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While computer vision and computing technology advances have facilitated advanced driver assistance applications, systems with multi-task design remain highly demanding to operate at high speed on resource-constrained devices. Our study addresses this challenge by proposing a real-time driver assistance solution specifically developed for a single Jetson AGX Xavier embedded device. It simultaneously performs lane detection, traffic object detection and recognition, and rule-based scene analysis. To achieve high throughput (up to 43 frames per second) without reliance on additional hardware or cloud server, the system exploits Jetson device’s specialized AI accelerator and employs various optimization techniques: multithreaded programming, the TensorRT framework, and post-training quantization. The modular design integrates state-of-the-art task-specific methods and ensures adaptation to diverse traffic scenarios across countries as well as future hardware and solutions. Experimental results using a Korean dashcam traffic dataset validated the system’s effectiveness and practicality.

To provide reliable position control for a permanent-magnet synchronous motor (PMSM) under conditions of lumped disturbances such as external load torque fluctuation and system parameter variation, an adaptive backstepping sliding-mode control (ABSMC) with a nonlinear disturbance observer (NDO) is proposed. An ABSMC is a non-cascade technique that employs a position-current single-loop control structure rather than a conventional cascade control structure for vector control of the PMSM. This method uses Lyapunov theory to design a control law that ensures the motor position reaches a desired value in a finite period of time and consequently achieves a rapid transient response. The proposed ABSMC incorporates adaptive convergence gain to avoid a large overshoot under point-to-point position command. The NDO with nonlinear observer gain is proposed to estimate unknown disturbances and provide feed-forward compensation for the ABSMC, improving robustness and reducing steady-state position error. The proposed methods are applied to an industrial motor drive to confirm their validity in real-world operating environments.

A robust mechanical parameter estimation together with adaptive speed control algorithms for permanent magnet synchronous motor (PMSM) drive systems based on the dual adaptive sliding-mode method is proposed in this paper. First, a robust adaptive sliding mode mechanical observer (RASM) and mechanical parameter identification (MPI) are proposed to eliminate system parameter errors in the first rotation. Based on RASM-MPI, the correct mechanical parameters are estimated from the system disturbance value. Then, an adaptive sliding-mode speed control (ASMSC) is applied to reduce the chattering inherent in the control signal and improve the convergence time to reach the sliding-mode surface. From the second rotation, the RASM identifies unknown external load torque and provides feed-forward compensation for the ASMSC to improve the speed control of the PMSM. All the proposed methods are applied to an industrial motor drive. The experimental results verified that the proposed scheme can estimate system parameters accurately within a short time. In addition, the speed controller can run stably, and with less error.

Permanent Magnet Synchronous Motors (PMSMs) are employed in various high-precision industrial applications. However, the torque ripple caused by nonideal factors, such as cogging torque, flux harmonics, and unbalanced stator phase currents diminishes motor performance. Therefore, torque ripple minimization is an important key in designing high-performance controllers for PMSMs. In this paper, an explicit model predictive speed control (EMPSC) is proposed as an advanced strategy for torque ripple minimization. First, the torque ripple is modeled as a periodic disturbance in the speed model. Subsequently, a Lyapunov-based periodic disturbance observer (PDOB) is designed to fast and accurately estimate the torque ripple. The EMPSC updates the estimated disturbance into the prediction model and minimizes the cost function to obtain the optimal control signal. This control signal effectively mitigates torque ripple while enhancing dynamic response performance. Furthermore, this paper introduces an explicit process aimed at significantly reducing the complexity and computational effort of the entire control scheme. Lastly, the simulation and experimental results are presented to demonstrate the effectiveness of the proposed method.

This paper proposes a new online auto-tuning method to improve the accuracy and reduce the tuning time of permanent magnet synchronous motor (PMSM) drives. Under varying loads, the ability to tune the controllers of PMSM drives using optimal tuning time is crucial. However, direct tuning of controller parameters using estimated parameters or conventional particle swarm optimization (PSO) methods do not satisfy the performance criteria. To solve this problem, the new method combining mechanical parameter estimation (MPE) and multi-layer particle swarm optimization (MLPSO) with K-means clustering (KMC) and an adaptive learning strategy (ALS) is proposed. First, the combination of an MPE method with a lookup table (LUT) for initial parameter selection is introduced to reduce the iteration time. Then, the MLPSO-KMCALS method is proposed as an improvement over the conventional PSO method by increasing the number of layers, grouping the swarm into several subswarms, and using the ALS for each particle to increase the population diversity and optimize the controller parameters within the shortest possible amount of time. Finally, a disturbance load torque observer is applied to compensate for the effect of external disturbances after tuning. The effectiveness of the proposed method is validated through experiments conducted under practical conditions.

This study describes a hands-on in-vehicle network (IVN) training course for automotive engineers, particularly for learners with different prior knowledge. We collected essential content to educate those with different backgrounds on IVN-related information, which is essential in practice. The course aims within a short period to effectively educate engineers working in the industry on IVN systems, and explains how to employ embedded systems and graphical user interfaces (GUIs) for this purpose. At the end of the training, the level of knowledge before and after training was compared to evaluate the training results, and engineers’ responses were recorded.

Advanced Driver Assistance Systems (ADAS) are a collection of intelligent solutions integrated into next-generation vehicles to assist in safe driving. When building ADAS systems, the main goals are that they are stable, flexible, easy to maintain, and allow for error tracing. If a driving assistance algorithm is designed to be implemented on one machine or in one model, there is a potential disadvantage that if one component fails, then the entire system would stop. We work on modularizing the ADAS system to be flexible to accommodate any changes or improvements based on up-to-date requirements. Using advanced current edge (or network) devices, we propose a Detection-based Driving Assistance algorithm, which can collaborate or integrate with an existing system in a vehicle. The core of any process is to ensure that the system has a predictable level of functionality and that any misbehavior can be easily traced to the root cause. The proposed system shows fast, real-time performance on edge devices with limited computing power.

This paper proposes a robot calibration method that uses an extended Kalman filter (EKF) and a neural network based on Levenberg–Marquardt combined accelerated particle swarm optimization (LMAPSO) to improve the accuracy of the robot’s absolute position. After the EKF optimizes all geometric parameters, the robot position still contains non-geometric errors due to joint clearance, gear backlash, and link deflection that are impossible to model. Therefore, an artificial neural network model (ANN) is designed to compensate for these un-modeled errors. The Levenberg–Marquardt combined accelerated particle swarm optimization (LMAPSO) provides a robust optimization search algorithm to optimize the weight and bias of the neural network based on the training set. An experiment on a five-bar parallel robot shows that geometric and non-geometric calibration reduced the maximum absolute position error from (1.548 to 0.045) mm. The experimental results demonstrate the proposed calibration method’s effectiveness with the robot’s absolute position accuracy improving by 98%.

This paper combines the extended Kalman filter (EKF), dual quantum-behaved particle swarm optimization (DQPSO), and adaptive neuro-fuzzy inference system (ANFIS) to propose a novel robot calibration method. Robot precision is influenced by kinematic and non-kinematic error sources. The EKF algorithm is robust for the nonlinear system with Gaussian noise to identify kinematic parameter errors for kinematic calibration. However, if inappropriate covariance matrices are selected, the EKF algorithm may converge to an incorrect solution. To increase the effectiveness of the EKF algorithm, we propose a DQPSO algorithm that consists of the QPSO-1 and QPSO-2 algorithms. The QPSO-1 algorithm adapts the covariance matrices of measurement noise and process noise, while the QPSO-2 algorithm optimizes the kinematic parameter errors estimated by the EKF algorithm. In addition, the used ANFIS predicts and compensates the non-kinematic error for non-kinematic calibration. Experiments have been performed on a five-bar parallel robot to confirm the effectiveness of the proposed method. The experimental results demonstrate that the proposed method significantly improves the positional accuracy, and is better than the previous methods.

This paper proposes a linear compensator algorithm to improve the performance of an absolute multipolar magnetic encoder (AMPME). An AMPME is an absolute magnetic rotary encoder that uses a multipolar magnet (MPM) to increase the resolution. The resolution can be dramatically increased in proportion to the number of poles in the MPM. However, various hardware problems that occur during the encoder manufacturing process degrade the AMPME performance. Also, harmonic components occur in the raw data due to various problems, such as the resistance error of the analog circuit, magnetic field overlap between the magnets, position error between the sensor and magnet, and pole-pitch difference. In particular, during the magnetization process of the MPM, the pole-pitch difference becomes a problem when the sizes of each pole are not uniform. This problem causes harmonic components that reduce the absolute position accuracy. To solve these problems, this paper proposes a linear compensation method. The proposed linear compensator consists of two parts. The first part is the enhanced ratiometric linearization for phase calculation and calibration. The second is the phase compensator for removing the phase difference via the pole-pitch difference of the MPM. The linear compensator improves various parameters by precomputing the offset, amplitude, and phase corrections. After compensating for sinusoidal signals, the linear compensator applies appropriate parameters at the appropriate times. This method is faster, easier to set up, and more accurate than the conventional method. Furthermore, this method is experimentally verified against the existing harmonic rejection method. Experimental results are provided to verify the effectiveness of the proposed method.

As multiple functions have been added to single-core-based engine electronic control units (ECUs) in vehicles, automotive researchers and manufacturers have actively studied multi-core architecture for engine ECUs. Multi-core architecture can provide load balancing and parallelism that can meet the requirements of international organization standard (ISO) 26262. However, since real-world engine ECUs have the most complex automotive open system architecture (AUTOSAR)-based control logic and datasets among automotive ECUs, developing multi-core-based engine ECUs is a substantial amount of work. Thus, automotive researchers and manufacturers will need new methodologies for multi-core-based engine ECUs. In this paper, we focus on designing a multi-core migration methodology and applying it to a real-world AUTOSAR-based engine ECU from HYUNDAI. We verify its practicability and enhanced performance. In conclusion, through connection with other automotive domain ECUs, it is demonstrated that a multi-core engine ECU using our migration technology can be applied in real-world automotive vehicles, leading to a significant improvement in performance.

Rectification is a core programming process that involves multiple views. In this study, we focus on uncalibrated cases that neglect intrinsic and extrinsic camera information. Existing uncalibrated rectification methods use feature-matching techniques to form relationships between different views and use those features to estimate optimized homography matrices. However, outliers are inevitable in feature matching. Using these outliers in a rectification process produces vertical disparity errors and unwanted geometric distortion. To tackle the problem, we propose a novel method that can learn from the rectification results, re-select the matching pairs, and find superior solutions. The proposed method introduces a novel workflow for uncalibrated rectification that incorporates three cores: field of view (FoV) neutralization, rectification, and feature matching re-selection (FMR). While the FoV neutralization module handles FoV differences among views, a combination of the rectification module and FMR module results in the optimal homography matrices. The rectification module takes neutral correspondences and estimates the optimized rectified matrices. Applying the results from the rectification module, the FMR module optimizes the correspondences and return them to the rectification module. For the rectification module, we apply adaptive geometric constraints and our updated optimization strategy to secure satisfied vertical disparity errors while maintaining low distortion levels. Multidisciplinary experiments are performed to analyze the capabilities of the proposed method. We combine existing datasets with extra samples, including various outdoor environments, to gauge the performance better. Compared with existing methods, the proposed method produces fewer rectification errors and keeps the rectified images under low distortion rates.

This paper proposes a stable adaptive gradient descent for harmonic-disturbance rejection as a tool for grid-power signal processing, magnetic rotary encoders, and permanent-magnet synchronous motors. The method can be widely applied to increase the accuracy of phase or position estimation in various systems in which harmonic disturbances exist. The proposed technique is based on learning the harmonic amplitudes by means of gradient descent on the feedback phase error. To compensate for the disadvantages of existing gradient-descent methods, the derivative expansion is fully developed with system transfer function integration for stable weighting update. In addition, an adaptive learning rate is also proposed based on discrete Lyapunov standard to achieve stability, and theoretical analysis is discussed. The performance of the method is evaluated by numerical simulation in MATLAB with various scenarios, and by the experiment with the rotary magnetic encoders. The results show that the proposed method achieves stability and high performance in harmonic rejection.

A network-based control system includes many connected devices, and time synchronization plays an important role in the unified operation of those systems. A clock servo needs a guarantee time offset and a clock rate to minimize the time difference between devices. This paper proposes an adaptive clock servo to improve time synchronization. This proposed algorithm uses a fuzzy-based proportional-integral clock servo (fuzzy-PI) based on IEEE 1588 to reduce the frequency of clock compensation. This method adapts the bandwidth and enhances noise reduction, improving both the time offset and rate difference between the slave and master. The node time can synchronize with the master time after just one cycle of synchronization. Experiments validated the effectiveness of the algorithm and demonstrated that the slave can track the master with the mean and standard deviation of the time offset are 0.432ns and 4.402ns. A cycle time of one second is used to ensure a low-bandwidth network. With these results, the number of nodes over a real-time network can be increased.