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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.

Human drivers navigate by continuously predicting the intent of road users and interacting with them. For safe autonomous driving, research about predicting future trajectory of vehicles and motion planning based on these predictions has drawn attention in recent years. Most of these studies, however, did not take into account driver’s intentions or any interdependence with other vehicles. In order to drive safely in real complex driving situations, it is essential to plan a path based on other driver’s intentions and simultaneously to estimate the intentions of other road user with different characteristics as human drivers do. We aim to tackle the above challenges on highway merge scenario where the intention of other road users should be understood. In this study, we propose an intention aware motion planning method using finite state machine and model predictive control without any vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications.

This paper focuses on clamping force control of electronic wedge brakes without additional sensors for cost-effectiveness and system simplicity. Brake-by-wire systems can be used for enhanced, safe braking of intelligent and environmentally friendly vehicles such as gas-electric hybrid and electric vehicles. For implementation of the electronic wedge brake, the clamping force should be controlled properly even though model uncertainty and parameter variations exist due to the environment or system characteristics changes, e.g., temperature variations, pad wear, and nonlinear friction. In this paper, the electronic wedge brake is modeled to include the wedge dynamics as well as the nonlinearities such as backlash and friction in mechanical connections and clearance between the brake disk and pad. An on-line status monitoring algorithm using the simplified mathematical models is designed to estimate the mechanical system parameters.

In future automobiles, conventional hydraulic brakes can be removed and be replaced by electrically operated brakes called brake-by-wire. The brake-by-wire units such as EMB(Electro-Mechanical Brake) provide better performance in braking by directly controlling the brake motor and are environmentally friendly without hydraulic fluid. Since the brake is safety-critical, the EMB should be reliable in its lifetime and robust fault diagnosis techniques should be included. Many researches have been carried out to develop the diagnosis techniques to improve their robustness and reliability. In this study, a fault size detection method is proposed with the parity space approach for the EMB system. In order to detect and isolate sensor faults from the residual, the residual generator is constructed. The model-based fault diagnosis system is developed for the EMB sensors; current sensor, position (or speed) sensor and clamping force sensor.

A new control algorithm of a wedge brake system has been developed. The proposed control algorithm is based on the position control and current control of electronic wedge brake(EWB). The EWB control system in rear wheel has both active braking functions like ABS and ESC and convenient function such as EPB. In this paper, development of control algorithm was performed using hybrid brake system(HBS) which consists of hydraulic brake in front wheel and electronic brake in rear wheel. At first, the configuration of EWB system is explained. Next, structure of electronic control in HBS is explained. And then ABS/ESC/EPB control algorithms are shown. ABS control algorithm has wheel slip calculation, wheel error calculation, wheel slip control, position control, current control, and duty control. ESC algorithm consists of yaw error calculation, yaw moment control, wheel slip control, position control, current control, and duty control.

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.

Electro-hydraulic brake (EHB) provides improved braking and stability functions such as ABS, TCS, ESC, EBD, etc. It also removes complex mechanical parts for freedom of design, improves maintenance requirements and reduces unit weight. But, the EHB should be at least as reliable as the conventional hydraulic brake system. In this paper, the model-based fault diagnosis system is developed to monitor the brake status using the analytical redundancy method. The performance of the model-based fault diagnosis system is verified in simulations and demonstrates the effectiveness of the proposed system in various faulty cases.

Lane keeping assistant system (LKAS) is expected to reduce the driver workload with assisting the driver during driving and is regarded as a promising active safety system. For the proposed LKAS which requires cooperative driving between driver and the assistance system, a Model Based Predictive Controller (MBPC) is proposed to minimize the effect of system overshoot caused by the time delay from the vision-based lane detection system. In order to validate the proposed LKAS controller, a HIL (Hardware In the Loop) simulator is built using steering mechanism, single camera, torque motor, sensors, etc. The performance of the proposed system is demonstrated in various roadways.

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.

Wheel-slip control systems are able to control the braking force more accurately and can be adapted to different vehicles more easily than conventional ABS systems. But, in order to achieve the superior braking performance, real-time information about the vehicle status variables such as wheel slip ratio, tire force, etc is required. In this paper, a wheel slip controller based on the estimated braking force is developed for brake-by-wire systems. The proposed wheel slip control system is composed of the braking force monitor and robust slip controller. In the brake force monitor, the tire braking forces as well as the brake disk-pad friction coefficient are estimated. The robust wheel slip controller using the estimated tire braking force is designed based on the sliding mode control technique. This system determines the braking pressure as the control input and maintains the wheel slip at any given target slip.

The Milliken Moment Method (MMM) can be used to quantify the constraints imposed on vehicle stability and controllability by front and rear tire traction limitations. The main aspect of the Milliken Moment Method is the plot of vehicle's yaw moment versus lateral acceleration for given vehicle sideslip and steering angle ranges. This plot is typically called the Milliken Moment Diagram (MMD). This paper proposes a dynamic simulation approach to implementing the MMM that emulates the traditional experimental one. The approach embeds a vehicle dynamics model in a control loop that maintains a constant desired sideslip angle, and integrates the resulting controlled vehicle system model in time to generate the MMD.

Recently, wheel slip controllers with controlling the wheel slip directly have 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 braking and stability 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.