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This paper deals with a blind spot assistance system that assists the driver by generating yaw-moment when a driver's lane change maneuver is detected and when there is an object present in the blind spot area of the adjacent lane. The system combines lane information and driver's maneuver information for estimating the driver's lane change. If the millimeter wave sensor detects an object in the rear blind spot area in the event of a lane change, direct yaw-moment in the opposite direction of the lane change maneuver is generated. The unique method of detecting driver's lane change, control method of assisting the driver to recover back in lane, and the system design to maintain driver compatibility is mentioned, together with the effectiveness of the system.

Comparisons have been made between dual-fuel (80% port-injection gasoline and 20% direct-injection diesel by mass) Highly Premixed Charge Combustion (HPCC) and blended-fuel (80% gasoline and 20% diesel) Low Temperature Combustion (LTC) modes on a 1-L single-cylinder test engine. In the HPCC mode, both early-injection (E-HPCC) and late-injection (L-HPCC) of diesel have been used. The comparisons have been conducted with a fixed fuel injection rate of 50 mg/cycle at 1500 rpm, and with the combustion phasing fixed (by adjusting the injection timing) so that the 50% heat release point (CA50) is at 8° ATDC. The rapid heat release process of LTC leads to the highest maximum pressure rise rate (MPRR). A two-peak heat release process is observed in L-HPCC, resulting in a lower MPRR. The heat release rate and MPRR values for the E-HPCC are comparable to the L-HPCC values. The EHPCC mode provides the lowest NOX emission. The soot emissions for all three modes are low.

A method for detecting drivers' intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.

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.

A new transparent type electrochromic device (ECD) has been developed. It consists of two electrochromic thin films facing each other, one of “prussian blue” (PB) and the other of tungsten trioxide (WO3). PB exhibits a high intensity of coloration and is blue in the oxidized state and transparent in the reduced state. By electrodeposition, the PB layer can be formed on large substrates at low cost. This ECD has been applied to large, curved automotive glass. It reversibly turns from dark blue to transparent at low operating voltage and maintains the same intensity of coloration without the aid of an external power supply. Compared with photochromic glass, it achieves a greater color change in a shorter time and its light absorption can be changed at will. It shields passengers from the discomfort of glare and heat flow through glass.

Gasoline engines employ various mechanisms for improvement of fuel consumption and reduction of exhaust emissions to deal with environmental problems. Direct fuel injection is one such technology. This paper presents a new quasi-dimensional combustion model applicable to direct injection gasoline engine. The Model consists of author's original in-cylinder turbulence and mixture homogeneity sub model suitable for direct fuel injection conditions. Model validation results exhibit good agreement with experimental and 3D CFD data at steady state and transient operating conditions.

A model has been developed to predict the performance of the Texaco Controlled Combustion, Stratified Charge Engine starting from engine geometry, fuel characteristics and the operating conditions. This performance model divides the engine cycle into the following phases: Intake, Compression, Rapid Combustion, Mixing-Dominated Expansion, Heat-Transfer Dominated Expansion and Exhaust. During the rapid combustion phase, the rate of heat release is assumed to be controlled by the rate of fuel injection and the air-to-fuel ratio. The burning rate in the mixing controlled stage appears to be dominated by the rate of entrainment of the surrounding gas by the plume of burning products and this rate is assumed to be controlled by the turbulent eddy entrainment velocity. A plume geometry model has been developed to obtain the surface area of the plume for entrainment during the mixing dominated phase.

The method for measuring air-fuel ratio is generally based on analysis of the exhaust gas components and its calculations. A new instrument has been developed which uses this method, but it attaches an oxygen sensor for exhaust gas analysis to the exhaust pipe and calculates the air-fuel ratio directly from the sensor output using a microprocessor. The response time of this instrument is 100 milliseconds and because it does not require an exhaust gas sampling system its weight is only 2.5 kg. This paper describes the operation theory, construction and characteristics of this instrument, as well as the results of air-fuel ratio of measurements on engines and vehicles using this instrument in a transient state.

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.

Two hypotheses based on the sensory conflict theory were postulated as possible means for reducing carsickness: (1) Reducing signals from the vestibular and vision systems through a reduction of low-frequency motion would mitigate carsickness and (2) Controlling stimulation of visual organs so as to reduce the amount of sensory conflict would mitigate carsickness. For hypothesis (1), the relations between subjective carsickness ratings and motions of the vehicle and passengers' body were investigated. Greater correlation was found between carsickness ratings and motions of the passengers' head, where the organs of the vestibular and vision systems are located, than between carsickness ratings and vehicle motions. For hypothesis (2), the incidence of carsickness in passengers who gazed at an in-vehicle display was investigated because there seemed to be large conflict between the vestibular system and the vision system.

Driving tests were conducted using an experimental vehicle equipped with an adaptive cruise control system incorporating low-speed following capability in order to evaluate drivers' trust in a driver support system. The results revealed that the drivers' trust in the system declined in cases where the control algorithm produced vehicle behavior that was inconsistent with their expectations. However, that decline in trust ceased to be observed as the drivers' understanding of the system improved. This result suggests a correlation between their understanding of the system and trust in it.

There have been strong demands recently for reductions in the fuel consumption and exhaust emissions of diesel engines from the standpoints of conserving energy and curbing global warming. A great deal of research is being done on new emission control technologies using direct-injection (DI) diesel engines that provide high thermal efficiency. This work includes dramatic improvements in the combustion process. The authors have developed a new combustion concept called Modulated Kinetics (MK), which reduces smoke and NOx levels simultaneously by reconciling low-temperature combustion with pre-mixed combustion [1, 2]. At present, research is under way on the second generation of MK combustion with the aim of improving emission performance further and achieving higher thermal efficiency [3]. Reducing heat rejection in the combustion chamber is effective in improving the thermal efficiency of DI diesel engines as well as that of MK combustion.

Vehicle manufacturers developed two mixture formation concepts for the first generation of gasoline direct-injection (GDI) engines. Both the wall-guided concept with reverse tumble air motion or swirl air motion and the air-guided concept with tumble air motion have the fuel injector located at the side of the combustion chamber between the two intake ports. This paper proposes a new GDI concept. It has the fuel injector located at almost the center of the combustion chamber and with the spark plug positioned nearby. An oval bowl is provided in the piston crown. The fuel spray is injected at high fuel pressures of up to 100 MPa. The spray creates strong air motion in the combustion chamber and reaches the piston bowl. The wall of the piston bowl changes the direction of the spray and air motion, producing an upward flow. The spray and air flow rise and reach the spark plug.

The probability or risk of traffic accidents must be estimated quantitatively in order to implement effective traffic safety measures. In this study, various statistical data and probability theory were used to examine a method for predicting the risk of crossing-collisions, representing a typical type of accident at intersections in Japan. Crossing-collisions are caused by a variety of factors, including the road geometry and traffic environment at intersections and the awareness and intentions of the drivers of the striking and struck vehicles. Bayes' theorem was applied to find the accident probability of each factor separately. Specifically, the probability of various factors being present at the time of a crossing-collision was estimated on the basis of traffic accident data and observation survey data.

Technologies for improving the fuel economy of gasoline engines have been vigorously developed in recent years for the purpose of reducing CO2 emissions. Increasing the compression ratio for improving thermal efficiency and downsizing the engine based on fuel-efficient operating conditions are good examples of technologies for enhancing gasoline engine fuel economy. A direct-injection system is adopted for most of these engines. Direct injection can prevent knocking by lowering the in-cylinder temperature through fuel evaporation in the cylinder. Therefore, direct injection is highly compatible with downsized engines that frequently operate under severe supercharging conditions for improving fuel economy as well as with high compression ratio engines for which susceptibility to knocking is a disadvantage.

A safety support system that uses information received from the road infrastructure is being developed in a project sponsored by the Ministry of Land, Infrastructure and Transport. The purpose of this system is to reduce the number of accidents at intersections and on highways. The system is now being tested in an experimental vehicle. This paper describes what kind of information is helpful to drivers based on the experimental results.

People often feel discomfort when entering or exiting a vehicle because of a static electric shock. In the electronics industry, ionizers have been developed to prevent electrostatic discharges and contamination sticking around or on circuit components. Ionizers incorporate corona discharge principles to neutralize the static electric field. Using this idea, we developed an in-vehicle system to neutralize the human body charge. To accomplish this, the mechanism by which the human body attains a charge when exiting a vehicle was first defined. That definition was then used to determine the design characteristics of the system.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.

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.

Cranking and startup fuel control has become increasingly important due to ever tightening emission requirements. Additionally, engine-off strategies during idle will require substantially more engine startup events with the associated need for very clean starts. Thus, knowledge of an engine's Air-Fuel Ratio (AFR) during its early cycles is necessary in order to optimize cranking and startup fueling. This paper examines and compares two methods of measuring an engine's AFR during engine startup (approximately the first second of operation); an in-cylinder technique using a Fast Flame Ionization Detector (FFID) and the conventional exhaust based Universal Exhaust Gas Oxygen (UEGO) sensor method. Engine starts using a Ford Zetec engine were performed at three different temperatures (0, 20 and 90 C) as well as different initial engine starting positions.