Search Results

This study proposes a method for presenting maneuver request information of accelerator pedal to a driver via the accelerator pedal itself. By applying periodic force like vibration on an accelerator pedal, information is transferred to the driver without displacing the accelerator pedal. In this study, the authors focus on a saw-tooth wave as the periodic force. When the saw-tooth-waved force is applied on the accelerator pedal, a human driver feels as if the accelerator pedal is knocked by someone periodically. In addition, information about the quantity of requested maneuver can be transferred by the amplitude of the saw-tooth wave. Based on these facts, the saw-tooth wave is modified and optimized empirically with ten human drivers so that the information of direction is transferred most reliably. In addition, the relationship between the amplitude of the saw-tooth wave and requested quantity of the pedal maneuver that the drivers feel is formulated.

Recently, a number of researches on detecting such inattentive driving have been carried out, and various detection methods have been proposed. In this paper, using driving data which can be measured with existing on-board vehicle sensors, the authors focus on headway distance behavior which relates to rear-end collision as well as the steering behavior which can be used to detect driver drowsiness. The emphasis of this study is that the inattentive driving can be potentially detected from the driving behavior deviated from the normative characteristics considering the transfer function from the environment to the driving operation. Therefore, intrusive measurement methods such as physiological measurement are not required. In addition, to detect the inattentive state by the criterion of the individual driver, the driver assistance system can be realized in the way that it can be adapted not only to various types of drivers, but also to the driver real-time driving state.

This study focuses on the scene that a vehicle overtakes a pedestrian in a narrow road in an urban area and proposes the safety index that shows the appropriate driving behavior in the target scene. Moreover, this study evaluates the validity of the index by experiments. In the target scenes, drivers need to drive carefully, preparing for the latent risk that the pedestrians may start crossing the roads suddenly. In other word, the vehicles must reduce the speed in order to be ready to avoid accidental contacts with pedestrians in case the pedestrians start crossing the road suddenly. However, according to the pedestrian model proposed by the authors in the previous study, the crossing angles of pedestrians are unpredictable. Therefore, the drivers must drive with such speed that the vehicle can avoid an accidental contact by braking even if the pedestrian start crossing the road at any angle.

This study aims to clarify the principal causes of head-on collisions and to establish a reference model when passing through unsignalized intersections without the right of way. The experiment was conducted on the test course using the experimental vehicle to collect driving data of expert drivers. The relation between the vehicle velocity and the face angle versus distance from the stop line was clarified and the characteristics of the safety confirmation behavior were obtained by analyzing driving data considering with driver's eye gaze.

A lot of countermeasures have been developed in order to reduce interior noise. For example, improvements of rubber mount characteristics and other measures have been implemented. Recently electromagnetic active engine mounts based on a hydraulic engine mount have been developed. They are significantly effective for the reduction of the booming noise which is unpleasant for passengers. Although the LMS algorithm has been generally used for the active control, it has been used only for reducing booming noise. The authors developed a new control method in order to reduce not only the booming noise but also the noise and the vibration over wide frequency band for comfortable vehicle interior space. The authors studied the method which determines the feedback gain according to various conditions by modifying LMS algorithm. In this modified LMS algorithm, only an error signal was used as an input signal.

This paper describes the classification algorithm of the driving steering intention based on brain-computer interface (BCI) using electroencephalogram (EEG). The classification algorithm of the driving steering intention was examined and developed. Experiments were conducted with 3 able-bodied subjects using driving simulator (DS). The drivers were instructed to operate the vehicle according to the series of three kinds of instructions (right steer, left steer, and straight running). Those instructions were given to the subject with random order, after the operation trigger had been signaled. The off-line analytical result shows that the driver's steering intention can be classified at the averaged probability of about 80%.

This paper proposes a driving torque control algorithm of micro-scale electric vehicle (NOVEL-I) at the scene of preceding vehicle following. First, an inter-vehicle distance controller aiming to keep a safe inter-vehicle distance is designed. Next, the experiments of vehicle following situation by automatic driving with the designed controller are realized and the effectiveness of the controller is verified. Next, the designed controller is applied to the human-vehicle closed-loop system as driver assistance systems, thereby enhancing driving comfort and reducing driving workload for human. Finally, the experiment of vehicle following by cooperative driving between the human and the controller is conducted. The experimental result in cooperative driving is compared with manual driving, and the effectiveness of the controller is verified.

The content of this paper is related to the analysis on driver characteristics in vehicle following situation using drive recorder. The characteristics are extracted from captured data related to rear-end collision incident which are collected on public road experiment using taxi. Main topic of this paper is concerning how drivers change their rear-end distance and cruising velocity according to snowy or icy road surface conditions. The characteristics are compared with public road experiment and experiment on proving ground. From the experimental results, the error of road surface estimation of driver could be one of reasons for the trigger of rear-end collision incident in snowfall season.

It is necessary to examine the driver behavior in intersections since many traffic accidents occur in the area. During yellow light, drivers occasionally cause the dilemma either passing through the intersection or stopping at stop line. In this paper, using the drive recorder equipped vehicle, many incident data that occurred during yellow light has been investigated. Using the collected data, actual dangerous zone during yellow light was extracted. Because the zone is one of the targets for accident prevention, the obtained understandings are useful in considering measures for enhancing safety such as road-vehicle communications.

Steering reaction torque is one of the most important types of information for drivers since it has significant influence on vehicle maneuverability. Even with today's advanced simulation technology, however, it is very difficult to accurately simulate steering feeling. The purpose of this study is to develop a steering Hardware-in-the-Loop (HIL) simulator that can quantitatively evaluate steering systems. This simulator can control the force on the tie rod with simple mechanism. The validity of this HIL simulator has been ascertained by comparing the simulation results with those obtained during actual vehicle testing.The preliminary research concerning the advanced simulators based on the developed HIL simulator is also reported.