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Mertcan Cibooglu

Bio: Mertcan Cibooglu is an academic researcher. The author has contributed to research in topics: Deep learning & Overshoot (signal). The author has an hindex of 1, co-authored 2 publications receiving 12 citations.

Papers
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Proceedings ArticleDOI
03 Jul 2017
TL;DR: This work proposes a hybrid method to improve convergence to the path, prevent cutting corner and overshoot from the desired path, and proposes a geometric model based lateral control system for a ground vehicle.
Abstract: This paper proposes a geometric model based lateral control system for a ground vehicle. Lateral control algorithm takes the advantages of two different path tracking methods at different path geometries. Two of the well-known geometric path tracking methods, namely Pure-Pursuit method and Stanley method, are combined with a simple and easy to implement approach. Pure-Pursuit method is very good at low speeds. However, as speed increases cutting corner behavior occurs and vehicle tends to converge to path relatively slow. In contrast, Stanley method convergence to the road is very fast and there is no cutting corner behavior. However since there is no look ahead behavior in Stanley method, it tends to overshoot from the desired path for sharp turns. In this work we propose a hybrid method to improve convergence to the path, prevent cutting corner and overshoot from the desired path.

25 citations

Proceedings ArticleDOI
01 Oct 2018
TL;DR: Traditional image processing methods and deep neural networks techniques are combined and a convolutional neural network is used to recognize candidate images of traffic signs for autonomous radio controlled cars.
Abstract: Traffic signs play an important role to regulate daily traffic by providing necessary information to the drivers. For unmanned driving systems, real time and robust detection and recognition of traffic signs is one of the main concerns. Therefore, a traffic sign detection and recognition system for autonomous radio controlled cars is proposed. In this work, traditional image processing methods and deep neural networks techniques are combined. First, the online video is streamed from the car camera and the input frame region of interest is detected. Secondly, a convolutional neural network is used to recognize these candidate images. Experimental results show that the proposed system works efficiently up to %87.36 of images. However, calibration is needed for image processing techniques for various environments.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper , the authors proposed a method of optimizing an electric vehicle (EV) trajectory to reduce energy consumption by using an inverse dynamics model, servo constraints, and the Ritz method.

13 citations

Journal ArticleDOI
TL;DR: A review of the state-of-the-art trajectory tracking of autonomous vehicles is presented and both the trajectory tracking methods and the most commonly used trajectory tracking controllers of autonomous Vehicles are described.
Abstract: . Air pollution, energy consumption, and human safety issues have aroused people's concern around the world. This phenomenon could be significantly alleviated with the development of automatic driving techniques, artificial intelligence, and computer science. Autonomous vehicles can be generally modularized as environment perception, path planning, and trajectory tracking. Trajectory tracking is a fundamental part of autonomous vehicles which controls the autonomous vehicles effectively and stably to track the reference trajectory that is predetermined by the path planning module. In this paper, a review of the state-of-the-art trajectory tracking of autonomous vehicles is presented. Both the trajectory tracking methods and the most commonly used trajectory tracking controllers of autonomous vehicles, besides state-of-art research studies of these controllers, are described.

12 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a hybrid tracker based optimal path tracking system by applying the concept of an observer that selects the optimal tracker appropriately in complex road environments, and the system experimentally showed high performance with consistent driving comfort by maintaining the vehicle within the lanes accurately even in the presence of high complexity of road conditions.
Abstract: Path tracking system plays a key technology in autonomous driving. The system should be driven accurately along the lane and be careful not to cause any inconvenience to passengers. To address such tasks, this research proposes hybrid tracker based optimal path tracking system. By applying a deep learning based lane detection algorithm and a designated fast lane fitting algorithm, this research developed a lane processing algorithm that shows a match rate with actual lanes with minimal computational cost. In addition, three modified path tracking algorithms were designed using the GPS based path or the vision based path. In the driving system, a match rate for the correct ideal path does not necessarily represent driving stability. This research proposes hybrid tracker based optimal path tracking system by applying the concept of an observer that selects the optimal tracker appropriately in complex road environments. The driving stability has been studied in complex road environments such as straight road with multiple 3-way junctions, roundabouts, intersections, and tunnels. Consequently, the proposed system experimentally showed the high performance with consistent driving comfort by maintaining the vehicle within the lanes accurately even in the presence of high complexity of road conditions. Code will be available in https://github.com/DGIST-ARTIV .

7 citations

Proceedings ArticleDOI
01 Nov 2019
TL;DR: The authors aim to improve the accuracy of path following by developing a new control strategy based on the geometry of the vehicle and the path and the proper weighting between two controllers.
Abstract: In recent years vehicles with high level of automation are becoming more popular which is expected to continue in the future. Advanced driving assistance systems are increasingly taking control of the vehicle, starting with the support of the driving task. Automated or highly automated vehicles are expected to follow a planned road safely. In this paper, the authors aim to improve the accuracy of path following by developing a new control strategy. The controller includes both pure pursuit and Stanley methods, the operation of the controller is based on the geometry of the vehicle and the path; the key is the proper weighting between two controllers. The performance of the combined path following controller was measured on a demonstration vehicle.

7 citations

Proceedings ArticleDOI
01 Jan 2019
TL;DR: This paper is the first study using FGM to solve the overtaking problem, and shows several advantages of FGM comparing to the X-sin functions based overtaking approach.
Abstract: This paper proposes a solution for one of the most important components of autonomous driving: "overtaking maneuver". Follow the Gap method (FGM) is oneof the most popular obstacle avoidance algorithms and directly obtains steering angle from position of the obstacles around. This paper is the first study using FGM to solve the overtaking problem. Different from previous studies where a trajectory is planned and then a controller is designed to track it; we use FGM for motion planning and control together. This paper focuses on overtaking maneuver in a challenging environment like highway traffic where the safety and fast response are the key factors. After we adapt the FGM to overtaking problem, we compare it with an existing overtaking method X-sin functions from literature. Since X-sin functions method requires a path tracker (controller), Stanley method is combined with X-sin functions. At the end of this work, we show several advantages of FGM comparing to the X-sin functions based overtaking approach.

4 citations