• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.173-188
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• 2024

With the advancement of autonomous vehicle (AV) technology, autonomous driving on real roads has become feasible. However, there are challenges in achieving complete autonomy due to perceptual blind areas, which occur when the AV's sensory range or capabilities are limited or impaired by surrounding objects or environmental factors. This study aims to analyze AV accident patterns and safety issues of perceptual blind area that may occur in urban areas, with the goal of developing test scenarios for Level 4+ autonomous driving. It utilized AV accident data from the California Department of Motor Vehicles (DMV) to compare accident patterns and characteristics between AVs and conventional vehicles based on activation status of autonomous mode. It also categorized AV disengagement data to identify types and real-world cases of disengagements caused by perceptual blind areas. The analysis revealed that AVs exhibit different accident types due to their safe driving maneuvers, and three types of perceptual blind area scenarios were identified. The findings of this study serve as crucial foundational data for developing Level 4+ autonomous driving test scenarios, enabling the design of efficient strategies to mitigate perceptual blind areas in various scenarios. This, in turn, is expected to contribute to the effective evaluation and enhancement of AV driving safety on real roads.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.3
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• pp.387-392
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• 2014

Now a days, there are various application functions to transmit from vehicles to the Internet and vice versa. And the communication can be operated through a roadside infrastructure including with possible use of routing protocols. Specifically, autonomous vehicles for remote driving and monitoring requires transmitting of high depth of multimedia such as video. Especially in a populated urban area, an efficient network is vital because of handling a great amount of the data. Therefore, in this paper, efficient network topology for a crossroad in urban area is suggested by performance evaluation of vehicular networks using a wireless LAN and a routing protocol. For the performance evaluation, various vehicular network topologies are designed and simulated in OPNet simulator.

• Journal of Auto-vehicle Safety Association
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• v.9 no.2
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• pp.6-12
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• 2017

An autonomous driving system based on a precise digital map is developed. The system is implemented to the Hyundai's Tucsan fuel cell car, which has a camera, smart cruise control (SCC) and Blind spot detection (BSD) radars, 4-Layer LiDARs, and a standard GPS module. The precise digital map has various information such as lanes, speed bumps, crosswalks and land marks, etc. They can be distinguished as lane-level. The system fuses sensed data around the vehicle for localization and estimates the vehicle's location in the precise map. Objects around the vehicle are detected by the sensor fusion system. Collision threat assessment is performed by detecting dangerous vehicles on the precise map. When an obstacle is on the driving path, the system estimates time to collision and slow down the speed. The vehicle has driven autonomously in the Hyundai-Kia Namyang Research Center.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.177-193
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• 2022

In the near future, it is expected that the use of autonomous mobility-on-demand services will increase. Considering its complicated service components, including vehicle convenience, driving and matching speed, and platform convenience, the priorities of them will need to be determined for a successful establishment. In this context, this study examined the importance rank of each service component through an online survey of potential users of autonomous mobility-on-demand services. As a result of the AHP (Analytic Hierarchy Process) with respect to the upper-level components, driving and matching speed component is selected as most important, followed by platform convenience and vehicle convenience. Mean rank analysis with respect to lower-level components showed that the in-vehicle congestion level of vehicle convenience, waiting time of driving and matching speed, and pre-booking availability of platform convenience each ranked first. Additional analysis regarding each group was conducted to establish a group-specific strategy. As a result, it would be better to focus on a vehicle than a mobile platform when designing services for the region with a high proportion of the older. Moreover, it is recommended to speed up the driving and matching speeds more than the current public transport, alleviate in-vehicle congestion, and enable the users to book the schedule in advance.

• Journal of Auto-vehicle Safety Association
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• v.9 no.3
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• pp.24-30
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• 2017

This paper presents the development of laser scanner based static obstacle detection algorithm for vehicle localization on lane lost section. On urban autonomous driving, vehicle localization is based on lane information, GPS and digital map is required to ensure. However, in actual urban roads, the lane data may not come in due to traffic jams, intersections, weather conditions, faint lanes and so on. For lane lost section, lane based localization is limited or impossible. The proposed algorithm is designed to determine the lane existence by using reliability of front vision data and can be utilized on lane lost section. For the localization, the laser scanner is used to distinguish the static object through estimation and fusion process based on the speed information on radar data. Then, the laser scanner data are clustered to determine if the object is a static obstacle such as a fence, pole, curb and traffic light. The road boundary is extracted and localization is performed to determine the location of the ego vehicle by comparing with digital map by detection algorithm. It is shown that the localization using the proposed algorithm can contribute effectively to safe autonomous driving.

• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.106-116
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• 2024

Ensuring robust 3D object detection is a core challenge for autonomous driving systems operating in urban environments. To tackle this issue, various 3D representation, including point cloud, voxels, and pillars, have been widely adopted, making use of LiDAR, Camera, and Radar sensors. These representations improved 3D object detection performance, but real-world urban scenarios with unexpected situations can still lead to numerous false positives, posing a challenge for robust 3D models. This paper presents a post-processing algorithm that dynamically adjusts object detection thresholds based on the distance from the ego-vehicle. While conventional perception algorithms typically employ a single threshold in post-processing, 3D models perform well in detecting nearby objects but may exhibit suboptimal performance for distant ones. The proposed algorithm tackles this issue by employing adaptive thresholds based on the distance from the ego-vehicle, minimizing false negatives and reducing false positives in the 3D model. The results show performance enhancements in the 3D model across a range of scenarios, encompassing not only typical urban road conditions but also scenarios involving adverse weather conditions.

• Journal of Platform Technology
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• v.11 no.3
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• pp.24-31
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• 2023

Recently, interest in UAM (Urban Air Mobility, UAM), which can take off and land vertically in the operation of urban air transportation systems, has been increasing. Therefore, various start-up companies are developing related technologies as eco-friendly future transportation with advanced technology. However, studies on ways to increase safety in the operation of UAM are still insignificant. In particular, efforts are more urgent to improve the safety of risks generated in the process of attempting to land in the city center by UAM equipped with autonomous driving. Accordingly, this study proposes a plan to safely land by avoiding dangerous region that interfere when autonomous UAM attempts to land in the city center. To this end, first, the latitude and longitude coordinate values of dangerous objects observed by the sense of the UAM are calculated. Based on this, we proposed to convert the coordinates of the distorted planar image from the 3D image to latitude and longitude and then use the calculated latitude and longitude to compare the pre-learned feature descriptor with the HOG (Histogram of Oriented Gradients, HOG) feature descriptor to extract the dangerous Region. Although the dangerous region could not be completely extracted, generally satisfactory results were obtained. Accordingly, the proposed research method reduces the enormous cost of selecting a take-off and landing site for UAM equipped with autonomous driving technology and contribute to basic measures to reduce risk increase safety when attempting to land in complex environments such as urban areas.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.39-51
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• 2021

Recently, there has been much interest in practical use of standardized HD map that can effectively define roads, lanes, junctions, road signs, and road facilities in autonomous driving. Various kinds of de jure or de facto standards such as ISO 22726-1, ISO 14296, HERE HD Live map, NDS open lane model, OpenDRIVE, and NGII HD map are currently being used. However, there are lots of differences in data modeling among these standards, it makes difficult to use them together in autonomous driving. Therefore, we propose a data model conversion method to enable an efficient use of various kinds of HD map standards in autonomous driving in this study. Specifically, we propose a conversion method between the NGII HD map model, which is easily accessible in the country, and the OpenDRIVE model, which is commonly used in the autonomous driving industry. The proposed method consists of simple conversion of NGII HD map layers into OpenDRIVE objects, new OpenDRIVE objects creation corresponding to NGII HD map layers, and linear transformation of NGII HD map layers for OpenDRIVE objects creation. Finally, we converted some test data of NGII HD map into OpenDRIVE objects, and checked the conversion results through Carla simulator. We expect that the proposed method will greatly contribute to improving the use of NGII HD map in autonomous driving.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.73-81
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• 2023

Globalized cities are currently showing changes due to autonomous driving (AD). It is also maximizing globalization connections in cities where autonomous mobility is as complex as AD. The purpose of this study is to reveal that cities that realize AD and mobility will grow into globalized cities. Several cities, including New York and Shanghai, have attempted and are in progress, but failed cities are increasing. Although the technology of AD and the trust of citizens are prioritized, the city that has built the city's infrastructure is expected to be a city that has succeeded in AD. This is because commercialized cities or AVs will become hubs for mobility globalization, excluding rapid climate change or AV companies, and empirical analysis has been conducted that if AVs fail in metropolitan New York due to urban complexity (population density), urban economy size (GRDP), patents, number of consumers, infrastructure public EV chargers, and road quality. It examines whether the realization of AD by region and country affects overall national innovation. As a result, even if AV succeeds in large cities such as New York, Seoul, which has a higher population density (complexity), has a negative meaning, and a more similar Tokyo has a positive meaning. It can be seen that regional research on AV should also be prioritized in large cities such as Shanghai. This means that in order for AV to be realized in each city, the construction of AI infrastructure data must be actively changed to establish globalization of cities for economic growth as autonomous mobility.