• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.123-128
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• 2021

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

• Journal of Auto-vehicle Safety Association
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• v.16 no.2
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• pp.14-19
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• 2024

This paper describes autonomous emergency braking systems (AEB) for elderly drivers designed to consider their driving characteristics. With aging, perception-reaction time, and decision-making time increase accordingly. Without being aware of these performance degradations, however, changes in driving patterns due to increased alertness while driving lead to vehicle crashes. Therefore, it is necessary to develop an autonomous emergency braking system by incorporating the characteristics of the elderly driver. In order to enhance the driver acceptance of older people, perception-reaction time, alertness, and ride comfort need to be considered for conventional autonomous emergency braking systems (C-AEB). Proactive AEB(P-AEB) algorithm has been proposed to reflect human factor of elderly driver above. The performance of the proposed algorithm has been evaluated through MATLAB simulink simulation studies. It has been shown from the computer simulations that the proposed P-AEB algorithm enhances the driver acceptance of older people by improving ride comfort while ensuring safety of vehicle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1223-1237
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• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.

• Journal of Auto-vehicle Safety Association
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• v.10 no.2
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• pp.29-35
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• 2018

This paper presents a quantitative evaluation method and result of moving vehicle perception using automotive radar. It is also important to analyze the accuracy of the perception algorithm quantitatively as well as to accurately percept nearby moving vehicles for safe and efficient autonomous driving. In this study, accuracy of the automotive radar-based perception algorithm which is developed based on interacting multiple model (IMM) has been verified via vehicle tests on real roads. In order to obtain experimental data for quantitative evaluation, Long Range Radar (LRR) has been mounted on the front of the ego vehicle and Short Range Radar (SRR) has been mounted on the rear side of both sides. RT-range has been installed on the ego vehicle and the target vehicle to simultaneously collect reference data on the states of the two vehicles. The experimental data is acquired in various relative positions and velocity, and the accuracy of the algorithm has been analyzed according to relative position and velocity. Quantitative analysis is conducted on relative position, relative heading angle, absolute velocity, and yaw rate of each vehicle.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.20-25
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• 2022

This paper presents a development of simulation environment for validation of autonomous driving (AD) algorithm based on Robot Operating System (ROS). ROS is one of the commonly-used frameworks utilized to control autonomous vehicles. For the evaluation of AD algorithm, a 3D autonomous driving simulator has been developed based on LGSVL. Two additional sensors are implemented in the simulation vehicle. First, Lidar sensor is mounted on the ego vehicle for real-time driving environment perception. Second, GPS sensor is equipped to estimate ego vehicle's position. With the vehicle sensor configuration in the simulation, the AD algorithm can predict the local environment and determine control commands with motion planning. The simulation environment has been evaluated with lane changing and keeping scenarios. The simulation results show that the proposed 3D simulator can successfully imitate the operation of a real-world vehicle.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.20-25
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• 2022

This paper represents an adaptive Region of Interest (ROI) decision for real-time performance in an autonomous driving perception module. Since the whole automated driving system consists of numerous modules and subdivisions of module occur, it is necessary to consider the characteristics, complexity, and limitations of each module. Furthermore, Light Detection And Ranging (Lidar) sensors require a considerable amount of time. In view of these limitations, division of submodule is inevitable to represent high real-time performance for stable system. This paper proposes ROI to reduce the number of data respect to computation time. ROI is set by a road's design speed and the corresponding ROI is applied differently to each vehicle considering its speed. The simulation model is constructed by ROS, and overall data analysis is conducted by Matlab. The algorithm is validated using real-time driving data in urban environment, and the result shows that ROI provides low computational costs.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.153-170
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• 2020

With the recent development of AI and ICT, autonomous vehicles are becoming a reality, and sales of the vehicles equipped with partial autonomous driving technology are also rapidly expanding. In this situation, technology research on autonomous vehicles has been actively conducted, but research on exploring the perception of autonomous vehicles from the user's perspective is relatively insufficient. Therefore, this study categorizes autonomous vehicle users into four types - , , , and . Then, it examines the characteristics of each type. For this purpose, we applied Q-methodology, a qualitative research method, to observe self-referent subjectivity of 32 P-samples using a Q-sample which consists of 34 statements. The results of our study have significance in that they provide domestic and global automakers with strategic directions for technological development and market expansion of autonomous vehicles, and academically provide hypotheses for subsequent quantitative research.

• 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 Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.787-793
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• 2012

This paper introduces the autonomous vehicle Pharos, which participated in the 2010 Autonomous Vehicle Competition organized by Hyundai-Kia motors. PHAROS was developed for high-speed on/off-road unmanned driving avoiding diverse patterns of obstacles. For the high speed traveling up to 60 km/h, long range terrain perception, real-time path planning and high speed vehicle motion control algorithms are developed. This paper describes the major hardware and software components of our vehicle.