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The purpose of this study is to develop an autonomous lane change control system that adapts to variable surrounding conditions, to ensure vehicle safety and traffic flow stability. In this paper, we propose decision-making and control procedures for realizing autonomous lane changing; to this end, we consider not only behaviors for changing lanes but also those involved in approaching the lane changing state, with a focus on the controller design. A decoupled control structure and longitudinal trajectory-free control approach are suggested. We design a novel inter-vehicle spacing policy and a 3DOF lateral error vehicle dynamics model. To verify the effectiveness of our system, simulation experiments are performed for 12 scenarios, and system assessments are conducted based on four evaluation perspectives. The results confirm that our system can safely control the vehicle amidst various surrounding vehicle conditions and can also ensure vehicle motion stability. Furthermore, we solve the existing dynamic instability problem of lateral control, which arises through longitudinal acceleration variability. Another significant advantage of this model is that the controlled vehicle does not interfere with the target-lane traffic flow and smoothly synchronizes with the flow during lane changing.

Vehicle emissions are largely determined by the details of driving behaviours. Accordingly, emissions are often estimated by integrating micro-scale emission models into traffic simulations. Under this approach, it is essential to replicate the actual traffic situation being considered in an emission evaluation using a proper calibration procedure. Most previous research with respect to traffic flow has primarily focused on adjusting the complex combinations of parameters evaluated in these models, but it is not guaranteed that the use of widely used calibration measures can lead more accurate emissions estimates. Accordingly, we propose a systematic guideline for calibration to ensure reliable micro-scale emissions estimates. A calibration procedure is thus established in this paper based on various measure of effect (MOE) compositions (i.e., calibration levels) consisting of aggregated traffic data to identify the level that most reliably estimates micro-scale emissions. Five calibration levels of progressively more detailed measurements are first defined, valid calibration levels are identified, and the reliable calibration level is finally selected based on the available traffic data. The effect of vehicle type (i.e., light vs. heavy vehicles) composition on the estimated emissions is also evaluated for a well-calibrated simulation. We expect that a highly reliable estimation of emissions is possible using this more detailed traffic simulation calibration measurement.

For driving safely and comfortably, the long-term trajectory prediction of surrounding vehicles is essential for autonomous vehicles. For handling the uncertain nature of trajectory prediction, deep-learning-based approaches have been proposed previously. An on-road vehicle must obey road geometry, i.e., it should run within the constraint of the road shape. Herein, we present a novel road-aware trajectory prediction method which leverages the use of high-definition maps with a deep learning network. We developed a data-efficient learning framework for the trajectory prediction network in the curvilinear coordinate system of the road and a lane assignment for the surrounding vehicles. Then, we proposed a novel output-constrained sequence-to-sequence trajectory prediction network to incorporate the structural constraints of the road. Our method uses these structural constraints as prior knowledge for the prediction network. It is not only used as an input to the trajectory prediction network, but is also included in the constrained loss function of the maneuver recognition network. Accordingly, the proposed method can predict a feasible and realistic intention of the driver and trajectory. Our method has been evaluated using a real traffic dataset, and the results thus obtained show that it is data-efficient and can predict reasonable trajectories at merging sections.

In this paper, we propose a lateral upper controller that predicts vehicle motion and generates a model-free LKAS steering angle by applying long-term memory (LSTM), one of the deep learning techniques. The apparent and distinct advantage of this LSTM model is that the relationship of nonlinear sensor data can be grasped and learned devoid of a mathematical model while using the time information. In this sense, the LKAS steering angle can be generated by predicting the movement of the vehicle in consideration of the past vehicle motion based on the sensor data. In addition, the time delay problem due to the difference of sampling time on each sensor can be simply solved by learning based on a time table which has a synchronized sampling time. The input values of the upper controller are the coefficient of the road model and the vehicle dynamic characteristics are obtained from the image processing data from the camera sensor. As for the target values, the steering angles in the next state are selected. The learning model was developed by learning the many-to-one LSTM prediction model with serial connection of LSTM and Fully-Connected (FC) Multilayer Perceptron (MLP). For implementation of the learning model, Tensorflow is employed and the data from a real test road was used. With this model, the learning is conducted and its effectiveness is shown by comparing with a LKAS lateral controller using a model-based multi rate Kalman Filter (MKF).

This paper clarifies an issue that the most commonly used ADAS sensors, monocular camera and radar, do not provide abundant information about dynamically changing road scenes. In order to make the sensor more useful for a wide range of ADAS functions, we present an approach to estimate the orientation of surrounding vehicles using deep neural network. We show the possibility that camera-based method can get more competitive, evaluating it on the KITTI Orientation Estimation Benchmark, and also verifying it on our test-driving scenarios. Although its localization performance is not perfect, our model is able to reliably predict the orientation when fine conditions are given. In addition, we further study on training models using synthetic dataset, and share the weakness of this method when comparing to LiDAR-based approach on several conditions such as fully-visible, lightly/heavily-occluded and shading/lighting circumstances.

The acceleration of a vehicle is important information in vehicle states. The vehicle acceleration is measured by an inertial measurement unit (IMU). However, gravity affects the IMU when there is a transition in vehicle attitude, thus, the IMU produces an incorrect signal output. Therefore, vehicle attitude information is essential for obtaining correct acceleration information. This paper proposes a convolutional neural network (CNN) for attitude estimation. Using sequential data of a vehicle’s chassis sensor signal, the roll and pitch angles of a vehicle can be estimated without using a high-cost sensor such as a global positioning system or a six-dimensional IMU. This paper also proposes a dual-extended Kalman filter (DEKF), which can accurately estimate acceleration/angular velocity based on the estimated roll/pitch information. The proposed method is validated by real-car experiment data and CarSim, a vehicle simulator. It accurately estimates the attitude estimation with limited sensors, and the exact acceleration/angular velocity is estimated considering the roll and pitch angle with de-noising effect. In addition, the DEKF can improve the modeling accuracy and can estimate the roll and pitch rates.

We investigated the protection capability of a plate against high speed projectiles demonstrating collision and penetration behaviors by finite element analysis. The element erosion method was used for penetration analysis, which showed that the speed of the projectile was slightly reduced by the collision with the protection plate. Protection capability was measured by the projectile’s attitude angle change because the damage of our tanks by projectiles was also dependent on the projectile-tank collision angle. When the length of the protection plate was sufficiently long, the projectile was severely deformed and incapacitated. In the case of a small plate, the projectile was deformed only in the collision region. Thus, projection capability was investigated by the change of attitude angle. The effect of collision angle, velocity, and length of the plate on the rotational and vertical velocities of the projectile was investigated.

Many studies using a laser scanner have been conducted in order to study the environment of vehicles in real time. The method to find the driving area using a two-dimensional lidar sensor is divided into a forward-looking lidar sensor and a downward-looking lidar sensor based on the installation method. A downward-looking lidar sensor looks at the ground, enabling it to recognize kerbs and ditches which are lower than the installation position of the sensor. However, a downward-looking lidar sensor requires pre-processing to find the road boundary. The existing sensor models cannot generate an occupancy grid map without support, as the driving area recognized through a downward-looking lidar sensor forms a circular sector shape from the sensor installation position to the road boundary. This paper proposes a road sensor model that is capable of modelling an occupancy grid. We also propose a method to generate an occupancy grid map more suitable for autonomous vehicles by presenting the occupancy grid map in curvilinear space. The proposed method was validated by an experiment at Hanyang University campus and the quantitative results obtained from that experiment. We also compared this method with three conventional sensor model methods. The experimental results show that our method performs better than the conventional methods do in terms of both visual qualities and metric qualities.

This paper proposes a geometric approach for forward kinematics analysis of a 3-SPS/S redundant motion mechanism using conformal geometric algebra (CGA). The forward kinematics of a parallel kinematic mechanism is generally very complicated and difficult to analyze. The proposed geometric method is useful because it provides simple, fast, and complete solutions to the problem and helps determine the relationship between the joints and end-effector without an iterative process. Thus, we introduce a geometric approach using CGA in an intuitive, stepwise fashion. We also use an extra sensor to provide more positional information, thereby allowing a unique solution to be selected geometrically from among the multiple solutions found using the geometric approach. In the mechanism considered herein, three identical legs linked by prismatic actuators are attached to a moving platform and to the base by two passive spherical joints. A passive leg is present at the center of the mechanism; it connects the center of the base to the platform, constraining the platform’s movement. The added components constrain the movement of the platform, making it possible to analyze the mechanism using a geometric approach. Herein we present performance comparisons that validate use of the proposed approach in real-time applications and demonstrate its low computational load.

This paper has presented optimal coordination of Rear Wheel Steering and Electronic Stability Control. The algorithm consists of three parts. First, state estimators for tire force, cornering stiffness and side slip angle have been designed. Second, desired vehicle motion has been generated considering driving situation and driver’s intention. Third, optimal coordinator has distributed the control task to the individual chassis control using analytical method with Karush-Kuhn-Tucker. The purpose of this study can be divided into two categories. One is optimal control of Rear Wheel Steering and Electronic Stability Control. Therefore, new cost functions have been defmed. The other is fault tolerant control during actuator fault or brake circuit failure. The performance of the proposed control system has been validated through closed-loop simulations with MATLABlSimulink and Carsim. The simulation results show that proposed system does not only distribute the control input to each control system but also ensure vehicle safety under actuator failure.

In this paper, an actuation mechanism for high-speed aiming of a target is proposed. The mechanism is a 3DOF-SPS (spherical-prismatic-spherical) parallel manipulator and can be used for a missile defense system with a fast reaction time. This type of parallel mechanism has high rigidity against external disturbances and accordingly high stiffness and precision. The target aiming requires 2 degrees of freedom and this 3 DOF mechanism has one redundancy. For fast manipulation of the proposed mechanism, the redundancy can be exploited and an optimal solution can be found out of the infinite number of inverse kinematic solutions. For finding a near time-optimal solution, a cost function is formulated considering displacement of each parallel link and an optimization technique is used for solution of the inverse kinematic problem.

We propose a 3-SPS/S redundant motion mechanism with kinematic redundancy in this paper. The proposed mechanism with redundancy enables redundant motion as it works like pan/tilt systems to reduce the unnecessary degree of freedom for the yaw and pitch motions in the configuration space. By reducing one degree of freedom, this robot has self-motion that causes the inverse kinematics to have infinite solutions, so that the proposed research performs an optimization process for obtaining the optimal solution using geometric approaches. This optimal solution provides the fastest response among the infinite solutions by minimizing each link movement to the target orientation. In addition, we perform a geometric singularity analysis due to limited link length and avoidance using conformal geometric algebra for the feasibility and correctness of the inverse kinematics.

A parallel mechanism with redundancy can be regarded as a means for not only maximizing the benefits of parallel mechanisms but also overcoming their drawbacks. We proposed a novel parallel mechanism by eliminating an unnecessary degree of freedom of the configuration space. Because of redundancy, however, the solution for the inverse kinematics of the developed parallel mechanism is infinite. Therefore, we defined a cost function that can minimize the movement time to the target orientation and found the solution for the inverse kinematics by using a numerical method. In addition, we proposed a method for determining the boundary of the geometric singularity in order to avoid singularities.

This paper presents a scheduling problem for a high-density robotic workcell using multi-objective genetic algorithm. We propose a new algorithm based on NSGA-II(Non-dominated Sorting Algorithm-II) which is the most popular algorithm to solve multi-objective optimization problems. To solve the problem efficiently, the proposed algorithm divides the problem into two processes: clustering and scheduling. In clustering process, we focus on multi-robot positions because they are fixed in manufacturing system and have a great effect on task distribution. We test the algorithm by changing multi-robot positions and compare it to previous work. Test results shows that the proposed algorithm is effective under various conditions.

A new method for supervoxel segmentation based on a weighted graph in complex urban environment is proposed. The proposed algorithm is designed to minimise the cost of the shortest path in the weighted graph with consideration of characteristics of the sensor for complex urban environment. We collected point clouds in a complex urban environment to evaluate the proposed method. The performance of the proposed algorithm was evaluated using boundary recall and under-segmentation errors.

This work was supported by the research fund of Survivability Technology Defence Research Center of the Agency for Defence Development of Korea (No. UD150013ID).

This paper presents methods of localization of mobile systems using recent Radio Frequency Identification (RFID) technology. We consider an indoor environment where RFID tags are implanted along the wall or in objects in the room. If the absolute position and orientation of a tag are read by an RF reader, a mobile system can estimate its location using the information saved in the tags. A reader-tag model is obtained through experiments in order to derive relative positions and orientations between an antenna and an RFID tag. To estimate the location, we propose two estimation methods. One uses a single RFID tag and the other uses multi-RFID tags. Experimental results show that the proposed methods can provide good performance for mobile system localization in an indoor environment.

Disturbance propagation and string stability of a large vehicle platoon that consists of a part of the traffic flow is closely related to traffic shockwaves and oscillation. In this respect, the concepts of the estimation and prediction of shockwave propagation speeds and congestion should be considered in order to establish a control strategy for safe conditions without collisions even when the congestion is amplified in an unstable string of the large platoon. This means that an advanced approach for a car-following control strategy, which includes a time delay and non-linearity terms, is necessary for the enhancement of Vehicle Platoon Control (VPC) and the system robustness. In this research, we have demonstrated the effect of the disturbance propagation phenomenon on traffic flow stability. The traffic flow shockwave and oscillation are interpreted in terms of both macroscopic and microscopic approaches. We also discuss how the phenomenon affects VPC systems based on the optimal velocity model (OVM), which is an advanced car-following model. In addition, we improve the OVM, which is called the advanced OVM, by including a term for the delay time and by setting up a boundary condition of acceleration in order to enhance the VPC system and to ensure its robustness.

A wireless capsule endoscope has been developed to replace the conventional endoscope. However, the commercialized capsule endoscope moves passively by peristaltic waves, which has some limitations for doctors to diagnose more thoroughly and actively. In order to solve this problem, a locomotive mechanism is proposed for wireless capsule endoscopes. Based on the tests of various actuators, a piezo actuator is selected as a micro actuator for capsule endoscope. In general, piezo actuators are known to have limited displacement with high voltage supply. In order to overcome the limitation of common piezo actuator, the impact based piezo actuator, is developed to realize long stroke up 11mm. By using the impact based piezo actuator, a prototype of an earthworm-like locomotive mechanism was developed. In addition, the proposed locomotive mechanism has engraved clamps mimicked the claw of an insect. The earthworm-like locomotive mechanism has 15 mm in diameter and 30mm under retraction stage and 41 mm under elongation stage in total length. Hollow space is allocated to comprise essential endoscope components such as a camera, a communication module, bio sensors, and a battery. For the feasibility test of proposed locomotive mechanism, a series of experiments were carried out including in-vitro tests. Based on results of the experiments, we conclude that the proposed locomotive mechanism is effective to be used for micro capsule endoscopes.

Vision-based lane-sensing systems require accurate and robust sensing performance in lane detection. Besides, there exists trade-off between the computational burden and processor cost, which should be considered for implementing the systems in passenger cars. In this paper, a stereo vision-based lane detection system considering sensor configuration aspects such as field of view (FOV), span pixels, resolution, etc is developed. An inverse perspective mapping method is formulated based on the relative correspondence between the left and right cameras so that the 3D road geometry can be reconstructed in a robust manner. The selection rule of the sensor configuration and specifications is investigated for a standard highway. Based on the selected sensor configurations, it is shown that sensing region range on the camera image coordinate can be determined for the best lane-sensing performance. The proposed system is implemented on a passenger car and its real-time sensing performance is verified experimentally.

This paper presents a lead-lag compensator design for a MEMS-fabricated microgyroscope. The microgyroscope is basically a high Q system, thus the bandwidth is limited to be narrow. To overcome the open-loop performance limitations, a feedback controller is designed to improve the resolution, bandwidth, linearity, and bias stability of the microgyroscope. The feedback controller is applied to the z-axis microgyroscope fabricated by SBM process. In MATLAB simulations, resolution, bandwidth, input range, and bias stability of closed-loop system are improved from 0.0021 deg/sec to 0.0013 deg/sec, from 14.8 Hz to 115 Hz, from ±50 deg/sec to ±200 deg/sec, and from 0.0249 deg/sec to 0.0028 deg/sec, respectively.

This paper presents a novel z-axis accelerometer with perfectly aligned vertical combs fabricated using the extended sacrificial bulk micromachining (extended SBM) process. The z-axis accelerometer is fabricated using only one (111) SOI wafer and two photo masks without wafer bonding or CMP processes as used by other research efforts that involve vertical combs. In our process, there is no misalignment in lateral gap between the upper and lower comb electrodes, because all critical dimensions including lateral gaps are defined using only one mask. The fabricated accelerometer has the structure thickness of , the vertical offset of , and lateral gap between electrodes of . Torsional springs and asymmetric proof mass produce a vertical displacement when an external z-axis acceleration is applied, and capacitance change due to the vertical displacement of the comb is detected by charge-to-voltage converter. The signal-to-noise ratio of the modulated and demodulated output signal is 80 dB and 76.5 dB, respectively. The noise equivalent input acceleration resolution of the modulated and demodulated output signal is calculated to be and . The scale factor and linearity of the accelerometer are measured to be 1.1 mV/g and 1.18% FSO, respectively.

There is a growing tendency to use the Internet as the transmission medium of the telerobotic system since the Internet is inexpensive and available all over the world. However, the bilateral real-time teleroperation can be unstable due to the random time delays of the Internet. The `Event based teleoperation' is one of the fine approaches to overcome the random time delays. Since the event based control uses a non-time action reference, the stability and response performance of the system are affected by the time which an event is held for. This study introduces the variable holding time to secure stability and response performance at once. The proper holding time for each event is obtained depending on the characteristics of the task through the fuzzy logic. The proposed control scheme was implemented on a robot arm over the Internet to show its effectiveness.

It is known that existence of irregular transmission time delay is a major bottleneck for application of advanced robot control schemes to internet telerobotic systems. In the internet teleoperation system, the irregular transmission time delay causes a critical problem, which includes instability and inaccuracy. This paper suggests a practical internet teleoperation system with streaming buffer system, which consists of a buffer, a buffer manager, and a control timer. The proposed system converts the irregular transmission time delay to a constant. So, the system effectively transmits the control input to a remote site to operate a robot stably and accurately. This feature enables short control input intervals. That means the entire system has a large control bandwidth. The validity of the proposed method is demonstrated by experiments of teleoperation from USC (University of Southern California in U. S.A.) to HYU (Hanyang Univ. in Korea) through the Internet. The proposed method is also demonstrated by experiments of teleoperation through the wireless internet.

This paper describes a geometrical approach for analysing the inverse kinematics of a 7 Degrees of Freedom (DOF) redundant manipulator. The geometric approach is desirable since it provides complete and simple solutions to the problem and determines the relationship between the joints and the end-effector without iterative process. This paper introduces the approach to solve kinematic solution of 7 DOF in an intuitive way using conformal geometric approach step by step. We finally present the comparison with pseudo inverse solution which is the most well-known method in redundant manipulator kinematic problem at the same simulation environment.

It was shown that the robustness of deterministic observers with respect to modeling errors, measurement bias and round-off errors can be represented by a single performance index, x 2 (P), the condition number of the observer eigenvector matrix. In this paper, based on the performance index, a robust observer design method is proposed for two-output systems where the observer gain can not be determined uniquely with respect to the desired observer poles. Utilizing the pole/zero locations of the observer dynamics, a step-by-step design procedure of the well-conditioned observer is developed. In addition, a new scaling method is developed with employing the eigenvector matrix as a scaling matrix. The proposed method is evaluated and compared with the Kalman filter technique in a spindle-drive simulation example where the load speed needs to be estimated based on the measured signals.

Optimal task sequence planning using a simulated annealing technique is developed for a high speed assembly station comprised of two robots. In order to minimize the tact time, parts are assembled by dual robots on moving workpieces which are transferred by a precision conveyer. The sequence of operations by the dual robots is optimized to minimize the tact time while avoiding the geometric and dynamic interferences between the dual robots. The optimization problem is a type of traveling salesman problem, but is highly complicated because the target destinations for placing individual parts keep moving on the conveyer. The speed of the conveyer which carries the workpieces should be also optimized in order to maximize the throughput of the assembly system. The task sequence optimization problem is formulated together with the problem of determining the conveyer speed. An efficient algorithm for the sequence optimization is developed and solved by the simulated annealing.