• Journal of Drive and Control
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• v.17 no.1
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• pp.13-20
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• 2020

Adaptive neural networks based lateral controller is presented to guarantee path following performance for vehicle lane keeping in the presence of parameter time-varying characteristics of the vehicle lateral dynamics due to the road surface condition, load distribution, tire pressure and so on. The proposed adaptive controller could compensate vehicle lateral dynamics deviated from nominal dynamics resulting from parameter variations by incorporating it with neural networks that have the ability to approximate any given nonlinear function by adjusting weighting matrices. The controller is derived by using Lyapunov-based approach, which provides adaptive update rules for weighting matrices of neural networks. To show the superiority of the presented adaptive neural networks controller, the simulation results are given while comparing with backstepping controller chosen as the baseline controller. According to the simulation results, it is shown that the proposed controller can effectively keep the vehicle tracking the pre-given trajectory in high velocity and curvature with much accuracy under parameter variations.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.3
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• pp.245-253
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• 2002

A driver model with nervous neuromuscular system was developed to steer a vehicle along the prescribed path during handling simulations. A 3-dimensional vehicle model with 10 DOF and 3 DOF steering handle are used to perform a computer simulation. PID and fuzzy controller are used to perform single and double lane change, and their tracking abilities were compared. The effects of time delay and preview distance are also investigated, and it is demonstrated that the driver model developed can be an aid far objective evaluation of vehicle handling simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.639-645
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• 2010

This paper presents the sensor fusion algorithm that recognizes a primary vehicle by fusing radar and monocular vision data. In general, most of commercial radars may lose tracking of the primary vehicle, i.e., the closest preceding vehicle in the same lane, when it stops or goes with other preceding vehicles in the adjacent lane with similar velocity and range. In order to improve the performance degradation of radar, vehicle detection information from vision sensor and path prediction predicted by ego vehicle sensors will be combined for target classification. Then, the target classification will work with probabilistic association filters to track a primary vehicle. Finally the performance of the proposed sensor fusion algorithm is validated using field test data on highway.

• Journal of Drive and Control
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• v.17 no.4
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• pp.72-79
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• 2020

To ensure the safety of Advanced Driver Assistance Systems (ADAS) or autonomous vehicles, it is important to recognize the vehicle position, and specifically, the increased accuracy of the lateral position of the vehicle is required. In recent years, the quality of GPS signals has improved a lot and the price has decreased significantly, but extreme urban environments such as tunnels still pose a critical challenge. In this study, we proposed stable and precise lane recognition and tracking methods to solve these two issues via fusion of AVM images and vehicle sensor data using an extended Kalman filter. In addition, the vehicle's lateral position recognition and the abnormal state of RTK GPS were determined using this approach. The proposed method was validated via actual vehicle experiments in urban areas reporting multipath and signal disconnections.

• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.130-139
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• 2011

In this paper, vehicle tracking algorithm using hue transformation in HIS color model is proposed. the proposed algorithm is installed on the road of the two horizontal virtual data sampling lines. The difference images are detected between the frame and the frame, respectively and also detected in the vehicle by using the hue color distribution to determine identity and lane changes. To examine the effectiveness of proposed algorithm, identification and velocity measurement for driving vehicle are evaluated. this evaluated results is shown by hue data of vehicle passing of two virtual data sample lines, and the velocity measurement for driving vehicle is less than 0.4% comparing with existing vehicle speed meter system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2355-2373
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• 2011

Vision-based vehicle detector systems are becoming increasingly important in ITS applications. Real-time operation, robustness, precision, accurate estimation of traffic parameters, and ease of setup are important features to be considered in developing such systems. Further, accurate vehicle detection is difficult in varied complex traffic environments. These environments include changes in weather as well as challenging traffic conditions, such as shadow effects and jams. To meet real-time requirements, the proposed system first applies a color background to extract moving objects, which are then tracked by considering their relative distances and directions. To achieve robustness and precision, the color background is regularly updated by the proposed algorithm to overcome luminance variations. This paper also proposes a scheme of feedback compensation to resolve background convergence errors, which occur when vehicles temporarily park on the roadside while the background image is being converged. Next, vehicle occlusion is resolved using the proposed prior split approach and through reasoning for rule-based tracking. This approach can automatically detect straight lanes. Following this step, trajectories are applied to derive traffic parameters; finally, to facilitate easy setup, we propose a means to automate the setting of the system parameters. Experimental results show that the system can operate well under various complex traffic conditions in real time.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.6
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• pp.730-736
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• 2016

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.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.3
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• pp.233-241
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• 2014

This paper contributes to development of a new method for detecting rear-side vehicles and estimating the positions for blind spot region or providing the lane change information by using vision systems. Because the real image acquired during car driving has a lot of information including the target vehicle and background image as well as the noises such as lighting and shading, it is hard to extract only the target vehicle against the background image with satisfied robustness. In this paper, the target vehicle has been detected by repetitive image processing such as sobel and morphological operations and a Kalman filter has been also designed to cancel the background image and prevent the misreading of the target image. The proposed method can get faster image processing and more robustness rather than the previous researches. Various experiments were performed on the highway driving situations to evaluate the performance of the proposed algorithm.

• Journal of Power System Engineering
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• v.17 no.3
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• pp.72-81
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• 2013

Many articles have been published about a 2-degree of freedom model that includes the lateral and yaw motions for controller synthesis in intelligent transport system applications. In this paper, a 3-degree of freedom linear model that includes the roll motion is developed to design a robust steering controller for lane following maneuvers using ${\mu}$-synthesis. This linear perturbed system includes a set of parametric uncertainties in cornering stiffness and unmodelled dynamics in steering actuators. The state-space model with parametric uncertainties is represented in linear fractional transformation form. Design purpose can be obtained by properly choosing the frequency dependent weighting functions. The objective of this study is to keep the tracking error and steering input energy small in the presence of variations of the cornering stiffness coefficients. Furthermore, good ride quality has to be achieved against these uncertainties. Frequency-domain analyses and time-domain numerical simulations are carried out in order to evaluate these performance specifications of a given vehicle system. Finally, the simulation results indicate that the proposed robust controller achieves good performance over a wide range of uncertainty for the given maneuvers.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.689-698
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• 2021

In this paper, lane recognition, steering control and speed control algorithms were developed using Bluetooth wireless communication and image processing techniques. Instead of recognizing road traffic signals based on image processing techniques, a methodology for recognizing the permissible road speed by receiving speed codes from electronic traffic signals using Bluetooth wireless communication was developed. In addition, a steering control algorithm based on PWM control that tracks the lanes using the Canny algorithm and Hough transform was developed. A vehicle prototype and a driving test track were developed to prove the accuracy of the developed algorithm. Raspberry Pi and Arduino were applied as main control devices for steering control and speed control, respectively. Also, Python and OpenCV were used as implementation languages. The effectiveness of the proposed methodology was confirmed by demonstrating effectiveness in the lane tracking and driving control evaluation experiments using a vehicle prototypes and a test track.