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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.

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.

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.

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.

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.

In making driver workload assessments, it is important to evaluate the driver's level of brain activity because the operation of a motor vehicle presumably involves higher-order brain functions. Driving on narrow roads in particular probably imposes a load on the driver's brain functions because of the need to be cognizant of the tight space and to pay close attention to the surroundings. Test vehicles were fitted with a functional near-infrared spectroscopy (fNIRS) system for measuring bloodstream concentrations at 32 locations in the frontal lobe of the participating drivers in order to evaluate their levels of mental activity while driving on narrow roads. The results revealed significant increases in cerebral blood flow corresponding to the perceived workload. This suggests that increases in cerebral blood flow can be used as an effective index for estimating mental workloads.

For the purpose of protecting the driver from injury in a crash, a safety standard has been established that specifies the allowable impact force when the driver hits the steering wheel as a result of an impact. In this work, an investigation was lade of steering column collapse behavior in which the steering column was constructed such that the crash energy was absorbed by the bending and tearing of a thin steel plate. As a result, a finite element analysis method has been developed for conducting an impact analysis using a ductile fracture model of the thin plate. This method makes it possible to predict the collapse behavior of the steering column and the deceleration that develops on the body block of the safety standard test model.

Eight rollover research tests were conducted using the 2001 Nissan Pathfinder with a modified FMVSS 208 dolly rollover test method where the driver and right front dummy restraint performance was analyzed. The rollover tests were initiated with the vehicle horizontal, not at a roll angle. After the vehicle translated laterally for a short distance, a trip mechanism was introduced to overturn the vehicle. Retractor, buckle, and latch plate performance in addition to the overall seat belt performance was analyzed and evaluated in the rollover test series. Retractor pretensioners were activated near the rollover trip in three of the tests to provide research data on its effects. Various dummy sizes were utilized. The test series experienced incomplete data collection and a portion of the analog data was not obtained. National Automotive Sampling System (NASS) data was also analyzed to quantify the characteristics of real world rollovers and demonstrated the benefits of restraint use.

This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel.

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.

The progress of computer technology and CAE methodology makes it possible to simulate dummy injury readings in vehicle crash simulations. Dummy neck injuries are generally more difficult to simulate than injuries to other regions such as the head or chest. Accordingly, improving the accuracy of dummy neck injury data is a major concern in frontal occupant safety simulations. This paper describes the use of an advanced airbag modeling methodology to improve the accuracy of dummy neck injury readings. First, the following items incorporated in the advanced airbag model are explained. (1) The Finite Point Method (FPM) is used to simulate the flow of gas. (2) A folding model is applied to simulate the folded condition. (3) The fabric material properties used in the simulation take into account anisotropy in the fiber directions and the nonlinear, hysteresis characteristics of stiffness.