• Journal of Biosystems Engineering
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• v.21 no.4
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• pp.422-428
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• 1996

This study was conducted to developed to develop a simple battery-powered autonomous vehicle for greenhouse works. A steering method using speed difference of two independent driving motors was adopted. DC motor driving circuit, speed control circuit and controller using one-chip microcomputer were constructed. The inputs of controller are rolling of the vehicle and current speed of driving motors. Using these signals, automatic guidance system along furrow was developed. A computer simulation program by the kenematic analysis was developed to find out optimal control algorithm. The results of this study are as follows. 1. Automatic guidance system along the furrow that adopted two independent driving motors and rolling of vehicle was developed. 2. The results of simulation showed that PID control was adequate to automatic guidance system along furrow. 3. Two commercial 12V battery serially connected were able to drive the vehicle on the soil ground for five hours in continuous operation and for four hours in intermittent operation without recharging the battery. 4. The speed range was 0-0.7m/s and the rolling of vehicle could be controlled within $pm5^{\circ}$ range. 5. From a series of tests, developed vehicle was found to be a useful tool for greenhouse works.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.543-547
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• 2005

In this paper, we designed a intelligent autonomous driving robot by using Fuzzy algorithm. The object of designed robot is recognition of obstacle, avoidance of obstacle and safe arrival. We append a suspension system to auxiliary wheel for improvement in stability and movement. The designed robot can arrive at destination where is wanted to go by the old and the weak and the handicapped at indoor hospital and building.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.27-32
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• 2020

This paper presents steering system requirements to ensure the stabilized lateral control of autonomous driving vehicles. The two main objectives of a lateral controller in autonomous vehicles are maintenance of vehicle stability and tracking of the desired path. Even if the desired steering angle is immediately determined by the upper level controller, the overall controller performance is greatly influenced by the specification of steering system actuators. Since one of the major inescapable traits that affects controller performance is the time delay of the steering actuator, our work is mainly focused on finding adequate parameters of high level control algorithm to compensate these response characteristics and guarantee vehicle stability. Actual vehicle steering angle response was obtained with Electric Power Steering (EPS) actuator test subject to various longitudinal velocity. Steering input and output response analysis was performed via MATLAB system identification toolbox. The use of system identification is advantageous since the transfer function of the system is conveniently obtained compared with methods that require actual mathematical modeling of the system. Simulation results of full vehicle model suggest that the obtained tuning parameter yields reduced oscillation and lateral error compared with other cases, thus enhancing path tracking performance.

• Journal of Auto-vehicle Safety Association
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• v.15 no.2
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• pp.21-27
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• 2023

This paper presents a simulation tool for developing and evaluating automated driving systems' lane change algorithm in urban congested traffic. The behavior of surrounding vehicles was modeled based on driver driving data measured in urban congested traffic. Surrounding vehicles are divided into aggressive vehicles and non-aggressive vehicles. The degree of aggressiveness is determined according to the lateral position to initiate interaction with the vehicle in the next lane. In addition, the desired velocity and desired time gap of each vehicle are all randomly assigned. The simulation was conducted by reflecting the cognitive limitations and control performance of the autonomous vehicle. It was possible to confirm the change in the lane change performance according to the variation of the lane change decision algorithm.

• Journal of Drive and Control
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• v.19 no.4
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• pp.104-109
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• 2022

For autonomous driving research using AI, datasets collected from road environments play an important role. In other countries, various datasets such as CityScapes, A2D2, and BDD have already been released, but datasets suitable for the domestic road environment still need to be provided. This paper analyzed and verified the dataset reflecting the Korean driving environment. In order to verify the training dataset, the class imbalance was confirmed by comparing the number of pixels and instances of the dataset. A similar A2D2 dataset was trained with the same deep learning model, ConvNeXt, to compare and verify the constructed dataset. IoU was compared for the same class between two datasets with ConvNeXt and mIoU was compared. In this paper, it was confirmed that the collected dataset reflecting the driving environment of Korea is suitable for learning.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.107-114
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• 2022

In this paper, proposes a delivery robot that can be autonomous driving learning. The proposed robot is designed to be used in park-type apartments without ground parking facilities. Compared to the existing apartments with complex ground and underground routes, park-type apartments have a standardized movement path, allowing the robot to run stably, making it suitable for students' initial education environment. The delivery robot is configured to enable delivery of parcels through machine learning technology for route learning and autonomous driving using cameras and LiDAR sensors. In addition, the control MCU was designed by separating it into three parts to enable learning by level, and it was confirmed that it can be used as a delivery robot for learning through operation tests such as autonomous driving and obstacle recognition. In the future, we plan to develop it into an educational delivery robot for various delivery services by linking with the precision indoor location information recognition technology and the public technology platform of the apartment.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.35-42
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• 2022

This paper presents a stochastic model predictive control (SMPC) for stop maneuver of autonomous vehicles considering perception uncertainty of stopped vehicle. The vehicle longitudinal motion should achieve both driving comfortability and safety. The comfortable stop maneuver can be performed by mimicking acceleration profile of human driving pattern. In order to implement human-like stop motion, we propose a reference safe inter-distance and velocity model for the longitudinal control system. The SMPC is used to track the reference model which contains the position uncertainty of preceding vehicle as a chance constraint. We conduct simulation studies of deceleration scenarios against stopped vehicle in urban environment. The test results show that proposed SMPC can execute comfortable stop maneuver and guarantee safety simultaneously.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.6
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• pp.74-80
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• 2009

Path tracking of unmanned vehicle is a basis of autonomous driving and navigation. For the path tracking, it is very important to find the exact position of a vehicle. GPS is used to get the position of vehicle and a direction sensor and a velocity sensor is used to compensate the position error of GPS. To detect path lines in a road image, the bird's eye view transform is employed, which makes it easy to design a lateral control algorithm simply than from the perspective view of image. Because the driving speed of vehicle should be decreased at a curved lane and crossroads, so we suggest the speed control algorithm used GPS and image data. The control algorithm is simulated and experimented from the basis of expert driver's knowledge data. In the experiments, the results show that bird's eye view transform are good for the steering control and a speed control algorithm also shows a stability in real driving.