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After the COVID-19 pandemic, leisure activities and cultures have undergone significant transformations. Particularly, there has been an increased demand for outdoor camping. Consequently, the need for capabilities that allow vehicles to navigate not only paved roads but also unpaved and rugged terrains has arisen. In this study, we aim to address this demand by utilizing AI to introduce a 'Stuck Probability Estimation Algorithm' for vehicles on off-road. To estimate the 'Stuck Probability' of a vehicle, a mathematical model representing vehicle behavior is essential. The behavior of off-road driving vehicles can be characterized in two main aspects: firstly, the harshness of the terrain (how uneven and rugged it is), and secondly, the extent of wheel slip affecting the vehicle's traction.

A Light Detection And Ranging (LiDAR) is now becoming an essential sensor for an autonomous vehicle. The LiDAR provides the surrounding environment information of the vehicle in the form of a point cloud. A decision-making system of the autonomous car is able to determine a safe and comfort maneuver by utilizing the detected LiDAR point cloud. The LiDAR points on the cloud are classified as dynamic or static class depending on the movement of the object being detected. If the movement class (dynamic or static) of detected points can be provided by LiDAR, the decision-making system is able to plan the appropriate motion of the autonomous vehicle according to the movement of the object. This paper proposes a real-time process to segment the motion states of LiDAR points. The basic principle of the classification algorithm is to classify the point-wise movement of a target point cloud through the other point clouds and sensor poses.

Intelligent driving assistant systems have been studied meticulously for autonomous driving. When the systems have the responsibility for driving itself, such as in an autonomous driving system, it should be aware of its’ surroundings including moving vehicles and must be able to evaluate collision risk for the ego vehicle's planned motion. However, when recognizing surrounding vehicles using a sensor, the measured information has uncertainty because of many reasons, such as noise and resolution. Many previous studies evaluated the collision risk based on the probabilistic theorem which the noise is modeled as a probability density function. However, the previous probabilistic solutions could not assess the collision risk and predict the motion of surrounding vehicles at the same time even though the motion is possible to be changed by the estimated collision risk.

Parking path planning is an essential technology for intelligent vehicles. Under a confined area, a parking path has to guide a vehicle into a parking space without collision. To realize this technology, circle-based planning algorithms have been studied. The main components of these algorithms are circles and straight lines; subsequently, the parking path of the algorithm is designed by the combination of these geometric lines. However, the circle-based algorithm was developed in an open space within an unlimited parking lot width, so a feasible path cannot always be guaranteed in a narrow parking lot. Therefore, we present a parking planning algorithm based on Turning Standard Line (TSL) that is a straight line segment. The algorithm uses the TSL lines to guide sequential quadratic Béizer curves. A set of these curves from parking start to goal position creates a continuous parking path.

In electronic vehicles (EVs) or hybrid electronic vehicles (HEVs), an inverter system has a direct-current-link capacitor (DC-link capacitor) which provides reactive power, attenuates ripple current, reduces the emission of electromagnetic interference, and suppresses voltage spikes. A film capacitor has been used as the DC-link capacitor in high level power system, but the film capacitor's performance has deteriorated over operating time. The decreasing performance of the film capacitor may cause a problem when supplying and delivering energy from the battery to the vehicle's power system. Therefore, the lifetime prediction of the film capacitor could be one of critical factors in the EVs and HEVs. For this reason, the lifetime and reliability of the film capacitor are key factors to show the stability of the vehicle inverter system. There are a lot of methods to predict the lifetime of the film capacitor.

Electro-Mechanical Brake (EMB) 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 possible faults in EMB systems. 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.

Supervisory control strategy of a hybrid electric vehicle (HEV) provides target powers and operating points of an internal combustion engine and an electric motor. To promise efficient driving of the HEV, it is needed to find the proper values of control parameters which are used in the strategy. However, it is very difficult to find the optimal values of the parameters by doing experimental tests, since there are plural parameters which have dependent relationship between each other. Furthermore variation of the test results makes it difficult to extract the effect of a specific parameter change. In this study, a model based parameter optimization method is introduced. A vehicle simulation model having the most of dynamics related to fuel consumption was developed and validated with various experimental data from real vehicles. And then, the supervisory control logic including the control parameters was connected to the vehicle model.

This paper presents a model based sensor fault detection and isolation algorithm for the vertical acceleration sensors of the Continuous Damping Control (CDC) system, installed on the sprung mass. Since sensor faults of CDC system have a critical influence on the ride performance as well as the vehicle stability, the sensor fault detection algorithm must be implemented into the overall CDC algorithm. In this paper, each vertical acceleration sensor installed on the sprung mass (two in the front corners and one in the rear) separately estimates the vertical acceleration of the center of gravity of the sprung mass. Then, the sensor fault is detected by cross-checking all three vertical acceleration estimates independently obtained by the each vertical acceleration sensor.

The paper presents a new offset compensation method of a yaw rate sensor and a lateral acceleration sensor. It is necessary to compensate the offsets of the analog sensors, such as the yaw rate sensor and the lateral acceleration sensor, to acquire accurate signals. This paper proposes two different offset compensation algorithms, the sequential compensation method and the model based compensation method. Both algorithms are combined with the algorithm map depending on the vehicle driving status. The proposed algorithm is verified by the computer simulations.

Rollover is one of the significant life threatening factors in SUVs (Sports Utility Vehicles). By applying braking or steering, active roll stability controllers help prevent rollover accidents in SUVs. The performance of these controllers is very sensitive to vehicle inertial parameters such as vehicle mass and mass center height. In this paper, a unified estimation method for vehicle mass is proposed considering available driving conditions, where three estimation algorithms are developed based on longitudinal, lateral or vertical vehicle dynamics, respectively. The first algorithm is designed using the longitudinal vehicle dynamics and the recursive least square with the disturbance observer technique for longitudinal traveling case. The second algorithm is designed using the lateral vehicle dynamics where the lateral velocity is estimated with the kinematic vehicle model via the Kalman filter.

This paper presents the Matlab/Simulink-based Software-in-the-Loop Simulation (SILS) tool which is the co-simulator for temporal and functional simulations of control systems. The temporal behavior of a control system is mainly dependent on the implemented software and hardware such as the real-time operating system, target CPU and communication protocol. In this research, the SILS components with temporal attributes are specified as tasks, task executions, real-time schedulers, and real-time networks. Methods for realizing these components in graphical block representations are investigated with Matlab/Simulink, which is the most commonly used tool for designing and simulating control algorithms in control engineering. These components are modeled in graphical blocks of Matlab/Simulink.

Increasing demands on the emission reduction of high speed direct injection (HSDI) diesel engines require more accurate control of injection parameters such as the injection timing, injection rate, and injection quantity. In order to meet injection requirements, the piezo injector, which has a piezoelectric element as an actuator, has been recently developed. Compared with solenoid-actuated injectors, piezo-actuated injectors yield greater force and give faster response times, resulting in more accurate and faster injections. In this study, a mathematical model of a piezo-actuated injector is developed. The injector model consists of three subsystems: the piezo-actuator subsystem, the mechanical subsystem, and the hydraulic subsystem. The constitutive relations of piezoelectricity are used for modeling the piezo-actuator subsystem. An estimation method of the injection timing and rate is introduced based on the proposed model.

The performance of an integrated Vehicle Stability Control (VSC) system depends on not only control logic itself, but also the performance of state estimator and control threshold. In conventional VSCs, a control threshold is designed by vehicle characteristics and is centered on average drivers. A VSC algorithm with variable control threshold has been investigated in this study. The control threshold can be determined by phase plane analysis of side slip angle and angular velocity. Vehicle side slip angle estimator has been evaluated using test data. Estimated side slip angle has been used in the determination of the control threshold. The performance of the proposed VSC algorithm has been investigated by human-in-the-loop simulation using a vehicle simulator. The simulation results show that the control threshold has to be determined with respect to the driver steering characteristics.

The purpose of this paper is to develop and experimentally validate a practical and effective technique for the automatic regulation of a hydraulic semiactive vibration absorber (SAVA) for automobiles. The work relies on a consistent hydraulic model of the actuator dynamics that includes the effects of fluid compressibility and a nonlinear viscous loss characteristic. A bistate control algorithm is developed using a Lyapunov approach that seeks to dissipate the energy of the system. The performance of the proposed semiactive damper design on a quarter car model of an automobile suspension is established experimentally on a vibrating test stand. The work provides evidence that the inexpensive hardware design makes it possible to improve the ride and handling performance.