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Forward Collision Warning (FCW) and Lane Departure Warning (LDW) systems are two active safety systems that have recently been added to the U.S. New Car Assessment Program (NCAP) evaluation. Vehicles that pass confirmation tests may advertise the presence of FCW and LDW alongside the vehicle's star safety rating derived from crash tests. This paper predicts the number of crashes and injured drivers that could be prevented if all vehicles in the U.S. fleet were equipped with production FCW and/or LDW systems. Models of each system were developed using the test track data collected for 16 FCW and 10 LDW systems by the NCAP confirmation tests. These models were used in existing fleetwide benefits models developed for FCW and LDW. The 16 FCW systems evaluated could have potentially prevented between 9% and 53% of all rear-end collisions and prevented between 19% and 60% of injured (MAIS2+) drivers. Earlier warning times prevented more warnings and injuries.

Safety and minimal transit time are vital during transportation of essential commodities and passengers, especially in winter conditions. Icy roads are the worst driving conditions with the least available friction, leaving valuable cargo and precious human lives at stake. The study investigates the available friction at the tire-ice interface due to changes in key operational parameters. Experimental analysis of tractive performance of tires on ice was carried out indoor, using the terramechanics rig located at the Advanced Vehicle Dynamics Laboratory (AVDL) at Virginia Tech. The friction-slip ratio curves obtained from indoor testing were inputted into TruckSIM, defining tire behavior for various ice scenarios and then simulating performance of trucks on ice. The shortcomings of simulations in considering the effects of all the operational parameters result in differences between findings of indoor testing and truck performance simulations.

This study undertakes an investigation of the effect of battery charge balance in hybrid electric vehicles (HEVs) on EPA fuel economy label values. EPA's updated method was fully implemented in 2011 and uses equations which weight the contributions of fuel consumption results from multiple dynamometer tests to synthesize city and highway estimates that reflect average U.S. driving patterns. For the US06 and UDDS cycles, the test results used in the computation come from individual phases within the overall certification driving cycles. This methodology causes additional complexities for hybrid vehicles, because although they are required to be charge-balanced over the course of a full drive cycle, they may have net charge or discharge within the individual phases. As a result, the fuel consumption value used in the label value calculation can be skewed.

The availability of active safety systems, such as Lane Departure Warning (LDW), has recently been added as a rating factor in the U.S. New Car Assessment Program (NCAP). The objective of this study is to determine the relevance of the NCAP LDW confirmation test to real-world road departure crashes. This study is based on data collected as part of supplemental crash reconstructions performed on 890 road departure collisions from the National Automotive Sampling System, Crashworthiness Data System (NASS/CDS). Scene diagrams and photographs were examined to determine the lane departure and lane marking characteristics not available in the original data. The results suggest that the LDW confirmation test captures many of the conditions observed in real-world road departures. For example, 40% of all single vehicle collisions in the dataset involved a drift-out-of-lane type of departures represented by the test.

A unique concept for a multi-body test rig enabling the simulation of longitudinal, steering and vertical dynamics was developed at the Institute for Mechatronic Systems (IMS) at TU Darmstadt. A prototype of this IMS test rig is currently being built. In conjunction with the IMS test rig, the Vehicle Terrain Performance Laboratory (VTPL) at Virginia Tech further developed a full car, seven degree of freedom (7 DOF) simulation model capable of accurately reproducing measured displacement, pitch, and roll of the vehicle body due to terrain excitation. The results of the 7 DOF car model were used as the reference input to the multi-body IMS test rig model. The goal of the IMS/VTPL joint effort was to determine whether or not a controller for the IMS test rig vertical actuator could accurately reproduce wheel displacements due to different measured terrain constraints.

This study evaluated the accuracy of 75 Event Data Recorders (EDRs) extracted from model year 2010-2012 Chrysler, Ford, General Motors, Honda, Mazda, and Toyota vehicles subjected to side-impact moving deformable barrier crash tests. The test report and vehicle-mounted accelerometers provided reference values to assess the EDR reported change in lateral velocity (delta-v), seatbelt buckle status, and airbag deployment status. Our results show that EDRs underreported the reference lateral delta-v in the vast majority of cases, mimicking the errors and conclusions found in some longitudinal EDR accuracy studies. For maximum lateral delta-v, the average arithmetic error was −3.59 kph (−13.8%) and the average absolute error was 4.05 kph (15.9%). All EDR reports that recorded a seatbelt buckle status data element correctly recorded the buckle status at both the driver and right front passenger locations.

With advances in virtual prototyping, accurate digital modeling of driving posture is regarded as a fundamental step in the design of ergonomic driver-seat-cabin systems. Extensive work on driving postures has been carried out focusing on the measurement and prediction of driving postures and the determination of comfortable joint angle ranges. However, studies on postural sensitivity are scarce. The current study investigated whether a driver-selected posture actually represents the most preferred one, by comparing the former with ratings of postures selected at 20 predefined places around the original hip joint center (HJC). An experiment was undertaken in a lab setting, using two distinctive driving package geometries: one for a sedan and the other for an SUV. The 20 postural ratings were compared with that of the initial user-selected position.

3D digital human modeling (DHM) tools for vehicle packaging facilitate ergonomic design and evaluation based on anthropometry, comfort, and force analysis. It is now possible to quickly predict postures and positions for drivers with selected anthropometry based on ergonomics principles. Despite their powerful visual representation technology for human movements and postures, these tools are still questioned with regard to the validity of the output they provide, especially when predictions are made for different populations. Driving postures and positions of two populations (i.e. North Americans and Koreans) were measured in actual and mock-up SUVs to investigate postural differences and evaluate the results provided by a DHM tool. No difference in driving postures was found between different stature groups within the same population. Between the two populations, however, preferred angles differed for three joints (i.e., ankle, thigh, and hip).

This work pertains to laboratory testing of truck cab suspensions for the purpose of improving in-cab ride quality. It describes the testing procedure of a complete truck cab suspension while still being mounted on the vehicle. It allows for testing with minimal amount of resources, limited to two mobile actuators and minimal modifications to the stock vehicle. The actuators can be attached to any axle through a set of modified brake drums and excite the drive axle in a vertical plane. The excitation signal sent to the actuators can be in phase for a heave type motion or out of phase for a roll motion. The chassis shock absorbers are replaced with rigid links to prevent the actuator input from becoming filtered by the primary suspension. This allows the input to reach the cab suspension more directly and the cab to be excited across a broader range of frequencies.

Rear-end crashes involving heavy trucks occur with sufficient frequency that they are a cause of concern within regulatory agencies. In 2006, there were approximately 23,500 rear-end crashes involving heavy trucks which resulted in 135 fatalities. As part of the Federal Motor Carrier Safety Administration's (FMCSA) goal of reducing the overall number of truck crashes, the Enhanced Rear Signaling (ERS) for Heavy Trucks project was developed to investigate methods to reduce or mitigate those crashes where a heavy truck has been struck from behind by another vehicle. Researchers also utilized what had been learned in the rear-end crash avoidance work with light vehicles that was conducted by the National Highway Traffic Safety Administration (NHTSA) with Virginia Tech Transportation Institute (VTTI) serving as the prime research organization. ERS crash countermeasures investigated included passive conspicuity markings, visual signals, and auditory signals.

Skyhook control has been used extensively for semiactive dampers for a variety of applications, most widely for passenger vehicle suspensions. This paper provides an experimental evaluation of how well skyhook control works for improving roll stability of a passenger vehicle. After discussing the formulation for various semiactive control methods that have been suggested in the past for vehicle suspensions, the paper includes the implementation of a semiactive system with magneto-rheological (MR) dampers on a sport utility vehicle. The vehicle is used for a series of road tests that includes lane change maneuvers, with different types of suspensions. The suspensions that are tested include the stock suspension, the uncontrolled MR dampers, skyhook control, and a new semiactive control method called “SIA skyhook.” The SIA Skyhook augments the conventional skyhook control with steering input, in order to account for the suspension requirements during a lateral maneuver.

Computer simulations, dummy experiments with a new enhanced upper extremity, and small female cadaver experiments were used to analyze the small female upper extremity response under side air bag loading. After establishing the initial position, three tests were performed with the 5th percentile female hybrid III dummy, and six experiments with small female cadaver subjects. A new 5th percentile female enhanced upper extremity was developed for the dummy experiments that included a two-axis wrist load cell in addition to the existing six-axis load cells in both the forearm and humerus. Forearm pronation was also included in the new dummy upper extremity to increase the biofidelity of the interaction with the handgrip. Instrumentation for both the cadaver and dummy tests included accelerometers and magnetohydrodynamic angular rate sensors on the forearm, humerus, upper and lower spine.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2009 - 2011 EcoCAR: The NeXt Challenge Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM), and the U.S. Department of Energy (DOE). Following GM's Vehicle Development Process (VDP), HEVT established team goals that meet or exceed the competition requirements for EcoCAR in the design of a plug-in extended range hybrid electric vehicle. The competition requires participating teams to re-engineer a stock crossover utility vehicle donated by GM. The result of this design process is an Extended Range Electric Vehicle (EREV) that uses grid electric energy and E85 fuel for propulsion. The vehicle design has achieved an SAE J1711 utility factor corrected fuel consumption of 2.9 L(ge)/100 km (82 mpgge) with an all-electric range of 87 km (54 miles) [1].

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is participating in the 2011-2014 EcoCAR 2 competition in which the team is tasked with re-engineering the powertrain of a GM donated vehicle. The primary goals of the competition are to reduce well to wheels (WTW) petroleum energy use (PEU) and reduce WTW greenhouse gas (GHG) and criteria emissions while maintaining performance, safety, and consumer acceptability. To meet these goals HEVT has designed a series parallel plug-in hybrid electric vehicle (PHEV) with multiple modes of operation. This paper will first cover development of the control system architecture with a dual CAN bus structure to meet the requirements of the vehicle architecture. Next an online optimization control strategy to minimize fuel consumption will be developed. A simple vehicle plant model will then be used for software-in-the-loop (SIL) testing to improve fuel economy.

Wind tunnel tests were conducted on two variations of a Grumman design for a Maglev vehicle The tests employed a moving belt system simulation of an elevated track which was designed to properly simulate the flowfield for Maglev vehicle configurations of the EMS type where the vehicle undercarriage partially wraps around or encloses the track Lift and drag forces and vehicle pitching moments were measured over a wide range of Reynolds number and the results compared with computations made by Grumman The tests also included measurements of flowfield velocity and turbulence profiles downstream of the vehicles and pressure measurements over the vehicle nose The test results showed a slight difference between the two designs with a possible reason to give preference to one of the designs due to reduced pitching moment.

In an age of growing complexity with regards to vehicle control systems, verification and validation of control algorithms is a rigorous and time consuming process. With the help of rapid control prototyping techniques, designers and developers have cost effective ways of validating controls under a quicker time frame. These techniques involve developments of plant models that replicate the systems that a control algorithm will interface with. These developments help to reduce costs associated with construction of prototypes. In standard design cycles, iterations were needed on prototypes in order to finalize systems. These iterations could result in code changes, new interfacing, and reconstruction, among other issues. The time and resources required to complete these were far beyond desired. With the help of simulated interfaces, many of these issues can be recognized prior to physical integration.

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.

Indoor laboratory tire testing on flat belt machines and tire testing on the actual road yield different results. Testing on the machine offers the advantage of repeatability of test conditions, control of the environmental condition, and performance evaluation at extreme conditions. However, certain aspects of the road cannot be reproduced in the laboratory. It is thus essential to understand the connection between the machine and the road, as tires spend all their life on the road. This research, investigates the reasons for differences in tire performance on the test machine and the road. The first part of the paper presents a review on the differences between tire testing in the lab and on the road, and existing methods to account for differences in test surfaces.

This study evaluates the accuracy of 41 Event Data Recorders (EDR) extracted from model year 2012 General Motors, Chrysler, Ford, Honda, Mazda, Toyota, and Volvo vehicles subjected to New Car Assessment Program 56 kph full-frontal barrier crash tests. The approach was to evaluate (1) the vehicle longitudinal change in velocity or delta-V (ΔV) as measured by EDRs in comparison with the high-precision accelerometers mounted onboard test vehicles and (2) the accuracy of pre-crash speed, seatbelt buckle status, and frontal airbag deployment status. On average the absolute error for pre-crash speed between the EDR and reference instrumentation was only 0.58 kph, or 1.0% of the nominal impact speed. In all cases in which the EDRs recorded the seatbelt buckle status of the driver or right front passenger, the modules correctly reported that the occupants were buckled. EDRs reported airbag deployment correctly in all of the tests.

The objective of this study was to investigate potential for traumatic brain injuries (TBI) using a newly developed, geometrically detailed, finite element head model (FEHM) within the concept of a simulated injury monitor (SIMon). The new FEHM is comprised of several parts: cerebrum, cerebellum, falx, tentorium, combined pia-arachnoid complex (PAC) with cerebro-spinal fluid (CSF), ventricles, brainstem, and parasagittal blood vessels. The model's topology was derived from human computer tomography (CT) scans and then uniformly scaled such that the mass of the brain represents the mass of a 50th percentile male's brain (1.5 kg) with the total head mass of 4.5 kg. The topology of the model was then compared to the preliminary data on the average topology derived from Procrustes shape analysis of 59 individuals. Material properties of the various parts were assigned based on the latest experimental data.