GPS position errors were corrected for guidance of autonomous vehicles. From the vision, we can obtain the lateral distance from the center of lane and the angle difference between the left and right detected line. By using a controller which makes these two measurements zero, a lane following system can be easily implemented. However, the problem is that if there's no lane, such as crossroad, the guidance system of autonomous vehicle does not work. In addition, Line detection has problems working on curved areas. In this case, the lateral distance measurement has an error because of a modeling mismatch. For this reason, we propose GPS error correction filter based on curve-modeled lane detection and evaluated the performance applying it to an autonomous vehicle at the test site.

This paper proposes a situation awareness method based on data fusion and independent objects for autonomous driving in on-road environment. The proposed method, designed to achieve an accurate analysis of driving situations in on-road environment, executes preprocessing tasks that include coordinate transformations, data filtering, and data fusion and independent object based situation assessment to evaluate the collision risks of driving situations and calculate a desired velocity. The method was implemented in an open-source robot operating system called ROS and tested on a closed road with other vehicles. It performed successfully in several scenarios similar to a real road environment.

In this paper, we develop a sliding mode control for steering wheel angle control based on torque overlay in order to resolve the problem of previous methods for Electric Power Steering (EPS) systems in the Lane Keeping System (LKS) of autonomous vehicles. For the controller design, we propose a 2nd order model of the electric power steering system in an autonomous LKS. The desired state model is designed to prevent a rapid change of the steering wheel angle. The sliding mode steering wheel angle controller is developed for the robustness of the disturbance. Since the proposed method is designed based on torque overlay, torque integration with basic functions of the EPS system for the steering wheel angle control is available for the driver's convenience. The performance of the proposed method was validated via experiments.

In this paper, we propose a technique of drowsiness detection using a histogram for vehicle safety. The drowsiness of vehicle drivers is often the main cause of many vehicle accidents. Therefore, the checking of eye images in order to detect the drowsiness status of a driver is very important for preventing accidents. In our suggested method, we analyse the changes of a histogram of eye region images which are acquired using a CCD camera. We develop a drowsiness detection system using this histogram change information. The experimental results show that the proposed method enhances the accuracy of detecting drowsiness to nearly 97%, and can be used to prevent accidents due to driver drowsiness.

Automated driving systems require a high level of performance regarding environmental perception, especially in urban environments. Today's on-board sensors such as radars or cameras do not reach a satisfying level of development from the point of view of robustness and availability. Thus, map data is often used as an additional data input to support these systems. An accurate digital map is used as a powerful additional sensor. In this paper, we propose a new approach for vehicle localization using a lane map and a single-layer LiDAR. The maps are created beforehand using a highly accurate DGPS and a single-layer LiDAR. A pose estimation of the vehicle was derived from an iterative closest point (ICP) match of LiDAR's intensity data to the lane map, and the estimated pose was used as an observation inside a Kalmanfilter framework. The achieved accuracy of the proposed localization algorithm is evaluated with a highly accurate DGPS to investigate the performance with respect to lateral vehicle control.

Recently, in automotive technology area, intelligent safety systems have been actively accomplished for drivers, passengers, and pedestrians. Also, many researches are focused on development of autonomous vehicles. This paper propose the application of LiDAR sensors, which takes major role in perceiving environment, terrain classification, obstacle data clustering method, and local map building for autonomous driving. Finally, based on these results, planning for lane change path that vehicle tracking possible were created and the reliability of path generation were experimented.

This paper presents a multiple vehicle recognition algorithm based on radar and vision sensor fusion for lane change assistance. To determine whether the lane change is possible, it is necessary to recognize not only a primary vehicle which is located in-lane, but also other adjacent vehicles in the left and/or right lanes. With the given sensor configuration, two challenging problems are considered. One is that the guardrail detected by the front radar might be recognized as a left or right vehicle due to its genetic characteristics. This problem can be solved by a guardrail recognition algorithm based on motion and shape attributes. The other problem is that the recognition of rear vehicles in the left or right lanes might be wrong, especially on curved roads due to the low accuracy of the lateral position measured by rear radars, as well as due to a lack of knowledge of road curvature in the backward direction. In order to solve this problem, it is proposed that the road curvature measured by the front vision sensor is used to derive the road curvature toward the rear direction. Finally, the proposed algorithm for multiple vehicle recognition is validated via field test data on real roads.

In this paper, we propose a robust curved lane marking detection method. Several lane detection methods have been proposed, however most of them have considered only straight lanes. Compared to the number of straight lane detection researches, less number of curved-lane detection researches has been investigated. This paper proposes a new curved lane detection and tracking method which is robust to various illumination conditions. First, the proposed methods detect straight lanes using a robust road feature image. Using the geometric relation between a vehicle camera and the road plane, several circle models are generated, which are later projected as curved lane models on the camera images. On the top of the detected straight lanes, the curved lane models are superimposed to match with the road feature image. Then, each curve model is voted based on the distribution of road features. Finally, the curve model with highest votes is selected as the true curve model. The performance and efficiency of the proposed algorithm are shown in experimental results.

In hybrid or electric vehicles, the hydraulic brake system must be controlled cooperatively with the traction motor for regenerative braking. Recently, a motor driven brake system with a PMSM (Permanent Magnet Synchronous Motor) has replaced conventional vacuum boosters to increase regenerative power. Unlike industry motor controls, additional source codes such as functional safety are essential in automotive applications to meet ISO26262 standards. Therefore, the control logic execution time increases, which also causes an extension of the motor current control period. The increased current control period makes precise motor current control challenging inhigh speed ranges where the motor is driven by high frequency. In this paper, a PWM update strategy and a time delay compensation method are suggested to improve current control and system performance. The proposed methods are experimentally verified.

This paper introduces a sway suppression control for container cranes with unknown payloads and sway rates. With no priori knowledge concerning the magnitude of payload mass and sway rate, the proposed control maintains superior sway suppressing and trolley positioning against external disturbances. The proposed scheme combines a second order sliding mode control and an adaptive control to cope with unknown payloads. A second order sliding mode control without feedback of the sway rate is first designed, which is based on a class of feedback linearization methods for stabilization of the under-actuated sway dynamics of the container. Under applicable restrictions of the magnitude of payload inertia and sway rate, a linear regression model is obtained, and an adaptive control with a payload estimator is then designed, which is based on Lyapunov stability methods for the fast attenuation of trolley oscillations in the vicinity of the target position. The asymptotic stability of the overall closed-loop system is assured irrespective of variations of rope length. Simulation are shown in the existence of initial sway and external wind disturbances.

In this paper, a marionette control system using wrist and finger gestures through an IMU sensor is studied. The signals from the sensor device are conditioned and recognized, then the commands are sent to the 8 motors of the marionette via Bluetooth (5 motors control the motion of the marionette, and 3 motors control the location of the marionette). It is revealed that the degree of freedom of fingers are not independent from each other, therefore, some gestures are hardly made. Gesture mode changes for difficult postures of the fingers in cases of a lack of finger DOF are proposed. Therefore, the gesture mode change switches the assignment of gesture as required. Experimental results show that gesture mode change is successful for appropriate postures of a marionette.

For machining systems having a high precision positioning with a long stroke, it is necessary to examine the repeatability of reference position decisions. Though ball-screw driven linear stages equipped with encoders have high precision feed drivers and a long stroke, they have some limitations for reference position decisions if they have not been equipped accurate home sensors. High precision machining technology has become one of the most important aspects of the development of a precision machine. Such a machine requires high precision positioning as well as high speed on a large workspace. This study is performed to experimentally compare the repeatability for home position decisions in the case of photo sensors and hall sensors as a home switch of the ball-screw driven linear stage.

In this paper, we propose a F-MBE algorithm for Dual mode seeker (CCD and IIR). The MBE algorithm show improved performance at the IIR target size estimation. but the MBE can't use at Dual Mode seeker. To overcome this problem,, we apply template matching method for CCD target size information. The performance of proposed F-MBE method is tested at target intercept scenario of dual mode seeker equipped missile. The experiment results show that the proposed algorithm has the relatively improved performance.

In general, open-loop fiber-optic gyroscopes (FOG) are less stable than closed-loop FOGs but they offer simpler implementation. The typical operation time of line-of-sight (LOS) stabilization systems is a few seconds to one hour. In this paper, a open-loop fiber optic gyroscope (FOG) for LOS applications is designed and implemented. The design goal is aimed at implementing a low cost, compact FOG with low Angle Random Walk (ARW) (< $0.03deg/\sqrt{h}$) and bias instability (< 0.25deg/h). The FOG uses an open-loop all-fiber configuration with 100M PM fiber wound on a small diameter spool. In order to get the design goal, digital signal processing techniques for signal detection, modulation control and compensation are designed and implemented in FPGA.

Cloud systems are efficient models that can utilize various infrastructures, platforms, and applications regardless of the type of clients. This paper proposes a cloud-based integrated development environment (IDE) for robot software development which would make software development easier. The proposed system provides robot simulation to test the robot HW modules or robot systems for development and testing of software operating in a robot system with two or more different operating systems (OS) such as Windows, Linux, and real-time OS. This paper implements and evaluates the proposed system using OPRoS [33].