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Added masses computation is a crucial aspect to be considered when the density of a body moving in a fluid is comparable to the density of the fluid displaced: added mass can be defined as the inertia added to a system because an accelerating or decelerating body displaces some volume of neighboring fluid as it moves through it. The motion of vehicles like airships and ships can be addressed only by keeping into account the effect of added masses, while in case of aircrafts and helicopters this contribution is usually neglected. Lighter Than Air flight simulation, unmanned airships flight control system, airships flight dynamics are typical applications in which added masses are fundamental to achieve an effective and realistic modeling. A panel based method using the mesh of an airship external shape is developed to account for the added massed.

An existing tire model was investigated for additional normal load-dependent characteristics to improve the large truck simulations developed by the National Highway Traffic Safety Administration (NHTSA) for the National Advanced Driving Simulator (NADS). Of the existing tire model coefficients, plysteer, lateral friction decay, aligning torque stiffness and normalized longitudinal stiffness were investigated. The findings of the investigation led to improvements in the tire model. The improved model was then applied to TruckSim to compare with the TruckSim table lookup tire model and test data. Additionally, speed-dependent properties for the NADS tire model were investigated (using data from a light truck tire).

The flight simulation of airships and hot air balloons usually considers the envelope geometry as a fixed shape, whose volume is eventually reduced by ballonets. However, the dynamic pressure or helium leaks in airships, and the release of air to allow descent in hot air balloons can significantly change the shape of the envelope leading to potential dangerous situations. In fact, in case of semi-rigid and non-rigid airships a reduction in envelope internal pressure can reduce the envelope bending stiffness leading to the loss of the typical axial-symmetric shape. For hot air balloons thing goes even worse since the lost of internal pressure can lead to the collapsing of the balloon shape to a sort of vertically stretched geometry (similar to a torch) which is not able to sustain the attached basket and its payload.

Recent studies in sheet metal forming, conducted at universities world wide, emphasize the development of computer aided techniques for process simulation. To be practical and acceptable in a production environment, these codes must be easy to use and allow relatively quick solutions. Often, it is not necessary to make exact predictions but rather to establish the influence of process variables upon part quality, tool stresses, material flow, and material thickness variation. In cooperation with its industrial partners, the ERC for Net Shape Manufacturing of the Ohio State University has applied a number of computer codes for analysis and design of sheet metal forming operations. This paper gives a few selected examples taken from automotive applications and illustrates practical uses of computer simulations to improve productivity and reduce tool development and manufacturing costs.

A practical Gasoline Compression Ignition (GCI) concept is presented that works on standard European 95 RON E10 gasoline over the whole speed/load range. A spark is employed to assist the gasoline autoignition at low loads; this avoids the requirement of a complex cam profile to control the local mixture temperature for reliable autoignition. The combustion phasing is controlled by the injection pattern and timing, and a sufficient degree of stratification is needed to control the maximum rate of pressure rise and prevent knock. With active control of the swirl level, the combustion system is found to be relatively robust against variability in charge motion, and subtle differences in fuel reactivity. Results show that the new concept can achieve very low fuel consumption over a significant portion of the speed/load map, equivalent to diesel efficiency. The efficiency is worse than an equivalent diesel engine only at low load where the combustion assistance operates.

Battery simulation by a DSP-controlled high current power supply is used to improve repeatability and comparability of starting tests, especially at low temperatures. The simulator's algorithm calculates the internal resistance of the battery by a timely constant resistor and a variable resistor representing the actual discharge history. The output voltage of the simulator is set as a function of internal resistor and load current with temperature and state of charge as setup parameter. The simulator was evaluated in cold start testing in comparison to real batteries. As a result, batteries are simulated with high repeatability. Deviations to real battery behavior are in the range of test to test deviations using real batteries.

This paper presents the details of the model for the physical steering system used on the National Advanced Driving Simulator. The system is basically a hardware-in-the-loop (steering feedback motor and controls) steering system coupled with the core vehicle dynamics of the simulator. The system's torque control uses cascaded position and velocity feedback and is controlled to provide steering feedback with variable stiffness and dynamic properties. The reference model, which calculates the desired value of the torque, is made of power steering torque, damping function torque, torque from tires, locking limit torque, and driver input torque. The model also provides a unique steering dead-band function that is important for on-center feel. A Simulink model of the hardware/software is presented and analysis of the simulator steering system is provided.

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.

Unmanned Aerial Vehicles (UAVs) provide the ability to perform a variety of experimental tests of systems and unproven research technologies, including new autopilot systems and obstacle avoidance capabilities, without risking the lives of human pilots. This paper describes the activities of design, optimization, and flight operations of a UAV conceived at Clarkson University (USA) and equipped to perform wind speed measurements to support wind farmsite planning. The UAV design has been assisted and validated by the use of an automatic virtual environment for the assisted design of civil UAVs. This tool can be used as a “computing machine” for civil UAVs. The operator inputs the mission profile and other generic parameters and data about performance, aerodynamics, and weight breakdown are extracted. A mathematical model of the UAV for flight simulation and its dynamic computations, along with automatic drawing is also produced.

Due to the recent anti-pollution policies, the performance increase in Spark Ignition (SI) engines is currently under the focus of automotive manufacturers. This trend drives control systems designers to investigate accurate solutions and build more sophisticated algorithms to increase the efficiency of this kind of engines. The development of a control strategy is composed of several phases and steps, and the first part of such process is typically spent in defining and investigating the logic of the strategy. During this phase it is often useful to have a light engine simulator, which allows to have robust synthetic combustion data with a low calibration and computational effort. In the first part of this paper, a description of the control-oriented ANalytical Engine SIMulator (ANESIM) is carried out.

Heavy trucks are involved in many accidents every year and Electronic Stability Control (ESC) is viewed as a means to help mitigate this problem. ESC systems are designed to reduce the incidence of single vehicle loss of control, which might lead to rollover or jackknife. As the working details and control strategies of commercially available ESC systems are proprietary, a generic model of an ESC system that mimics the basic logical functionality of commercial systems was developed. This paper deals with the study of the working of a commercial ESC system equipped on an actual tractor trailer vehicle. The particular ESC system found on the test vehicle contained both roll stability control (RSC) and yaw stability control (YSC) features. This work focused on the development of a reliable RSC software model, and the integration of it into a full vehicle simulation (TruckSim) of a heavy truck.

During the development of an Internal Combustion Engine-based powertrain, traditional procedures for control strategies calibration and validation produce huge amount of data, that can be used to develop innovative data-driven applications, such as emission virtual sensing. One of the main criticalities is related to the data quality, that cannot be easily assessed for such a big amount of data. This work focuses on an emission modeling activity, using an enhanced Light Gradient Boosting Regressor and a dedicated data pre-processing pipeline to improve data quality. First thing, a software tool is developed to access a database containing data coming from emissions tests. The tool performs a data cleaning procedure to exclude corrupted data or invalid parts of the test. Moreover, it automatically tunes model hyperparameters, it chooses the best set of features, and it validates the procedure by comparing the estimation and the experimental measurement.

This paper details the advancements and outcomes of the NEXTCAR (Next-Generation Energy Technologies for Connected and Automated on-Road Vehicles) program, an initiative led by the Advanced Research Projects Agency-Energy (ARPA-E). The program focusses on harnessing the full potential of Connected and Automated Vehicle (CAV) technologies to develop advanced vehicle dynamic and powertrain control technologies (VD&PT). These technologies have shown the capability to reduce energy consumption by 20% in conventional and hybrid electric cars and trucks at automation levels L1-L3 and by 30% L4 fully autonomous vehicles. Such reductions could lead to significant energy savings across the entire U.S. vehicle fleet.

This paper describes the development of a more accurate shock absorber model in order to obtain better vehicle simulation results. Previous shock models used a single spline to represent shock force versus shock velocity curves. These models produced errors in vehicle simulations because the damper characteristics are better represented by the application of a hysteresis loop in the model. Thus, a new damper model that includes a hysteresis loop is developed using Matlab Simulink. The damper characteristics for the new model were extracted from measurements made on a shock dynamometer. The new model better represents experimental shock data. The new shock model is incorporated into two different lumped-parameter vehicle models: one is a three degree-of-freedom vehicle handling model and the other is a seven degree-of-freedom vehicle ride model. The new damper model is compared with the previous model for different shock mileages (different degrees of wear).

Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This paper concentrates on low speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of campus as an initial autonomous vehicle (AV) pilot test route for the deployment of low speed autonomous shuttles. This paper presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment.

After the realization of very low exhaust gas emissions and corresponding OBD requirements to fulfill Euro VI and Tier 4 legislation, the focus in heavy-duty powertrain development is on the reduction of fuel consumption and thus CO₂ emissions again. Besides this, the total vehicle operation costs play another major role. A holistic view of the overall powertrain system including the combustion process, exhaust gas aftertreatment, energy recuperation and energy storage is necessary in order to obtain the best possible system for a given application. A management system coordinating the energy flow between the different subsystems while guaranteeing low exhaust emissions plays a major part in operating such complex architectures under optimal conditions.

The Ohio State University has fielded teams at all three of the DARPA Grand Challenge and DARPA Urban Challenge autonomous vehicle competitions, using three very different vehicle platforms. In this paper we present our experiences in these competitions, comparing and contrasting the different requirements, strategies, tasks, and vehicles developed for each challenge. We will discuss vehicle control and actuation, sensors, sensor interpretation, planning, behavior, and control generation. We will also discuss lessons learned from the engineering and implementation process for these three vehicles.

The paper discusses the development of a model for the 2006 BMW 330i for the National Advanced Driving Simulator's (NADS) vehicle dynamics simulation, NADSdyna. The front and rear suspensions are independent strut and link type suspensions modeled using recursive rigid-body dynamics formulations. The suspension springs and shock absorbers are modeled as force elements. The paper includes parameters for front and rear semi-empirical tire models used with NADSdyna. Longitudinal and lateral tire force plots are also included. The NADSdyna model provides state-of-the-art high-fidelity handling dynamics for real-time hardware-in-the-loop simulation. The realism of a particular model depends heavily on how the parameters are obtained from the actual physical system. Complex models do not guarantee high fidelity if the parameters used were not properly measured. Methodologies for determining the parameters are detailed in this paper.

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.

With the advent of increasing autonomous vehicles on public roads, the safety of vulnerable road users such as pedestrians, cyclists, etc. has never been more important. These especially include Blind or Visually Impaired (BVI) pedestrians who face difficulty in making confident decisions in road crossings without the help of accessible pedestrian signals (APS). This paper addresses some of the safety measures that can be taken to improve and assess the safety of BVI pedestrians in a controlled environment like a BVI school campus where autonomous vehicles are operated. The majority of research on autonomous vehicle safety does not consider the edge cases of encounters with BVI pedestrians. Based on this motivation, requirements and characteristics of Non-BVI and BVI pedestrians have been stated along with the motion models used to predict their future movements. Existing tools based on Bayesian multi-model filters were used for pedestrian tracking and motion predictions.