute of Automated Mobility (IAM) that have explored the Operational Safety Assessment (OSA) metrics and infrastructure-based safety monitoring systems, in this work, we compare the performance of an infrastructure-based Light Detection And Ranging (LIDAR) system to an onboard vehicle-based LIDAR system in testing at the Maricopa County Department of Transportation SMARTDrive testbed in Anthem, Arizona. The sensor modalities are located in infrastructure and onboard the test vehicles, including LIDAR, cameras, a real-time differential GPS, and a drone with a camera.

The operational safety of Automated Driving System-equipped vehicles (AVs) is a critical issue with AVs being deployed on public roads. Methodologies for evaluating the operational safety are therefore necessary to maintain public safety. One possible approach is a safety case established by the AV developer that uses evidence to support a structured argument that the AV exhibits a given level of operational safety. One of the key components of a safety case for AVs is a set of testing results showing behavioral competency in a variety of scenarios within the AV’s operational design domain (ODD). The Institute of Automated Mobility (IAM) has previously published operational safety assessment (OSA) metrics along with a means to evaluate the severity of violations of the safety envelope-type OSA metrics for navigation of individual scenarios in the proposed OSA Methodology.

Due to increased urgency regarding environmental concerns within the transportation industry, sustainable solutions for combating climate change are in high demand. One solution is a widespread transition from internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs). To facilitate this transition, reliable energy consumption modeling is desired for providing quick, high-level estimations for a BEV without requiring extensive vehicle and computational resources. Therefore, the goal of this paper is to create a simple, yet reliable vehicle model, that can estimate the energy consumption of most electric vehicles on the market by using parameter normalization techniques. These vehicle parameters include the vehicle test weight and performance to obtain a unified net Willans line to describe the input/output power using a linear relationship.

The safety, performance, and operational life of power dense Lithium-ion batteries used in Hybrid and Electric Vehicles are dependent on the operating temperature. Modeling and simulation are essential tools used to accelerate the design process of optimal thermal management systems. However, high-fidelity 3D computational fluid dynamics (CFD) simulation of such systems is often difficult and computationally expensive. In this paper, we demonstrate a multi-part coupled system model for simulating the heating/cooling system of the traction battery at a module level. We have reduced computational time by employing reduced-order modeling (ROM) framework on separate 3D CFD models of the battery module and the cooling plate. The order of the thermal ROM has also been varied to study the trade-off between accuracy, fidelity, and complexity. The ROMs are bidirectionally coupled to an empirical battery model built from in-house test data.

A common scenario in engineering design is the evaluation of expensive black-box functions: simulation codes or physical experiments that require long evaluation times and/or significant resources, which results in lengthy and costly design cycles. In the last years, Bayesian optimization has emerged as an efficient alternative to solve expensive black-box function design problems. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition functions that drives the design process. Successful Bayesian optimization strategies are characterized by accurate surrogate models and well-balanced acquisition functions. The Gaussian process (GP) regression model is arguably the most popular surrogate model in Bayesian optimization due to its flexibility and mathematical tractability. GP regression models are defined by two elements: the mean and covariance functions.

The significance and the number of vehicle safety features enabled via connectivity continue to increase. OnStar, with its automatic airbag notification, was one of the first vehicle safety features that demonstrate the enhanced safety benefits of connectivity. Vehicle connectivity benefits have grown to include remote software updates, data analytics to aid with preventative maintenance and even to theft prevention and recovery. All of these services require available and reliable connectivity. However, except for the airbag notification, none have strict latency requirements. For example, software updates can generally be postponed till reliable connectivity is available. Data required for prognostic use cases can be stored and transmitted at a later time. A new set of use cases are emerging that do demand continuous, reliable and low latency connectivity. For example, remote control of autonomous vehicles may be required in unique situations.

Assurance of the operational safety of automated vehicles (AVs) is crucial to enable commercialization and deployment on public roads. The operational safety must be quantified without ambiguity using well-defined metrics. Several efforts are in place to establish an appropriate set of metrics that can quantify the operational safety of AVs in a technology-neutral way, including the Operational Safety Assessment (OSA) metrics proposed by the Institute of Automated Mobility (IAM). The focus of this work is to compute real-world measurements of the relevant safety envelope OSA metrics in car-following scenarios. This allows for an analysis of the impact of different parameters and thresholds and for an evaluation of the individual usefulness of the safety envelope OSA metrics. The current work complements prior IAM work involving evaluating the safety envelope OSA metrics in car-following scenarios in simulation.

The successful deployment of automated vehicles (AVs) has recently coincided with the use of off-board sensors for assessments of operational safety. Many intersections and roadways have monocular cameras used primarily for traffic monitoring; however, monocular cameras may not be sufficient to allow for useful AV operational safety assessments to be made in all operational design domains (ODDs) such as low ambient light and inclement weather conditions. Additional sensor modalities such as Light Detecting and Ranging (LiDAR) sensors allow for a wider range of scenarios to be accommodated and may also provide improved measurements of the Operational Safety Assessment (OSA) metrics previously introduced by the Institute of Automated Mobility (IAM).

The pre-prototype development of a simulated rule-based hybrid energy management strategy for a 2019 Chevrolet Blazer RS converted parallel P4 full hybrid is presented. A vehicle simulation model is developed using component bench data and validated using EPA-reported dynamometer fuel economy test data. A combined Willans line model is proposed for the engine and transmission, with hybrid control rules based on efficiency-derived engine power thresholds. Algorithms are proposed for battery state of charge (SOC) management including engine loading and one pedal strategies, with battery SOC maintained within 20% to 80% safe limits and charge balanced behavior achieved. The simulated rule-based hybrid control strategy for the hybrid vehicle has an energy consumption reduction of 20% for the Hot 505, 3.6% for the HwFET, and 12% for the US06 compared to the stock vehicle.