• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.70-76
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• 2022

This paper investigates the effects of age, gender, and situational factors on take-over performance in automated driving. The existing automated driving systems still consider a driver as a fallback-ready user who is receptive to take-over requests. Thus, we need to understand the impact of situations and human factors on take-over performance. 34 drivers drove on a simulated track, consisting of one baseline and four event scenarios. The data, including the brake reaction time and the standard deviation of lane position, and physiological data, including the heart rate and skin conductance, were collected. The analysis was performed using repeated-measures ANOVA. The results showed that there were significant age, gender, and situational differences in the takeover performance and mental workload. Findings from this study indicated that older drivers may face risks due to their degraded driving performance, and female drivers may have a negative experience on automated driving.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.113-119
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• 2022

The structural safety of hydrogen buses is being evaluated for the successful introduction of hydrogen buses. The crash test methodology, for example, side impact test procedure is being discussed for hydrogen bus structure safety with a compressed hydrogen storage system located under the bus floor. Thus this study describes a new experiment method for side impact test with compressed hydrogen storage system independently based on finite element analysis instead of side impact test using full hydrogen bus. A side crash procedure of conceptual compressed hydrogen storage structure was investigated and impact simulations were performed. The finite element models of hydrogen bus, simplified structures, fuel tank system and side impact moving barrier were set up and simulation results reported model performance and result comparison of three different simplified models. Computational results and research discussion proposed the fundamental test framework for safety assessment of the compressed hydrogen storage system.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.27-34
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• 2022

In the development of automated driving, interest in the interior parts of vehicle is to become more significant in terms of the occupant safety and comfort. This study proposed an optimal design of front seat according to the design requirements for frame stiffness and seat comfort. For the seat comfort, the appropriate foam thickness was obtained using the structural analysis under reclined occupant loadings. While the strength and stiffness analyses were performed to evaluate the seat frame structure. Topology optimization was carried out based on the simulation results and the derived optimal model and baseline seat design was updated. The conceptual seat design for the autonomous vehicle in this study showed that the model development process is appropriate for the design parameters in both frame stiffness and seat comfort.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.14-19
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• 2022

This paper presents motion planning algorithm that yields to intervening side lane vehicles in a congested traffic flow based on vehicle to vehicle (V2V) communication. Autonomous driving in dense traffic situation requires advanced driving performance in terms of vehicle interaction and risk mitigation. One of the most important functions necessary for congested traffic autonomous driving is to predict the lane change intention of the side lane target vehicle. However, implementing this function by using only environmental sensors has limitations. In this study, V2V communication is used to overcome the limitations and determine the intention of cut-in vehicles. Lane change intention of the intervening side lane vehicle is inferred by its longitudinal speed, steering angle, and turn signal light information received by the on-board-unit (OBU). Once the yield decision is made, the subject vehicle decelerates to generate sufficient clearance for the target vehicle to enter. Validation of the algorithm was conducted with actual autonomous test vehicles.

• 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.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.45-50
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• 2022

The vehicle motion in urban environment is determined by surrounding traffic flow, which cause understanding the flow to be a factor that dominantly affects the motion planning of the vehicle. The traffic flow in this urban environment is accessed using various urban infrastructure information. This paper represents a color recognition algorithm for traffic lights to perceive traffic condition which is a main information among various urban infrastructure information. Deep learning based vision open source realizes positions of traffic lights around the host vehicle. The data are processed to input data based on whether it exists on the route of ego vehicle. The colors of traffic lights are estimated through pixel values from the camera image. The proposed algorithm is validated in intersection situations with traffic lights on the test track. The results show that the proposed algorithm guarantees precise recognition on traffic lights associated with the ego vehicle path in urban intersection scenarios.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.26-38
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• 2022

This paper proposes multiple RLS and actuator performance index-based adaptive actuator fault-tolerant control and detection algorithms for longitudinal autonomous driving. The proposed algorithm computes the desired acceleration using feedback law for longitudinal autonomous driving. When actuator fault or performance degradation exists, it is designed that the desired acceleration is adjusted with the calculated feedback gains based on multiple RLS and gradient descent method for fault-tolerant control. In order to define the performance index, the error between the desired and actual accelerations is used. The window-based weighted error standard deviation is computed with the design parameters. Fault level decision algorithm that can represent three fault levels such as normal, warning, emergency levels is proposed in this study. Performance evaluation under various driving scenarios with actuator fault was conducted based on co-simulation of Matlab/Simulink and commercial software (CarMaker).

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.90-95
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• 2022

Composite material is widely used in the automotive industries because it has excellent mechanical properties and is possible to reduce weight due to the low density. However, there is a new obstacle to meet environment regulation like Cu less or Cu free regulation for the friction material. Although it is strongly demanded, there are few research results about that unfortunately. Unless this problem is not solved properly, it is impossible to apply composite brake system to vehicle. In this paper, a new eco-friendly friction material for composite brake system is represented to respond these regulations. To do this, friction characteristics between existing low steel friction material and new eco-friendly friction material are verified to secure performances for brake system such as effect characteristic, fade characteristic and wear characteristic. And composite brake gets the equivalent or better performance compared to a low steel friction material. Finally, this result contributes to the study of major principles for the development of eco-friendly friction material in the future.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.7-13
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• 2022

This paper presents a yaw moment control based on estimated mass for manual-shift commercial vehicles. In yaw moment controller, parameter uncertantiy of vehicle mass is important because the desired yaw moment depends on vehicle parameter. However, in the case of commercial vehicle, the weight of the loaded vehicle is more than twice as much as compared to the unloaded vehicle. The proposed algorithm estimates the vehicle mass by using the longitudinal dynamic and gear shifting characteristics. The estimated mass is used to adaptively modify the vehicle parameters. In addition, this paper estimates the chamber pressure of a pneumatic brake and generates the target yaw moment through on/off valve control. MATLAB/Simulink and Trucksim were performed under sine with dwell test. The results demonstrate that the proposed algorithm improves the lateral and rollover stability.