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The adoption of the electronic controlled steering systems with new technologies has been extended in recent years. They have interactions with other complex vehicle subsystems and it is a hard task for the vehicle developer to find the best solution from huge number of the combination of parameter settings with track tests. In order to improve the efficiency of the steering system development, the authors had developed a steering bench test method for steering system using a Hardware-In-the-Loop Simulation (HILS). In the steering HILS system, vehicle dynamics simulation and the tie rod axial force calculation are required at the same time in the real-time simulation environment. The accuracy of the tie rod axial force calculation is one of the key factors to reproduce the vehicle driving condition. But the calculation cannot be realized by a commercial software for the vehicle dynamics simulation.

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.

In order to build a useful and comfortable in-car human machine interface systems, the information presentation method should be easy to understand (low mental workload) and one should be able to respond with ease to the information presented (low response workload). We are making efforts to establish an evaluation method that would differentiate between mental workload and response workload. Here, we present the results of our trial using brain waves measurements (Eye Fixation Related Potentials). We focus on the relation between P3 latencies and drivers response workload compared to mental workload in a task involving eye movements. Previous experiments showed that P3 latency correlates strongly with the amount of information presented. The current experiment shows that P3 latencies seem to be independent to the type of response the subject is requested to perform.

A robotic driver has been designed on the basis of an analysis of a human driver's action in following a given driving schedule. The self-learning algorithm enables the robot to learn the vehicle characteristics without human intervention. Based on learned relationships, the robotic driver can determine an appropriate accelerator position and execute other operations through sophisticated calculations using the future scheduled vehicle speed and vehicle characteristics data. Compensation is also provided to minimize vehicle speed error. The robotic driver can reproduce the same types of exhaust emission and fuel economy data obtained with human drivers with good repeatability. It doesn't require long preparation time. Thereby making it possible to reduce experimentation work in the vehicle development process while providing good accuracy and reliability.

A set of devices for measurement of human balance orientation and eye movements in weightlessness was developed for neurovestibular experiments on Spacelab. The experiments involve astronaut motion, limb position changes, and moving visual fields, measurements are made of eye movements, muscular activity and orientation perception. This joint US/Canadian research program represent a group of closely related experiments designed to investigate space motion sickness, any associated changes in otolith-mediated responses occurring during weightlessness, and the continuation of changes to postflight conditions. The otoliths are a component of the vestibular apparatus which is located in the middle ear. It is responsible for maintaining the body's balance. Gravitational pull on the otoliths causes them to constantly appraise the nervous system of the position of the head with respect to the direction of gravity.

This paper describes a headway distance control system for platoon driving on an automated highway system (AHS). The system implemented on a test vehicle is described first, followed by a description of a vehicle control method based on the use of throttle and brake actuators. This method makes it possible to obtain the target acceleration and deceleration regardless of the vehicle speed range and the rate of acceleration or deceleration. Experimental and simulation results obtained with this method are presented. A control method is then described that uses inter-vehicle communication and laser radar to maintain a constant headway between vehicles. The results of simulations and driving tests conducted with three vehicles are presented to illustrate that the use of inter-vehicle communication is highly effective in improving headway control performance.

This paper assesses the potential improvement of automotive powertrain technologies 25 years into the future. The powertrain types assessed include naturally-aspirated gasoline engines, turbocharged gasoline engines, diesel engines, gasoline-electric hybrids, and various advanced transmissions. Advancements in aerodynamics, vehicle weight reduction and tire rolling friction are also taken into account. The objective of the comparison is the potential of anticipated improvements in these powertrain technologies for reducing petroleum consumption and greenhouse gas emissions at the same level of performance as current vehicles in the U.S.A. The fuel consumption and performance of future vehicles was estimated using a combination of scaling laws and detailed vehicle simulations. The results indicate that there is significant potential for reduction of fuel consumption for all the powertrains examined.

Most of the side impact air bag systems in the current market are designed to protect the thorax area only. The new Head and Thorax SRS Side Impact Air Bag system, which Nissan recently introduced into the market, was designed to help provide additional protection for the head in certain side impacts. The system may help protect occupant head contacts when the vehicle collides into a tree, or the high hood of a large striking vehicle. This paper introduces the additional features and function of the new Head and Thorax SRS Side Impact Air Bag system, and some evaluation results in laboratory testing.

The durability test with road-load input is necessary for evaluating durability of body and chassis structure in automotive applications. This paper shows the method to analyze road-load input to a suspension system for development of a simple component level bench test. This method enables the extraction of the essential inputs to evaluate the durability of suspension parts using the transfer function (frequency response function) measured by Multi-axial Road Simulator and wheel force transducers. These extracted inputs contribute to development of a new realistic component bench test.

Increasing expectations for stability at high speed call for the improvement of cars' aerodynamic performance, in particular lift reduction. However, due to styling constraints, traditional spoilers must be avoided and replaced by other solutions like underfloor components. Flow simulation is expected to be a useful tool for lift prediction, but the conventional models used so far did not represent complex geometry details such as the engine compartment and underfloor, and accuracy was insufficient. In the present study, a full vehicle simulation model, including the engine compartment and underfloor details, was used. Other improvements were also made such as optimization of the computational grid and the setting of boundary conditions for reproducing wind tunnel experiments or actual driving, making it possible to predict lift variations due to vehicle geometry changes.

The aim of this study was to explore if fingernail delamination injury following EMU glove use may be caused by compression-induced blood flow occlusion in the finger. During compression tests, finger blood flow decreased more than 60%, however this occurred more rapidly for finger pad compression (4 N) than for fingertips (10 N). A pressure bulb compression test resulted in 50% and 45% decreased blood flow at 100 mmHg and 200 mmHg, respectively. These results indicate that the finger pad pressure required to articulate stiff gloves is more likely to contribute to injury than the fingertip pressure associated with tight fitting gloves.

This paper describes the method used to design the basic control algorithm of a lane-keeping support system that is intended to assist the driver's steering action. Lane-keeping control has been designed with steering torque as the control input without providing a minor loop for the steering angle. This approach was taken in order to achieve an optimum balance of lane-keeping control, ease of steering intervention by the driver and robustness. The servo control system was designed on the basis of H2 control theory. Robustness against disturbances, vehicle nonlinearity and parameter variation was confirmed by μ - analysis. The results of computer simulations and driving tests have confirmed that the control system designed with this method provides the intended performance.

On-board voice-interaction systems such as a voice-activated system or a text -to-speech (TTS) system enable drivers to operate devices or to obtain desired information without relying on visual processes. These systems are aimed to reduce the driver's workload, but there is a concern about their possible effect on mental distraction. This paper describes driving simulator tests that were conducted to examine the potential influence of such systems on driver's mental distraction. The results obtained for all of the indices show that the mental distraction level when operating a voice-activated system is significantly lower than that of a traditional manually operated system.

Lock-up operation of an automatic transmission is known as one good method of improving fuel economy. However, locking up the transmission at low vehicle speeds can often cause undesirable vibration or booming noise. Slip control of the lock-up clutch can resolve these problems, but the speed difference of the lock-up clutch needs to be controlled at a certain value. This control system has to overcome large changes in the parameters of the lock-up system at low vehicle speeds and also changes with regard to the speed ratio in a continuously variable transmission (CVT). In this study, this complex non-linear system has been modeled as a first-order linear parameter varying (LPV) system. A robust control algorithm was applied taking various disturbances into account to design a new slip lock-up control system.

NASA has long recognized the advantages of providing improved information interfaces to EVA astronauts and has pursued this goal through a number of development programs over the past decade. None of these activities or parallel efforts in industry and academia has so far resulted in the development of an operational system to replace or augment the current extravehicular mobility unit (EMU) Display and Controls Module (DCM) display and cuff checklist. Recent advances in display, communications, and information processing technologies offer exciting new opportunities for EVA information interfaces that can better serve the needs of a variety of NASA missions. Hamilton Sundstrand Space Systems International (HSSSI) has been collaborating with Simon Fraser University and others on the NASA Haughton Mars Project and with researchers at the Massachusetts Institute of Technology (MIT), Boeing, and Symbol Technologies in investigating these possibilities.

Computer simulation of extravehicular activity (EVA) is increasingly being used in planning and training for EVA. A space suit model is an important, but often overlooked, component of an EVA simulation. Because of the inherent difficulties in collecting angle and torque data for space suit joints in realistic conditions, little data exists on the torques that a space suit’s wearer must provide in order to move in the space suit. A joint angle and torque database was compiled on the Extravehicular Maneuvering Unit (EMU), with a novel measurement technique that used both human test subjects and an instrumented robot. Using data collected in the experiment, a hysteresis modeling technique was used to predict EMU joint torques from joint angular positions. The hysteresis model was then applied to EVA operations by mapping out the reach and work envelopes for the EMU.

Evaluations of dummy injury readings obtained in regulatory crash tests and new car assessment program tests provide indices for the development of crash safety performance in the process of developing new vehicles. Based on these indices, vehicle body structures and occupant restraint systems are designed to meet the required occupant injury criteria. There are many types of regulatory tests and new car assessment program tests that are conducted to evaluate vehicle safety performance in side impacts. Factoring all of the multiple test configurations into the development of new vehicles requires advanced design capabilities based on a good understanding of the mechanisms producing dummy injury readings. In recent years, advances in computer-aided engineering (CAE) tools and computer processing power have made it possible to run simulations of occupant restraint systems such as side airbags and seatbelts.

Driving car means to control a vehicle according to a target path, e.g. road and speed, with some constraints. Human driving models have been proposed and applied for simulations. However, human control in driving has not been analyzed sufficiently comparing with that of machine control system in term of control theory. Input - output property with internal information processing is not easily measured and described. Response of human driving is not as quicker as that of machine controller but human can learn vehicle response to driving operation and predict target changes. Driving behavior of an expert driver and a beginner in an emission test cycle was measured and difference in target speed tracking was looked into with performance indices. The beginner's operation was less stable than that of the expert. Transfer function of the vehicle system was derived based on linearized model to investigate human driving behavior as a tracking controller in the system.

This paper presents the results of a study of how people interacted with a production voice-command based interface while driving on public roadways. Tasks included phone contact calling, full address destination entry, and point-of-interest (POI) selection. Baseline driving and driving while engaging in multiple-levels of an auditory-vocal cognitive reference task and manual radio tuning were used as comparison points. Measures included self-reported workload, task performance, physiological arousal, glance behavior, and vehicle control for an analysis sample of 48 participants (gender balanced across ages 21-68). Task analysis and glance measures confirm earlier findings that voice-command interfaces do not always allow the driver to keep their hands on the wheel and eyes on the road, as some assume.

The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.