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This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

The advent of Vehicle-to-Everything (V2X) communication has revolutionized the automotive industry, particularly with the rise of Advanced Driver Assistance Systems (ADAS). V2X enables vehicles to communicate not only with each other (V2V) but also with infrastructure (V2I) and pedestrians (V2P), enhancing road safety and efficiency. ADAS, which includes features like adaptive cruise control and automatic intersection navigation, relies on V2X data exchange to make real-time decisions and improve driver assistance capabilities. Over the years, the progress of V2X technology has been marked by standardization efforts, increased deployment, and a growing ecosystem of connected vehicles, paving the way for safer and more efficient automated navigation. The EcoCAR Mobility Challenge was a 4-year student competition among 12 universities across the United States and Canada sponsored by the U.S.

Driver’s license examinations require the driver to perform either a parallel parking or a similar maneuver as part of the on-road evaluation of the driver’s skills. Self-driving vehicles that are allowed to operate on public roads without a driver should also be able to perform such tasks successfully. With this motivation, the S-shaped maneuverability test of the Ohio driver’s license examination is chosen here for automatic execution by a self-driving vehicle with drive-by-wire capability and longitudinal and lateral controls. The Ohio maneuverability test requires the driver to start within an area enclosed by four pylons and the driver is asked to go to the left of the fifth pylon directly in front of the vehicle in a smooth and continuous manner while ending in a parallel direction to the initial one. The driver is then asked to go backwards to the starting location of the vehicle without stopping the vehicle or hitting the pylons.

Automated driving has become a very promising research direction with many successful deployments and the potential to reduce car accidents caused by human error. Automated driving requires automated path planning and tracking with the ability to avoid collisions as its fundamental requirement. Thus, plenty of research has been performed to achieve safe and time efficient path planning and to develop reliable collision avoidance algorithms. This paper uses a data-driven approach to solve the abovementioned fundamental requirement. Consequently, the aim of this paper is to develop Deep Reinforcement Learning (DRL) training pipelines which train end-to-end automated driving agents by utilizing raw sensor data. The raw sensor data is obtained from the Carla autonomous vehicle simulation environment here. The proposed automated driving agent learns how to follow a pre-defined path with reasonable speed automatically.

The Large Omnidirectional Child (LODC) developed by the National Highway Traffic Safety Administration (NHTSA) has an improved biofidelity over the currently available Hybrid III 10-year-old (HIII-10C) Anthropomorphic Test Device (ATD). The LODC design incorporates enhancements to many body region subassemblies, including a redesigned HIII-10C head with pediatric mass properties, and the neck, which produces head lag with Z-axis rotation at the atlanto-occipital joint, replicating the observations made from human specimens. The LODC also features a flexible thoracic spine, a multi-point thoracic deflection measurement system, skeletal anthropometry that simulates a child's sitting posture, and an abdomen that can measure belt loading directly. This study presents the development and validation of a dynamic nonlinear finite element model of the complete LODC dummy. Based on the three-dimensional CAD model, Hypermesh was used to generate a mesh of the finite element (FE) LODC model.

Recent advancements towards autonomous heavy-duty vehicles are directly associated with increased interconnectivity and software driven features. Consequently, rise of this technological trend is bringing forth safety and cybersecurity challenges in form of new threats, hazards and vulnerabilities. As per the recent UN vehicle regulation 155, several risk-based security models and assessment frameworks have been proposed to counter the growing cybersecurity issues, however, the high budgetary cost to develop the tool and train personnel along with high risk of leakage of trade secrets, hinders the automotive manufacturers from adapting these third party solutions. This paper proposes an automated Threat Assessment & Risk Analysis (TARA) framework aligned with the standard requirements, offering an easy to use and fully customizable framework. The proposed framework is tailored specifically for heavy-duty vehicular networks and it demonstrates its effectiveness on a case study.

To accurately evaluate the energy consumption benefits provided by connected and automated vehicles (CAV), it is necessary to establish a reasonable baseline virtual driver, against which the improvements are quantified before field testing. Virtual driver models have been developed that mimic the real-world driver, predicting a longitudinal vehicle speed profile based on the route information and the presence of a lead vehicle. The Intelligent Driver Model (IDM) is a well-known virtual driver model which is also used in the microscopic traffic simulator, SUMO. The Enhanced Driver Model (EDM) has emerged as a notable improvement of the IDM. The EDM has been shown to accurately forecast the driver response of a passenger vehicle to urban and highway driving conditions, including the special case of approaching a signalized intersection with varying signal phases and timing. However, most of the efforts in the literature to calibrate driver models have focused on passenger vehicles.

Super Truck II is a 48V mild hybrid class 8 truck with an all auxiliary loads powered purely by the battery pack. Electric Heating Ventilation and Air Conditioning (HVAC) load is the most prominent battery load during the hotel period, when the truck driver is resting inside the sleeper. For the PACCAR Super Truck II (ST-II) project a 48 V battery system provides the required power during the hotel period. A cabin-HVAC model estimates the electric load on the 48V battery system, allowing the control system to implement an efficient energy management strategy that avoids engine idling during the hotel period. The thermal model accounts for the sun load due to the time of day and the geographic location of the truck during the hotel period. The cabin-HVAC model has two parts. First, a grey box model with two heat exchangers (Condenser and Evaporator) working in unison with refrigerant mass flow rate as an input and HVAC load as an output.

Eigenvalues of a simple rotating flexible disk-shaft system are obtained using different methods. The shaft is supported radially by non-rigid bearings, while the disk is situated at one end of the shaft. Eigenvalues from a finite element and a multi-body dynamic tool are compared against an established analytical formulation. The Campbell diagram based on natural frequencies obtained from the tools differ from the analytical values because of oversimplification in the analytical model. Later, detailed whirl analysis is performed using AVL Excite multi-body tool that includes understanding forward and reverse whirls in absolute and relative coordinate systems and their relationships. Responses to periodic force and base excitations at a constant rotational speed of the shaft are obtained and a modified Campbell diagram based on this is developed. Whirl of the center of the disk is plotted as an orbital or phase plot and its rotational direction noted.

Gear profile deviation is the difference in gear tooth profile from the ideal involute geometry. There are many causes that result in the deviation. Deflection under load, manufacturing, and thermal effects are some of the well-known causes that have been reported to cause deviation of the gear tooth profile. The profile deviation caused by gear tooth profile deformation due to interference-fit assembly has not been discussed previously. Engine timing gear trains, transmission gearboxes, and wind turbine gearboxes are known to use interference-fit to attach the gear to the rotating shaft. This paper discusses the interference-fit joint design and the mechanism of tooth profile deformation due to the interference-fit assembly in gear trains. A new analytical method to calculate the profile slope deviation change due to interference-assembly of parallel axis spur gears is presented.

The ported shroud casing treatment for turbocharger compressors offers a wider operating flow range, elevated boost pressures at low compressor mass flow rates, and reduced broadband whoosh noise in spark-ignition internal combustion engine applications. However, the casing treatment elevates tonal noise at the blade-pass frequency (BPF). Typical rotational speeds of compressors employed in practice push BPF noise to high frequencies, which then promote multi-dimensional acoustic wave propagation within the compressor ducting. As a result, in-duct acoustic measurements become sensitive to the angular location of pressure transducers on the duct wall. The present work utilizes a steady-flow turbocharger gas stand featuring a unique rotating compressor inlet duct to quantify the variation of noise measured around the duct at different angular positions.

During high-speed rear impacts with delta-V > 25 km/h, the front seats may rotate rearward due to occupant and seat momentum change leading to possibly large seat deflection. One possible way of limiting this may be by introducing a structure that would restrict large rotations or deformations, however, such a structure would change the front seat occupant kinematics and kinetics. The goal of this study was to understand the influence of seat back restriction on head, neck and torso responses of front seat occupants when subjected to a moderate speed rear-impact. This was done by simulating a rear impact scenario with a delta-V of 37.4 km/h using LS-Dyna, with the GHBMC M50 occupant model and a manufacturer provided seat model. The study included two parts, the first part was to identify worst case scenarios using the simplified GHBMC M50-OS, and the second part was to further investigate the identified scenarios using the detailed GHBMC M50-O.

Roundabout is a unique approach of managing traffic at intersections because it relies on driver’s instincts of safety. Roundabouts are considered safer than other ways of intersection traffic management due to low speed limits, smoother merging, and reduced fatal accidents. Despite their benefits and increasing usage, there is lack of clear understanding of the roundabouts, particularly due to scarcity of data and simulation models and the complexity of the structure. Real-time and offline traffic data recorded at a roundabout provides a basis for 1) identification of the safety issues, 2) understanding unexpected and risky driver behavior, 3) proposing potential mobility solutions, and 4) developing simulation models. The processed data may be used in controlling metered roundabouts, communicating with connected and automated vehicles (CAVs) etc. In this paper an approach to obtain useful traffic information from video feed data at a roundabout is presented.

Automated driving systems (ADS) are one of the key modern technologies that are changing the way we perceive mobility and transportation. In addition to providing significant access to mobility, they can also be useful in decreasing the number of road accidents. For these benefits to be realized, candidate ADS need to be proven as safe, robust, and reliable; both by design and in the performance of navigating their operational design domain (ODD). This paper proposes a multi-pronged approach to evaluate the safety performance of a hypothetical candidate system. Safety performance is assessed through using a set of test cases/scenarios that provide substantial coverage of those potentially encountered in an ODD. This systematic process is used to create a library of scenarios, specific to a defined domain. Beginning with a system-specific ODD definition, a set of core competencies are identified.

This study is focused on exploring the possibilities of using camera and route planner images for autonomous driving in an end-to-mid learning fashion. The overall idea is to clone the humans’ driving behavior, in particular, their use of vision for ‘driving’ and map for ‘navigating’. The notion is that we humans use our vision to ‘drive’ and sometimes, we also use a map such as Google/Apple maps to find direction in order to ‘navigate’. We replicated this notion by using end-to-mid imitation learning. In particular, we imitated human driving behavior by using camera and route planner images for predicting the desired waypoints and by using a dedicated control to follow those predicted waypoints. Besides, this work also places emphasis on using minimal and cheaper sensors such as camera and basic map for autonomous driving rather than expensive sensors such Lidar or HD Maps as we humans do not use such sophisticated sensors for driving.

A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.

The assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.

The Elderly Female Dummy (EFD) is an omni-directional ATD developed to represent a vulnerable population. The EFD it is able to be 3D printed and quickly altered to meet design requirements. A recent side impact sled test series suggested that small, elderly females may be at risk of thoracic injuries in side impact crashes due to combined loading from the belt pre-tensioner and side airbag. The EFD was altered to add four IR-TRACCs to the thoracic region to allow both x-axis and y-axis displacement to be evaluated in a similar test. While the IR-TRACCs did record the displacement due to combined loading, the rate of displacement and timing of the peak displacements did not match external chestband outputs. The next step for the EFD is to revise the locations of IRTRACCs in the thorax and begin component testing in lateral and frontal directions to improve thoracic biofidelity.

Prediction of power efficiency of gear boxes has become an increasingly important research topic since fuel economy requirements for passenger vehicles are more stringent, due to not only fuel cost but also environmental regulations. Under this circumstance, the automotive industry is dedicatedly focusing on developing a highly efficient gear box. Thus, the analysis of power efficiency of gear box should be performed to have a transmission that is highly efficient as much as possible at the beginning of design stage. In this study, a program is developed to analyze the efficiency of an entire gearbox, considering all components’ losses such as gear mesh, wet clutches, bearings, oil pump and so on. The analytical models are based on the formulations of each component power loss model which has been developed and published in many existing papers. The program includes power flow analysis of both a parallel gear-train and a planetary gear-train.