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Roller offsetting is an incremental forming technique used to generate offset stiffening or mating features in sheet metal parts. Compared to die forming, roller offsetting utilizes generic tooling to create versatile designs at a relatively lower forming speed, making it well-suited for low volume productions in automotive and other industries. However, more significant distortion can be generated from roller offset forming process resulting from springback after forming. In this work, we use particle swarm optimization to identify the tool path and resulting feature geometry that minimizes distortion. In our approach, time-dependent finite element simulations are adopted to predict the distortion of each candidate tool path using a quarter symmetry model of the part. A multi-objective fitness function is used to both minimize the distortion measure while constraining the minimal radius of curvature in the tool path.

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.

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.

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced Long short-term memory (LSTM) network integrated with a state refinement module.

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.

Effectively determining automated driving system (ADS)-equipped vehicle (AV) safety without relying on testing an infeasibly large number of driving scenarios is a challenge with wide recognition in industry and academia. The following paper builds on previous work by the Institute of Automated Mobility (IAM) and Science Foundation Arizona (SFAz), and proposes a test selection and scoring methodology (TSSM) as part of a safety case-based framework being developed by the SFAz to ensure the safety of AVs while addressing the scenario testing challenge. The TSSM is an AV verification and validation (V&V) process that relies, in part, on iterative, partially random generation of AV driving scenarios. These scenarios are generated using an operational design domain (ODD) and behavioral competency portfolio, which expresses the vehicle ODD and behavioral competencies in terms of quantifiable amounts or intensities of discrete components.

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.

Layered materials are one of the most commonly used acoustical treatments in the automotive industry, and have gained increased attention, especially owing to the popularity of electric vehicles. Here, a method to model and couple layered systems with various layer types (i.e., poro-elastic layers, solid-elastic layers, stiff panels, and fluid layers) is derived that makes it possible to stably predict their acoustical properties. In contrast with most existing methods, in which an equation system is constructed for the whole structure, the present method involves only the topmost layer and its boundary conditions at two interfaces at a time, which are further simplified into an equivalent interface. As a result, for a multi-layered system, the proposed method splits a complicated system into several smaller systems and so becomes computationally less expensive.

As the vehicle and wind speeds and directions change, the unsteady flow creates non-stationary wind noise. To investigate people’s perceptions of non-stationary wind noise, a method to simulate the non-stationary wind noise is needed. Previously, a method was developed that used stationary recordings taken at several speeds and directions to create a set of sound pressure level predictions in each one-third octave band that are a function of wind speed and direction. These functions are used to create time-varying filters based on provided wind profiles. A reference wind noise measurement is then filtered to produce the sounds. A drawback of this method is that many stationary wind condition measurements are needed to form accurate sound pressure level functions, which can be time consuming. A method requiring fewer measurements was investigated.

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.

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.

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.

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).

A common scenario in engineering design is the availability of several black-box functions that describe an event with different levels of accuracy and evaluation cost. Solely employing the highest fidelity, often the most expensive, black-box function leads to lengthy and costly design cycles. Multi-fidelity modeling improves the efficiency of the design cycle by combining information from a small set of observations of the high-fidelity function and large sets of observations of the low-fidelity, fast-to-evaluate functions. In the context of Bayesian optimization, the most popular multi-fidelity model is the auto-regressive (AR) model, also known as the co-kriging surrogate. The main building block of the AR model is a weighted sum of two Gaussian processes (GPs). Therefore, the AR model is well suited to exploit information generated by sources that present strong linear correlations.

As the deployment of automated vehicles (AVs) on public roadways expands, there is growing interest in establishing metrics that can be used to evaluate vehicle operational safety. The set of Operational Safety Assessment (OSA) metrics, that include several safety envelope-type metrics, previously proposed by the Institute of Automated Mobility (IAM) are a step towards this goal. The safety envelope OSA metrics can be computed using kinematics derived from video data captured by infrastructure-based cameras and thus do not require on-board sensor data or vehicle-to-infrastructure (V2I) connectivity, though either of the latter data sources could enhance kinematic data accuracy. However, the calculation of some metrics includes certain vehicle-specific parameters that must be assumed or estimated if they are not known a priori or communicated directly by the vehicle.