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The safety of an automated vehicle requires accurate information of surrounding conditions, because a false sensor output can lead to a fatal accident during driving. Thus, monitoring of abnormalities in every sensor is important for robust perception of the environment. Since it is difficult to obtain anomalous data, it is hard to develop a robust detection algorithm using only a relatively small number of anomalies. In this paper, we propose a data augmentation method for oversampling minority anomalies in lane detection. Using a generative adversarial network that makes the generator learn to estimate the distribution of anomalous data, it generates synthesized minority anomalies. The generated anomalies are used to train an anomaly detection network while minimizing latency for use in real situations. During training, the generated anomalies, with various mixed quality, are sampled differently according to their quality. This helps the detection network to be optimized with better quality data. Experimental result shows that when using the proposed anomaly detection framework for monitoring lane abnormality, it improves the performance by 12% when compared to the vanilla recurrent neural network.

Prediction of surrounding vehicles accurately is an essential prerequisite for safe autonomous driving. Trajectory prediction methods can be classified into physics-, maneuver-, or learning-based methods. Learning-based methods have been studied extensively in recent years because it effectively exploits the road information and interactions among vehicles. However, learning-based methods perform poorly in unseen environments that were not considered during training and provide unreasonable results such as inconsistent trajectories according to road geometry. In this paper, to address this problem, a hybrid model that combines a learning-based model with physics- and maneuver-based models according to their uncertainties is proposed. The deep ensemble technique is also used to estimate the uncertainty of the learning-based method. Because the deep ensemble tends to show a large variance in unseen environments, this method is used to determine whether to use a hybrid model. The proposed method is trained and validated using the Lyft l5 dataset, the real environment vehicle driving data containing several types of intersections.

One of the main issues that potentially cause faults in ego-vehicle trajectory prediction is various styles of drivers. To deal with this problem, we propose Driving Style Attention Generative Adversarial Network (DSA-GAN), which can generate the trajectory of ego-vehicle conditioned on the driving style. This system can be adopted in many vehicles because it only needs CAN-bus data to predict the trajectory. The proposed architecture involves two stages, Driving style recognition and Trajectory prediction. In the Driving style recognition, Recurrence Plot (RP) transforms sequential data into images and the converted images are processed into the driving styles by Convolutional Neural Network (CNN). In the Trajectory prediction part, Conditional Generative Adversarial Network (CGAN) generates the multi-modal realistic trajectories from the distribution and these trajectories are conditioned by the driving style. In this paper, we predict more realistic and accurate trajectories than conventional prediction methods, even if a driver's driving style is not categorized by our defined classes.

Even though some of the driver assistant systems have been commercialized to provide safety and convenience to the driver, they can be applied for autonomous driving in limited situations such as highways. In this paper, we propose a supervisor agent that can enhance the driver assistant systems by using deep distributional reinforcement learning. The supervisor agent is trained using end-to-end approach that directly maps both a camera image and LIDAR data into action plan. Because the well-trained network of deep reinforcement learning can lead to unexpected actions, collision avoidance function is added to prevent dangerous situations. In addition, the highway driving case is a stochastic environment with inherent randomness and, thus, its training is performed through the distributional reinforcement learning algorithm, which is specialized for stochastic environment. The optimal action for autonomous driving is selected through the return value distribution. Finally, the proposed algorithm is verified through a highway driving simulator, which is implemented by the Unity ML-agents.

Adaptive cruise control (ACC) considering vehicle performances becomes a core technology for autonomous vehicle to adjust the vehicle speed while maintaining a safe distance from preceding vehicles...

This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction. We propose a simple encoder-decoder architecture based on multi-head attention. The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel. Our approach to model the interactions can learn to attend to a few influential vehicles in an unsupervised manner, which can improve the interpretability of the network. The experiments using naturalistic trajectories at highway show the clear improvement in terms of positional error on both longitudinal and lateral direction., Comment: 6 pages, 5 figures, 2020 IEEE Intelligent Vehicles Symposium (IV)

Estimation algorithms for road slope angle and vehicle mass are presented for commercial vehicles. It is well known that vehicle weight and road grade significantly affect the longitudinal motion of a commercial vehicle. However, it is very difficult to measure the weight and road slope angle in real time because of lack of sensor technology. In addition, the total weight of a commercial vehicles varies depending on the freight. In this study, the road grade and vehicle mass estimation algorithms are proposed using the RLS (Recursive Least Square) method and only the in-vehicle sensors. The proposed algorithms are verified in experiments using a commercial vehicle under various conditions.

Recent research suggests the various applications of Model Predictive Control on vehicle systems. In numerous cases, nonlinear tire models such as the Magic Formula, which are highly complex and are more detailed than necessary, are used. This paper presents a nonlinear tire model that excludes the region of negative slope but expresses the nonlinear properties of tire well enough for tracking the lane of a racing course. The proposed inverse tire model can also be used to calculate the slip angle from the tire force. Thus, the model can be utilized to design the Model Predictive Controller.

Recently, an active safety system based on vehicle-to-vehicle communication was introduced to minimize the threat of car accidents on the road and to overcome the limitations of the current sensor-based advanced driver assist system. In order to implement the system based on vehicle-to-vehicle communication, target classification is the key layer to be developed. In this study, based on the transmitted path history of the remote vehicles, the road geometry around the host vehicle is reconstructed without in-vehicle sensors such as vision cameras. A tracking algorithm for the remote vehicle is formulated in order to predict its position continuously by using an extended Kalman filter. A local map is obtained with an outlier filter, and the target classification algorithm is designed from vehicle-to-vehicle communication data. The proposed algorithms are validated by simulations and experiments carried out using test vehicles.

This paper presents a fault-tolerant brake torque controller for four-wheel-distributed braking systems with in-wheel motors and Electro-Mechanical Brakes (EMB). Mechanical and electrical faults can degrade the performance of the EMB actuators and, thus, their effects need to be compensated in vehicle dynamics level. In this study, the faults are identified as performance degradation and expressed by the gains of each actuator. Assuming the brake force distribution and the regenerative braking ratios, the over-actuated braking system is simplified into a two-input system. A sliding mode controller is designed to track the driver’s braking and steering commands, even if there exist faults in EMBs. In addition, adaptive schemes are constructed to achieve the fault-tolerant control in braking. The proposed controller and strategies are verified in the EMB HILS (Hardware-in-loop-simulation) unit for various conditions.

It is well known that laser scanner has better accuracy than stereo vision in detecting the distance and velocity of the obstacles, whereas stereo vision can distinguish the objects better than the laser scanner. These advantages of each sensor can be maximized by sensor fusion approach so that the obstacles in front can be detected accurately. In this paper, high-level sensor fusion for the laser scanner and stereo vision is developed for object matching between the sensors. Time synchronization, object age, and reordering algorithms are designed for robust tracking of the objects. A time-delay update algorithm is also developed to determine the process time delay of the laser scanner. The expanded laser scanner data at every 1 ms is predicted by Kalman filter and is matched with the stereo vision data at every 66 ms. A cost function is formulated to describe the object matching similarity between the sensors, and the best matching candidate is selected for the minimum cost function. The proposed matching algorithms are verified experimentally in field tests of various maneuvering cases.

Estimating the maximum road friction coefficient with high reliability in various driving situation is one of the most significant issue in the field of automotive research. Numerous study has been done in this field, however, because of the several limitations and problems, researches in this field are still active. This paper uses a deep learning method to estimate the maximum road friction coefficient. The network of this study is mainly composed of convolutional neural network, recurrent neural network with deep ensemble architecture. In addition, through Prioritized Batch Selection (PBS), which is proposed in this paper, the training result is dramatically enhanced. The performance of the proposed estimator is verified in simulation of test driving scenarios.

Semi-trailer trucks generate the articulation angle during turning situation due to structural characteristics. This angle can cause the trailer to deviate from the road boundary and, thus, lead to collision with structures and/or vehicles on the roadway. In this study, a narrow passage assistant system is proposed using rear steering so that the semi-trailer truck is controlled inside the lane boundary even in tight turning circumstances. Based on the measured lane information using camera sensors, the model predictive control is applied for predicting the future state and tracking the reference path. The distribution rule for steering angles of the multi-axle trailer is described such that each wheel of the trailer has the same turning center and their tire slip angles are consistent. The proposed system is verified using commercial software and its performance demonstrates the effectiveness of rear steering in semi-trailer truck.

This paper proposes semi-autonomous control that minimizes intervention considering driver's steering and braking intentions. The biggest challenge of this problem is how to fairly judge driver's intentions that appear differently in the lateral and longitudinal directions and how to minimize controller intervention. A decoupled model predictive control (MPC) and optimal intervention decision methods are proposed considering driver incompatibility. Several MPCs are designed first considering the fact that the driver can avoid obstacles either by braking or moving to the left or right lanes. The control input to avoid the collision is calculated for each MPC such that its intervention can be minimized reflecting the driver's intention. After driver incompatibility is formalized, the optimal input is selected to minimize the incompatibility among the paths that can avoid accidents. The proposed algorithm is validated in simulations where collision can be avoided while minimizing the intervention.

The slope angle is one of the most influential factors which limit the control performance and the energy efficiency of not only internal-combustion engine vehicles but also electrified vehicles such as hybrid vehicles and plug-in hybrid electric vehicles. However, measuring the slope angle is not easy because slope sensors are not sufficiently accurate and they are too expensive to implement in vehicles. In this study, the climbing angle as well as the slope angle are considered in the presence of yawing motion on a sloping road, and both angles are estimated. In order to describe the combined vehicle motions when turning on a sloping road, a new three-dimensional dynamics model is proposed including the heading angle with respect to the road slope orientation. The extended Kalman filter is designed on the basis of the three-dimensional dynamics model on a sloping road, and the key parameters for the sloping road are updated using the geometry relationship. The proposed estimation method is evaluated using simulations and field tests with a hybrid electric vehicle.

Deep learning is a fast-growing field of research, in particular, for autonomous application. In this study, a deep learning network based on various sensor data is proposed for identifying the roads where the vehicle is driving. Long-Short Term Memory (LSTM) unit and ensemble learning are utilized for network design and a feature selection technique is applied such that unnecessary sensor data could be excluded without a loss of performance. Real vehicle experiments were carried out for the learning and verification of the proposed deep learning structure. The classification performance was verified through four different test roads. The proposed network shows the classification accuracy of 94.6% in the test data.

In controlling the longitudinal motion of electrified vehicles such as hybrid vehicles and PHEV (Plug-in Hybrid Electric Vehicles), the variation of the driving resistance loads (or driving loads) such as road grade and actual vehicle mass, is the most influential factor which limits the control performance. Measuring the driving load is not impossible, but it is costly since additional sensors have to be mounted on the vehicle. In this study, methods for estimating vehicle mass and road grade are designed to compensate for the driving loads. The proposed methods are verified using simulation tools and then evaluated experimentally.

This paper describes an autonomous vehicle A1 that won the Hyundai Motor Group 2012 Autonomous Vehicle Competition. The A1 was developed for autonomous on-road and off-road driving conditions without driver intervention. The autonomous driving system consists of four parts that are localization, perception, planning, and control. Localization which estimates the ego-vehicle position on the map should be first performed to autonomously drive the A1. A perception algorithm detects and recognizes the objects around the ego-vehicle are also important to prevent collisions with obstacles and road departure. A planning algorithm generates the drivable motion of the A1 based upon previous information from the localization and perception system. Subsequently, a vehicle control algorithm calculates the desired steering, acceleration and braking control commands based on the information from the planning algorithm. This paper also presents the entire system architecture that the A1 used to accomplish all the required missions in the 2012 Autonomous Vehicle Competition.

This paper presents the lane change system for collision avoidance. The proposed algorithm for the collision avoidance consists of path generation and path following. Using a calculated TTC (Time to Collision), partial braking is operated and collision avoidance path is generated considering relative distance, velocity and acceleration. Based on the collision avoidance path, desired yaw angle and yaw rate are calculated for the automated path following. The lateral controller is designed by a Lyapunov function approach using 3 D.O.F vehicle model and vehicle parameters. The required steering angle is determined from wheel velocity, longitudinal and lateral velocity in order to follow the desired yaw angle and yaw rate. This system is developed MATLAB/Simulink and its performance is evaluated using the commercial software CarSim.

Brake systems of the future, including BBW (Brake-by-Wire), are in development in various forms. In one of the proposed hydraulic BBW systems, an electric booster system replaces the pneumatic brake booster with an electric motor and a rotational-to-linear motion mechanism. This system is able to provide improved braking performance by the design of controllers with precise target pressure tracking and control robustness for better system reliability. First, a sliding mode controller is designed using the Lyapunov function approach to secure the robustness of the system against both the model uncertainty and the disturbance caused by the master cylinder and mechanical components. Next, a simulation tool is constructed to validate the electric booster system with the proposed controller. Finally, the electric booster system is implemented into an actual brake ECU and installed in a vehicle for testing under various braking conditions. The experimental results demonstrate that the proposed controller produces faster pressure build-up performance than the conventional brake system, and its tracking performance is sufficient to ensure comfortable braking.

In the future, the conventional hydraulic brake system in automobiles will be removed and replaced by an electrically operated brake system called brake-by-wire. The brake-by-wire units, such as the EMB (Electro-Mechanical Brake), provide better braking performance by directly controlling the brake motor and are environmentally friendly because they do not use hydraulic fluid. For implementation of the EMB systems, reliable and robust fault detection and diagnosis methods become increasingly important. In this study, a sensor fault diagnosis method is proposed with parity space and observer approaches to detect faults in the motor current sensor, speed (or position) sensor and clamping force sensor. The proposed method is verified through a closed-loop simulation using Matlab/Simulink, and the simulation result is compared with the HILS bench test results.

Collision detection plays a key role in collision mitigation system. The malfunction of the collision mitigation system can result in another dangerous situation or unexpected feeling to driver and passenger. To prevent this situation, the collision time, offset, and collision decision should be determined from the appropriate collision detection algorithm. This study focuses on a method to determine the time to collision (TTC) and frontal offset (FO) between the ego vehicle and the target object. The path prediction method using the ego vehicle information is proposed to improve the accuracy of TTC and FO. The path prediction method utilizes the ego vehicle motion data for better prediction performance. The proposed algorithm is developed based on laser scanner. The performance of the proposed detection algorithm is validated in simulations and experiments.

The electronic wedge brake is one of the brake-by-wire systems with a self-energizing effect. It is attractive because it can produce enough braking torque with the 12-voltage system. However, the electronic wedge brake cannot be implemented unless the self-energizing effect is effectively controlled. In this study, the electronic wedge brake is modeled into dynamic equations, and a sliding mode controller is designed based on the model. The clamping force is estimated based on the simplified electronic wedge brake model and the contact point detection algorithm is also provided. The performance of the proposed controller is verified in simulations and experiments using a prototype electronic wedge brake.

Collision detection plays a key role in collision mitigation systems. The malfunction of a collision mitigation system can result in a dangerous or unexpected situation for the driver and passengers. In order to prevent this situation, reliable collision detection algorithms are essential in terms of the collision time, the overlap and the collision decision. This study focuses on the reliable determination of the time to collision and the frontal overlap between the ego vehicle and the objects. The path prediction method using information on the ego vehicle is proposed to improve the accuracies of the time to collision and the frontal overlap calculations. The proposed algorithm is developed on the basis of a multi-layer laser scanner. The procedure of collision detection includes three steps. The first step is to select a high-risk object among multiple objects, the second step is to judge the time to collision and the frontal overlap to the high-risk object and, finally, the third step is to decide whether a crash is imminent or not. The performance of the proposed detection algorithm is validated in simulations and experiments.

EMB (Electro-Mechanical Brake) systems can provide improved braking and stability functions such as ABS, EBD, TCS, ESC, BA, ACC, etc. For the implementation of the EMB systems, reliable and robust fault detection algorithm is required. In this study, a model-based fault detection algorithm is designed based on the analytical redundancy method in order to monitor current and force sensor faults in EMB systems. A state-space model for the EMB is derived including faulty signals. The fault diagnosis algorithm is constructed using the analytical redundancy method. Observer is designed for the EMB and the fault detectability condition is examined based on the residual analysis. The performance of the proposed model-based fault detection algorithm is verified in simulations. The effectiveness of the proposed algorithm is demonstrated in various faulty cases.

Monitoring of lateral vehicle motion is very useful in many active safety applications such as yaw stability control and rollover prevention. Lateral velocity and sideslip angle are regarded as the most important motion variables. However, it is not feasible during vehicle operation to directly measure them due to the high cost of sensors, limitations to sensor technology, etc. Therefore, the monitoring of lateral vehicle motion can be realized by using either accurate monitoring models or improved estimation algorithms. In this paper, monitoring models are obtained such that they utilize not only the vehicle dynamics but the cornering stiffness and the roll motion as well. Based on these monitoring models, three monitoring systems are constructed to estimate the vehicle velocity and the sideslip angle based on the sliding mode observer, the extended Kalman filter (EKF) including Dugoff's tire model, and the scaled Kalman filter with model error compensator (SKFMEC) methods, respectively. The estimation performance of the monitoring systems is verified in field tests, and the experimental results demonstrate the effectiveness of this approach to provide accurate monitoring of lateral vehicle motion.

The longitudinal and lateral vehicle control techniques have been widely used in several active driver assistance systems. The adaptive cruise control, lane keeping assistant control, vehicle platooning and stop-and-go control are typical examples of the most important applications. In this study, a novel path planning method is proposed considering the driving environment such as road shape, ego vehicle and surrounding vehicles’ movement. The relative distance and velocity between the ego vehicle and surrounding vehicles are identified with respect to the predicted lane shape in front of the ego vehicle. Based on the identified information, the road shape and surrounding vehicles are mapped into the intensity image and the desired vector for the ego vehicle’s movement is determined by the maximum intensity density tracing method. The desired vehicle path is followed by the acceleration/deceleration control and the steering assist control, respectively. In order to evaluate the performance of the proposed system, simulations are conducted and compared with ACC systems.

For high-speed/high-accuracy position control of a gantry-moving-type linear motor, we propose a nonlinear adaptive controller including a synchronization algorithm. Linear motors are easily affected by force ripple, friction, and parameter variations because there is no mechanical transmission to reduce the effects of model uncertainties and external disturbances. Synchronization error is also caused by skew motion, model uncertainties, and force disturbance on each axis. Nonlinear effects such as friction and ripple force are estimated and compensated for. The synchronization algorithm is used to reduce the synchronous error of the two side pillars. The performance of the controller is evaluated via computer simulations.

For advanced electron beam lithography systems, a simulation tool for a two-axis nano stage is developed in this paper. The stage is equipped with piezo-actuators and flexure guides. Even if piezo-actuators are believed to be feasible for realizing nano scale motions, it is difficult to predict their characteristics due to their nonlinearities such as hysteresis and creep. In this paper, the nonlinear properties are modeled considering the input conditions. In detail, the hysteresis is described as a first order differential equation with 24 sets of the hysteresis parameters and the creep is modeled as a time-dependent logarithmic function with two sets of creep parameters. The characteristics of the flexure guides are analyzed using the finite element method and are embodied into a multi-body-dynamics simulation tool. The dynamic behavior of the simulation tool is compared with the experimental data.

Linear motors are easily affected by load disturbances, force ripples, friction, and parameter variations because there are no mechanical transmissions that can reduce the effects of model uncertainties and external disturbance. In this study, a nonlinear adaptive controller to achieve high-speed/high-accuracy position control of a two-axis linear motor is designed. The operation of this controller is based on a cross-coupling algorithm. Nonlinear effects such as friction and force ripples are estimated and compensated for. An enhanced cross-coupling algorithm is proposed for effectively improving the biaxial contour accuracy while achieving closed-loop stability. The proposed controller is evaluated by performing computer simulations.

Vehicle detection is a crucial issue for driver assistance system as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based vehicle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, a vehicle detection system using stereo vision sensors is developed. This system utilizes feature extraction, epipoplar constraint and feature matching in order to robustly detect the initial corresponding pairs. The proposed system can detect a leading vehicle in front and can estimate its position parameters such as the distance and heading angle. After the initial detection, the system executes the tracking algorithm for the vehicles in the lane. The proposed vehicle detection system is implemented on a passenger car and its performances are verified experimentally.

As active chassis controllers are becoming increasingly complex and sophisticated, the performance of these controllers is very sensitive to a vehicle's inertial parameters such as the vehicle mass and mass centre height. Also, these parameters vary with the number of passengers and with different load situations. In this paper, in order to determine the vehicle mass during arbitrary vehicle manoeuvring, an online estimation system is proposed considering available driving conditions, which includes two estimation algorithms. One is designed on the basis of the vehicle's longitudinal motion using the recursive least-squares method with the disturbance observer technique. The other is designed on the basis of the vehicle's lateral motion by estimating the lateral velocity. Then, two estimation algorithms are combined to extract the vehicle mass information using the recursive least-squares method. The performance of the proposed estimation system is demonstrated through experimental tests.

The linear motors are easily affected by load disturbance, ripple force, friction, and parameter variations because there is no mechanical transmission to reduce the effects of external disturbance. For high-speed/high-accuracy position control of a linear-motor-driven X—Y table, a non-linear adaptive controller including a cross-coupling algorithm is proposed in this paper. The non-linear effects such as friction and ripple force are estimated and compensated. The cross-coupling algorithm is adopted to reduce the contour error of the two-axial system. The performance of the proposed controller is evaluated through the computer simulations and experiments.

Obstacle detection is a crucial issue for driver assistance systems as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision with the front vehicle. The vision-based obstacle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an obstacle detection system using stereo vision sensors is developed. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the obstacles. The proposed system can detect a front obstacle, a leading vehicle and a vehicle cutting into the lane. Then, the position parameters of the obstacles and leading vehicles can be obtained. The proposed obstacle detection system is implemented on a passenger car and its performance is verified experimentally.

Lane Keeping Assistant Systems (LKAS) require the cooperative operation between drivers and active steering angle/torque controllers. An LKAS system is proposed in this study such that the desired path is generated to minimize the trajectory overshoot. Based on the reference path, an optimal controller is designed to minimize the cost function. A HIL (Hardware In the Loop) simulator is constructed to evaluate the proposed LKAS system. The single camera is mounted on the simulator and acquires the monitor images to detect lane markers. The performance of the proposed system is evaluated by HIL system using the Carsim and the Matlab Simulink.

Information on contact forces in robot manipulators is indispensable for fast and accurate force control. Instead of expensive force sensors, estimation algorithms for contact forces have been widely developed. However, it is not easy to obtain the accurate values due to uncertainties. In this article, a new robust estimator is proposed to estimate three-dimensional contact forces acting on a three-link robot manipulator. The estimator is based on the extended Kalman filter (EKF) structure combined with a Lyapunov-based adaptation law for estimating the contact force. In contrast to the conventional EKF the new estimator is designed such that it is robust to the deterministic uncertainties such as the modelling error and the sensing bias. The performance of the proposed estimator is evaluated through simulations of a robot manipulator and demonstrates robustness in estimating the contact force. The estimation results show that it can be potentially used to replace the expensive force sensors in robot applications.

This paper presents a new multi-vehicle simulator for platoon simulation. The main new feature of the developed simulator is a network structure for the real-time simulation of multiple vehicles, each with a detailed powertrain and engine model. It has a small initial delay, which is determined by the number of connected PCs, but the actual simulation is performed and displayed in real-time after this initial and one-time delay. Several longitudinal controllers, including a PID controller with gain scheduling, an adaptive controller, and a fuzzy controller, are also implemented in the simulator. Various system parameters can be modified interactively in the simulator screen, which is very useful for simulating a platoon of heterogeneous vehicles, in which vehicles with different dynamics and different longitudinal controllers may be involved. The simulator provides an excellent tool to develop vehicle longitudinal controllers and to study platoon behaviors. The developed simulator is also effective in tes...

The lane departure avoidance systems have been considered promising to assist human drivers in AVCS (Advanced Vehicle Control System). In this paper, a lane departure monitoring and control system is developed and evaluated in the hardware-in-the-loop simulations. This system consists of lane sensing, lane departure monitoring and active steering control subsystems. The road image is obtained based on a vision sensor and the lane parameters are estimated using image processing and Kalman Filter technique. The active steering controller for avoiding the lane departure is designed based on the lane departure metric. The proposed lane departure avoidance system is realized in a steering HILS (hardware-in-the-loop simulation) tool and its performance is evaluated with a driver in the loop.

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.

Track tension is closely related to the maneuverability of tracked vehicles and the durability of their tracks and suspension systems. The tension needs to be maintained at an optimum level throughout the maneuver in order to minimize the excessive load on the tracks and to prevent track peeloff from the sprocket. In this paper, a track tension control system is developed for tracked vehicles which are subject to various maneuvering tasks. It consists of track tension estimator, track tension controller, and hydraulic unit. The tension around the idler and sprocket is estimated in real time, respectively. Using the estimated track tension and considering the highly nonlinear vehicle characteristics, a fuzzy logic controller is designed in order to control the track tension in the vehicles. The performance of the proposed tension control system is verified through simulation and experimental field tests.

This paper presents a feedback-controlled, MEMS-fabricated microgyroscope. The microgyroscope is basically a high Q system, and 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. The 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.

Recently, wheel slip controllers with controlling the wheel slip directly has been studied using the brake-by-wire actuator. The wheel slip controller is able to control the braking force more accurately and can be adapted to various different vehicles more easily than the conventional ABS systems. The wheel slip controller requires the information about the tire braking force and road condition in order to achieve the control performance. In this paper, the tire braking forces are estimated considering the variation of the friction between brake pad and disk due to aging of the brake, moisture on the contact area or heating. In addition, the road friction coefficient is estimated without using tire models. The estimated performance of tire braking forces and the road friction coefficient is evaluated in simulations.

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.

Maintaining track tension in tracked vehicles minimizes the excessive load on the tracks and prevents the peal-off of tracks from the road-wheel, and adequately guarantees the stable and improved driving of the tracked vehicles. However, the track tension cannot be easily measured due to the limitation in the sensor technology, harsh environment, etc. In this study, the track tension is estimated in realtime from the measurable signals of tracked vehicles and controlled based on a fuzzy logic controller. The proposed control system is implemented on tracked vehicles and its performance is evaluated under various driving conditions.