The growing adoption of electric vehicles (EVs) has heightened the need for reliable and efficient On-Board Chargers (OBCs). Power Factor Correction (PFC) circuits within OBCs are critical for optimizing energy conversion and minimizing power losses. However, fault diagnosis in PFC circuits remains a challenge due to the difficulty of replicating real-world fault scenarios for data collection. This study addresses these challenges by employing Generative Adversarial Networks (GANs) to augment fault signal data. By generating diverse and realistic fault signals, this approach enhances the robustness of fault classification models. The proposed CRNNWGAN model, a fusion of C-RNN-GAN and WGAN-GP, effectively captures temporal dependencies and improves the accuracy of fault diagnosis. Experimental results demonstrate the superiority of the augmented dataset in classification tasks, providing a scalable solution for improving the reliability of EV charging systems.

Different from existing MOT (Multi-Object Tracking) techniques that usually aim at improving tracking accuracy and average FPS, real-time systems such as autonomous vehicles necessitate new requirements of MOT under limited computing resources: (R1) guarantee of timely execution and (R2) high tracking accuracy. In this paper, we propose RT-MOT, a novel system design for multiple MOT tasks, which addresses R1 and R2. Focusing on multiple choices of a workload pair of detection and association, which are two main components of the tracking-by-detection approach for MOT, we tailor a measure of object confidence for RT-MOT and develop how to estimate the measure for the next frame of each MOT task. By utilizing the estimation, we make it possible to predict tracking accuracy variation according to different workload pairs to be applied to the next frame of an MOT task. Next, we develop a novel confidence-aware real-time scheduling framework, which offers an offline timing guarantee for a set of MOT tasks based on non-preemptive fixed-priority scheduling with the smallest workload pair. At run-time, the framework checks the feasibility of a priority-inversion associated with a larger workload pair, which does not compromise the timing guarantee of every task, and then chooses a feasible scenario that yields the largest tracking accuracy improvement based on the proposed prediction. Our experiment results demonstrate that RT-MOT significantly improves overall tracking accuracy by up to 1.5 ×, compared to existing popular tracking-by-detection approaches, while guaranteeing timely execution of all MOT tasks.

In this study, we propose a robust nonlinear control method using the barrier Lyapunov function (BLF) under the lateral offset error constraint for lateral control of autonomous vehicles. Furthermore, for the application of the BLF-based control method to lateral control, we propose a second-order lateral dynamics scheme based on the look-ahead distance of the vehicle. In the second-order lateral dynamics, the system functions with unknown parameters, and the external disturbances can be lumped into a disturbance term. The proposed method consists of an extended state observer (ESO) and a nonlinear controller. The ESO is designed to estimate the full state variable and disturbance, including the system modeling and external disturbance. The nonlinear controller is developed using the BLF to compensate for the disturbances and to guarantee the constraint for the lateral offset error. Consequently, the proposed method satisfies the output constraint in the presence of disturbances and modeling uncertainties as well as improves the lateral control performance using only the lateral offset error at the look-ahead distance. The lateral control performance of the proposed method is validated using CarSim and MATLAB/Simulink.

In this paper, to address the vehicle state estimation problem, we propose an approach that can overcome the practical problems associated with vehicle lane changes on highways. The vehicle state with respect to the road, which is called the lateral offset, jumps in camera vision sensors when the vehicle changes lanes. Thus, we solve this state jump problem by translating the state estimation problem into a new domain called the cylinder domain. We propose a linear parameter varying (LPV) observer to estimate the vehicle states by considering the lane width variation during a lane change. The lane information is estimated by the proposed LPV observer in the cylinder domain; thus, the estimated lane information does not jump. Consequently, the lane estimation performance can be improved during the transient response when the lateral position of the autonomous vehicle crosses over between the original and target lanes in the event of a lane change. The proposed algorithm is validated with real vehicle driving data obtained using a camera vision sensor.

In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder. This structure produces the K most likely trajectory candidates over occupancy grid map by employing the beam search technique which keeps the K locally best candidates from the decoder output. The experiments conducted on highway traffic scenarios show that the prediction accuracy of the proposed method is significantly higher than the conventional trajectory prediction techniques.

In this paper, we propose a vehicle path prediction employing yaw acceleration for adaptive cruise control (ACC). First, a path prediction method employing yaw acceleration is proposed to improve the path prediction performance of ego vehicles. In the proposed method, the vehicle path is predicted by using a clothoidal cubic polynomial curve model, and for this purpose, the curvature rate, yaw rate, and longitudinal velocity are required. The curvature rate can be mathematically obtained by differentiating the yaw rate without a camera sensor. To obtain the yaw acceleration from the noisy measured yaw rate, we derive the state-space model from the steer wheel angle for the yaw rate. Then, the KF is designed by using the state-space model to estimate the yaw acceleration. Second, a multirate longitudinal control method is proposed to improve the longitudinal control performance. The multirate KF employs the constant acceleration model in order to estimate the relative distance and velocity of the target vehicle at a faster sampling rate. Then, the desired acceleration is achieved to maintain a safe headway distance or velocity by means of the longitudinal controller. Consequently, the whole ACC system operates with a faster sampling rate so that multirate control scheme reduces ripples in both the relative longitudinal distance and the desired acceleration. The performance of the proposed method was evaluated via simulations and experiments.

In this paper, we propose a flatness based controller which tracks the desired angle of an electric power steering system with augmented observer and a state feedback controller. Unlike the torque-overlayed backstepping method in a lane-keeping system, using the known nominal model parameters results in better tracking performance then the previous method. This method can be implemented as a torque-overlay control as the backstepping controller. From numerical simulations, we observed that the flatness-based steering wheel controller outperforms the backstepping controller.

It is known that the kinematic model based motion control is robust against unknown vehicle parameters variation. Recently we reported that lane keeping system (LKS) with look-ahead distance using the kinematic vehicle lateral motion model is feasible and its performance is compatible with a dynamic vehicle lateral motion model using look-ahead distance under a limited condition in highway driving. In this paper, we developed a kinematic vehicle motion model with road friction coefficient estimation. The adaptive kinematic vehicle motion based lateral controller requires no complex tuning process. The proposed method provides improved LKS performance over the previous method. Control performance of model was validated via computational simulation results with CarSim and MATLAB/Simulink.

In this paper, we are interested in the situation of the ego vehicle tracking the previous vehicle's GPS waypoint on highway. Even if waypoints are irregular, in order to improve the tracking performance and steering performance of the GPS waypoint tracking, curve fitting and model predictive control have been applied. The improvement of the performance of the waypoint tracking was validated via computational experimental results.

In this paper, we propose kinematic vehicle lateral motion model based lane keeping system considering look-ahead distance. The state-space model based on the kinematic vehicle lateral motion model is derived and we design the lane keeping system(LKS) based on the kinematic model. The kinematic model based LKS is robust against unknown vehicle parameters variation. Furthermore, to consider look-ahead distance in the kinematic vehicle lateral motion model, we designed output measurement matrix using clothoidal constraints. Hence, we can control the vehicle at look-ahead distance like human driver. Control performance of each model was validated via computational simulation results with CarSim and MAT-LAB/Simulink, and experimental results with electric power steering system equipped with an Autobox from dSPACE.

In this paper, we propose a new vehicle trajectory prediction algorithm for adaptive cruise control (ACC). When vehicle trajectory prediction is not precise enough, it is possible for a neighboring vehicle to be detected as a target. Thus, we propose a new method using both yaw rate and curvature rate to precisely predict vehicle trajectory and to resolve an undesirable case in ACC system. The proposed method was validated via CarSim and MATLAB/Simulink. Also, we validated the proposed method via experimental results with a test vehicle on highway system for the practicality.