• Proceedings of the KSME Conference
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• 2007.05a
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• pp.861-866
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• 2007

This paper presents development of a vehicle lateral and longitudinal control for autonomous driving control and test results obtained using an electric vehicle. Sliding control theory has been used to develop a vehicle speed and distance control algorithm. The longitudinal control algorithm that maintains safety and comfort of the vehicle consists of a cruise and STOP&GO control depending on traffic conditions. Desired steering angle is determined through the lateral position error and the yaw angle error based on preview optimal control. Motor control inputs have been directly derived from the sliding control law. The performance of the autonomous driving control which is integrated with a lateral and longitudinal control is investigated by computer simulations and driving test using an electric vehicle. Electric vehicle system consists of DC driving motor, an electric power steering system, main controller (Autobox)

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.123-129
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• 2014

SCC (Smart Cruise Control) and AEBS (Autonomous Emergency Braking System) are using various types of sensors data, so it is important to consider about sensor data reliability. In this paper, data from radar and vision sensor is fused by applying a Bayesian sensor fusion technique to improve the reliability of sensors data. Then, it presents a sensor fusion verification tool developed to monitor acquired sensors data and to verify sensor fusion results, efficiently. A parallel computing method was applied to reduce verification time and a series of simulation results of this method are discussed in detail.

• Journal of Auto-vehicle Safety Association
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• v.8 no.2
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• pp.6-10
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• 2016

The objective of this paper is to understand value of active safety features on the customer perspective. In this study, 30 participants who don't have experience with active safety features were recruited and asked for preference, usefulness and consideration of each active safety feature after driving evaluation by them. Through this research, the preference of active safety features were analyzed and which of active safety features were the most useful and the most considered by customer when they purchase new vehicle. As a result, adaptive cruise control and side blind zone alert were the most strongly preferred and considered features by respondents and it means that respondents wanted comfort environment while driving and seemed to value features that compensated for limited visibility. On the other hand, active safety features that warned driver without control of the vehicle was deemed generally less desirable such as lane departure warning and forward collision alert. But autonomous emergency braking was higher than the other active safety features with only warning even if they did not have experience for it while this test. They thought it will be helpful in case of front-end collision situation even they just listened description before the test.

• Journal of Auto-vehicle Safety Association
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• v.6 no.1
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• pp.27-32
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• 2014

A study assessing driver acceptance level for various active safety systems against Korean drivers has been conducted. A 2013 Cadillac ATS model vehicle was tested along southern outskirt of Seoul including local roadway and interurban highway. Active safety systems included were FCA(Forward Collision Alert), LDW(Lane Departure Warning), SBZA(Side Blind Zone Alert), FRPA(Front/Rear Park Assist), RCTA(Rear Cross Traffic Alert), ACC(Adaptive Cruise Control), and AEB(Autonomous Emergency Braking). Participants experienced the FRPA, RCTA and AEB features in a controlled parking lot with a dummy vehicle and traffic cones as target obstacles. Remaining features have been tested on the accumulated stretched of 106 km long urban and interurban roadway. Series of questionnaires corresponding to each active safety systems have been conducted. Tentative results revealed that RCTA and SBZA systems received favourable ratings compared to the other ones.

• Journal of Auto-vehicle Safety Association
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• v.11 no.3
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.198-208
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• 2022

In this paper, an autonomous driving system is implemented for the Segye AI Robot Race Competition that multiple vehicles drive simultaneously. By utilizing the ERP42-racing platform, RTK-GPS, and LiDAR sensors provided in the competition, we propose an autonomous driving system that can drive safely and quickly in a road environment with multiple vehicles. This system consists of a recognition, judgement, and control parts. In the recognition stage, vehicle localization and obstacle detection through waypoint-based LiDAR ROI were performed. In the judgement stage, target velocity setting and obstacle avoidance judgement are determined in consideration of the straight/curved section and the distance between the vehicle and the neighboring vehicle. In the control stage, adaptive cruise longitudinal velocity control based on safe distance and lateral velocity control based on pure-pursuit are performed. To overcome the limited experimental environment, simulation and partial actual experiments were conducted together to develop and verify the proposed algorithms. After that, we participated in the Segye AI Robot Race Competition and performed autonomous driving racing with verified algorithms.

• Journal of Auto-vehicle Safety Association
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• v.7 no.1
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• pp.33-39
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• 2015

This study examined the drivers' acceptance level of various active safety features with Korean drivers on Seoul urban and city roads. The test vehicle, 2013 Cadillac ATS, was equipped with FCA(Forward Collision Alert), LDW(Lane Departure Warning), SBZA(Side Blind Zone Alert), FRPA(Front/Rear Park Assist), RCTA(Rear Cross Traffic Alert), ACC(Adaptive Cruise Control), and AEB(Autonomous Emergency Braking). Participants had chances to run the tests on those systems in the parking lot accompanied by the 106km long stretch of predetermined route including local road and interurban highway in Seoul and Gyeonggi-do under normal traffic flowing environment. After the test, participants completed a series of questionnaires about the features they experienced. The results revealed that RCTA and SBZA systems received more favourable ratings compared to the other features in avoiding crashes. The respondents preferred sound alerts to haptic ones even though haptic warning methods were better in providing directional information.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.187-192
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• 2017

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.2
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• pp.92-101
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• 2000

The objective of this paper is to design the controller for longitudinal vehicle following which makes the vehicle follow the lead vehicle and keeps a safety distance without human driver operation. This paper presents a sliding mode control algorithm for the ACC system. The controller is based on three sliding surfaces. Each surface plays an individual control-deviation control, throttle control and brake control. In addition to sliding mode control, we propose some additional schemes to enhance controller performance. The first one is a gear shift-down controller which makes tractive force increase with a change of gear ratio. The other is a predictive correction method which reduces slinky effect.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1060-1065
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• 2005

An Independent Administrative Corporation Japan Agency for Marine-Earth Science and Technology (JAMSTEC) is developing light-and-small Autonomous Underwater Vehicles (AUV)$^{1)}$, named 'MR-X1' (Marine Robot Experimental 1), which can cruise, investigate and observe by itself without human's help. In this paper, we consider the motion control problem of 'MR-X1' and derive a controller. Since the dynamic property of 'MR-X1' is changed by the influence of the speed, the mathematical model of 'MR-X1' becomes the nonlinear model. In order to design a controller for 'MR-X1', we generally apply nonlinear control theories or linear control theories with some constant speed situation. If we design a controller by applying Linear Quadratic (LQ) optimal control theory, the obtained controller only compensates t e optimality at the designed speed situation, and does not compensate the stability at another speed situations. This paper proposes a controller design method using Linear Matrix Inequalities (LMIs)$^{2),3),4)}$, which can adapt the speed variation of 'MR-X1'. And examples of numerical analysis using our designed controller are shown.