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The current approach for new Advanced Driver Assistance System (ADAS) and Connected and Automated Driving (CAD) function development involves a significant amount of public road testing which is inefficient due to the number miles that need to be driven for rare and extreme events to take place, thereby being very costly also, and unsafe as the rest of the road users become involuntary test subjects. A new development, evaluation and demonstration method for safe, efficient, and repeatable development, demonstration and evaluation of ADAS and CAD functions called Vehicle-in-Virtual –Environment (VVE) was recently introduced as a solution to this problem. The vehicle is operated in a large, empty, and flat area during VVE while its localization and perception sensor data is fed from the virtual environment with other traffic and rare and extreme events being generated as needed.

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.

This paper presents an energy-optimal plan for the allocation of a heterogeneous fleet of delivery vehicles in a dynamic network of multiple distribution centers and fulfillment centers. Each distribution center with a heterogeneous fleet of delivery vehicles is considered as a hub connected with the fulfillment centers through the routes as spokes. The goal is to minimize the overall energy consumption of the fleet while meeting the demand of each of the fulfillment centers. To achieve this goal, the problem is divided into two sub-problems that are solved in a hierarchical way. Firstly, for each spoke, the optimal number of vehicles to be allocated from each hub is determined. Secondly, given the number of allocated delivery vehicles from a hub for each spoke, the optimal selection of vehicle type from the available heterogeneous fleet at the hub is done for each of spokes based on the energy requirement and the energy efficiency of the spoke under consideration.

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.

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.

The importance of fluid-structure interaction (FSI) is of increasing concern in automotive design criteria as automobile hoods become lighter and thinner. This work focuses on computational simulation and analysis of automobile hoods under unsteady aerodynamic loads encountered at typical highway conditions while trailing another vehicle. These driving conditions can cause significant hood vibrations due to the unsteady loads caused by the vortex shedding from the leading vehicle. The study is carried out using coupled computational fluid dynamics (CFD) and computational structural dynamics (CSD) codes. The main goal of this work is to characterize the importance of fluid modeling fidelity to hood buffeting response by comparing fluid and structural responses using both Reynolds-Averaged Navier-Stokes (RANS) and detached eddy simulation (DES) approaches. Results are presented for a sedan trailing another sedan.

Advances in sensor solutions in the automotive sector make it possible to develop better ADAS and autonomous driving functions. One of the main tasks of highway chauffeur and highway pilot automated driving systems is to keep the vehicle between the lane lines while driving on a pre-defined route. This task can be achieved by using camera and/or GPS to localize the vehicle between the lane lines. However, both sensors have shortcomings in certain scenarios. While the camera does not work when there are no lane lines to be detected, an RTK GPS can localize the vehicle accurately. On the other hand, GPS requires at least 3 satellite connections to be able to localize the vehicle and more satellite connections and real-time over-the-air corrections for lane-level positioning accuracy. If GPS localization fails or is not accurate enough, lane line information from the camera can be used as a backup.

Springback affects the dimensional accuracy and final shape of stamped parts. Accurate prediction of springback is necessary to design dies that produce the desired part geometry and tolerances. Springback occurs after stamping and ejection of the part because the state of the stresses and strains in the deformed material has changed. To accurately predict springback through finite element analysis, the material model should be well defined for accurate simulation and prediction of stresses and strains after unloading. Despite the development of several advanced material models that comprehensively describe the Bauschinger effect, transient behavior, permanent softening of the blank material, and unloading elastic modulus degradation, the prediction of springback is still not satisfactory for production parts. Dies are often recut several times, after the first tryouts, to compensate for springback and achieve the required part geometry.

Autonomous vehicle technology has been developing rapidly in recent years. Vehicle parametric uncertainty in the vehicle model, variable time delays in the CAN bus based sensor and actuator command interfaces, changes in vehicle sped, sensitivity to external disturbances like side wind and changes in road friction coefficient are factors that affect autonomous driving systems like they have affected ADAS and active safety systems in the past. This paper presents a robust control architecture for automated driving systems for handling the abovementioned problems. A path tracking control system is chosen as the proof-of-concept demonstration application in this paper. A disturbance observer (DOB) is embedded within the steering to path error automated driving loop to handle uncertain parameters such as vehicle mass, vehicle velocities and road friction coefficient and to reject yaw moment disturbances.

This paper describes a systematic approach to identify the best sensor combination by performing sensor placement analysis to detect and isolate clutch stuck-off faults in Automatic Transmissions (AT) based on structural analysis. When an engaged clutch in the AT loses pressure during operation, it is classified as a clutch stuck-off fault. AT can enter in neutral state because of these faults; causing loss of power at wheels. Identifying the sensors to detect and isolate these faults is important in the early stage of the AT development. A universal approach to develop a structural model of an AT is presented based on the kinematic relationships of the planetary gear set elements. Sensor placement analysis is then performed to determine the sensor locations to detect and isolate the clutch stuck-off faults using speed sensors and clutch pressure sensors. The proposed approach is then applied to a 10-Speed AT to demonstrate its effectiveness.

An important part of automotive driving assistance systems and autonomous vehicles is speed optimization and traffic flow adaptation. Vehicle sensors and wireless communication with surrounding vehicles and road infrastructure allow for predictive control strategies taking near-future road and traffic information into consideration to improve fuel economy. For the development of autonomous vehicle speed control algorithms, it is imperative that the controller can be evaluated under different realistic driving and traffic conditions. Evaluation in real-life traffic situations is difficult and experimental methods are necessary where similar driving conditions can be reproduced to compare different control strategies. A traditional approach for evaluating vehicle performance, for example fuel consumption, is to use predefined driving cycles including a speed profile the vehicle should follow.

The elastomeric joints (bushings or mounts) in vehicle structural frames are usually described as uncoupled springs (only with diagonal terms) in large scale system models. The off-diagonal terms of an elastomeric joint have been previously ignored as they are often unknown since their properties cannot be measured in a uniaxial elastomer test system. This paper overcomes this deficiency via a scientific study of a laboratory frame that is designed to maintain a high fidelity with real-world vehicle body subframes in terms of natural modes under free boundaries. The steel beam construction of the laboratory frame, with four elastomeric mounts at the corners, permits the development of a highly accurate, yet simple, beam finite element model. This allows for a correlation study between the experiment and model that helps shed light upon the underlying physical phenomenon.

This paper describes the mechanical design of a Suspension Parameter Identification Device and Evaluation Rig (SPIDER) for wheeled military vehicles. This is a facility used to measure quasi-static suspension and steering system properties as well as tire vertical static stiffness. The machine operates by holding the vehicle body nominally fixed while hydraulic cylinders move an “axle frame” in bounce or roll under each axle being tested. The axle frame holds wheel pads (representing the ground plane) for each wheel. Specific design considerations are presented on the wheel pads and the measurement system used to measure wheel center motion. The constraints on the axle frames are in the form of a simple mechanism that allows roll and bounce motion while constraining all other motions. An overview of the design is presented along with typical results.

Most do not consider there to be a risk in pushing on, bumping into or falling against an elevator door from the hallway side. However, the lack of the elevator cars presence alone, and the potential for severe injury or even death make this seemingly mundane situation potentially critical. Standards exist relative to such situations, and past and current designs attempt to account for this possibility, still people get injured interacting with these doors every year. In order to evaluate a real-world elevator door system's ability to withstand the quasi-static and impactive loads that can be placed on it by the general public during its life, both intentionally and unintentionally, a predictive tool is needed. This work represents the combination of empirical laboratory testing and numerical modeling of a typical elevator door system exposed to quasi-static and dynamic loading.

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.

As we continue to create ever-lighter road vehicles, the challenge of balancing weight reduction and structural performance also continues. One of the key parts this occurs on is the hood, where lighter materials (e.g. aluminum) have been used. However, the aerodynamic loads, such as hood lift, are essentially unchanged and are driven by the front fascia and front grille size and styling shape. This paper outlines a combination CFD/FEA prediction method for hood deflection performance at high speeds, by using the surface pressures as boundary conditions for a FEA linear static deflection analysis. Additionally, custom post-processing methods were developed to enhance flow analysis and understanding. This enabled the modification of existing test methods to further improve accuracy to real world conditions. The application of these analytical methods and their correlation with experimental results are discussed in this paper.

The interface between a plenum and primary runner in log-style intake manifolds is one of the dominant sources of flow losses in the breathing system of Internal Combustion Engines (ICE). A right-angled T-junction is one such interface between the plenum (main duct) and the primary runner (sidebranch) normal to the plenum's axis. The present study investigates losses associated with the combining flow through these junctions, where fluid from both sides of the plenum enters the primary runner. Steady, incompressible-flow experiments for junctions with circular cross-sections were conducted to determine the effect of (1) runner interface radius of 0, 10, and 20% of the plenum diameter, (2) plenum-to-runner area ratio of 1, 2.124, and 3.117, and (3) runner taper area ratio of 2.124 and 3.117. Mass flow rate in each branch was varied to obtain a distribution of flow ratios, while keeping the total flow rate constant.

This paper presents an empirical dynamic model of a single spring electromagnetic solenoid actuator system, including bounce, temperature effects and coil leakage inductance. The model neglects hysteresis and saturation, the aim being to compensate for these uncertainties through estimator robustness. The model is validated for all regions of operation and there is a good agreement between model and experimental data. A nonlinear (sliding mode) estimator is developed to estimate position and speed from current measurements. Since the estimator makes use of only current measurement it is given the name sensorless. The estimator is validated in simulation and experimentally. The novelty in this paper lies in the fact that accurate state estimation can be realized on a simple linear model using a robust observer theory. Also, the formulations for leakage inductance and coil temperature are unique.

This paper presents the modeling of an Electromagnetic Valve Actuator (EMV). A nonlinear model is formulated and presented that takes into account secondary nonlinearities like hysteresis, saturation, bounce and mutual inductance. The uniqueness of the model is contained in the method used in modeling hysteresis, saturation and mutual inductance. Theoretical and experimental methods for identifying parameters of the model are presented. The nonlinear model is experimentally validated. Simulation and experimental results are presented for an EMV designed and built in our laboratory. The experimental results show that sensorless estimation could be a possible solution for position control.

Long-duration space missions require high-quality, nutritious foods, which will need reheating to serving temperature, or sterilization on an evolved planetary base. The package is generally considered to pose a disposal problem after use. We are in the process of development of a dual-use package wherein the food may be rapidly reheated in situ using the technology of ohmic heating. We plan to make the container reusable, so that after food consumption, the package is reused to contain and sterilize waste. This approach will reduce Equivalent System Mass (ESM) by using a compact heating technology, and reducing mass requirements for waste storage. Preliminary tests of the package within a specially-designed ohmic heating enclosure show that ISS menu item could easily be heated using ohmic heating technology. Mathematical models for heat transfer were used to optimize the layout of electrodes to ensure uniform heating of the material within the package.