• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.472-484
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• 2012

Raised or plain block is widely used for the pedestrian's safe passage. The insincere construction, insufficient maintenance and obstacle overlaid on the pavement cause pedestrian's accidents. This paper proposes a method to detect brick path by analyzing the shape pattern and texture feature of brick located in visible distance for a safe passage. A brick appears to a regular type because of its specific shape which repeats with its sized gap and its type varies according to the surrounding environment or use. This paper shows a method which extracts the shape pattern by analyzing single surface polygon and its frequency appearing in road area. The shape pattern is used to detect similar shape regions. Some regions are not detected because extraneous substances or chopped bricks distort the original shape. This problem can be solved by analyzing the texture feature vector. The analyzed vector of the previously detected regions yields the Gaussian distribution. This value in each undetected region is computed and checked whether it's satisfied with Gaussian distribution or not. The satisfied region is detected as the brick path. The experiment was performed with the various type's bricks to recognize so that the results showed as accurate as 95.9% in average.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.6
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• pp.323-334
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• 2014

In this paper, we proposed indoor positioning system with improved accuracy. The proposed indoor location measurement system is based pedestrian location measurement method that use the embedded sensor of smartphone. So, we do not need wireless external resources, such as GPS or WiFi signals. The conventional methods measure indoor location by generating a movement route of pedestrian by step and direction recognition. In this paper, to correct the direction sensor error, we use the common feature of the normal indoor floor map that the indoor path is lattice-structured. And we quantize moving directions depending on the direction of indoor path. In addition, we propose moving direction measuring method using geomagnetic sensor and gyro sensor to improve the accuracy. Also, the proposed step detection method uses angle and accelerometer sensors. The proposed step detection method is not affected by the posture of the smartphone. Direction errors caused by direction sensor error is corrected due to proposed moving direction measuring method. The proposed location error correction method corrects location error caused by step detection error without the need for external wireless signal resources.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.53-59
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• 2021

According to the Korean Traffic Accident Analysis System (TAAS), more than 200,000 traffic accidents occur every year. Also, the statistics including auto insurance companies data show 1.3 million traffic accidents. In the case of TAAS, the types of traffic accidents are simply divided into four; frontal collision, side collision, rear collision, and rollover. However, more detailed information is needed to assess for advanced driver assist systems at intersections. For example, directional information is needed, such as whether the vehicle in the car accident way in a straight or a left turn, etc. This study intends to redefine the type of accident with the more clear driving direction and path by referring to the Negligence standards used in automobile insurance accidents. The standards largely divide five categories of car-to-car/motorcycle /pedestrian/cyclist, and highway, and the each category is classified into dozens of types by status of the traffic signal, conflict situations. In order to present more various accident types for auto insurance accidents, the standards are reclassified driving direction and path of vehicles from crash situations. In results, the car-to-car accidents are classified into 33 accident types, car-to-pedestrian accidents have 19 accident types, car-to-motorcycle accidents have 38 accident types, and car-to-cyclist accidents are derived into 26 types.

• Journal of Drive and Control
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• v.19 no.4
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• pp.110-116
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• 2022

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.61-68
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• 2022

In this paper, we propose a system to predict the GPS trajectory of a pedestrian based on a deep learning model. Pedestrian trajectory prediction is a study that can prevent pedestrian danger and collision situations through notifications, and has an impact on business such as various marketing. In addition, it can be used not only for pedestrians but also for path prediction of unmanned transportation, which is receiving a lot of spotlight. Among various trajectory prediction methods, this paper is a study of trajectory prediction using GPS data. It is a deep learning model-based study that predicts the next route by learning the GPS trajectory of pedestrians, which is time series data. In this paper, we presented a data set construction method that allows the deep learning model to learn the GPS route of pedestrians, and proposes a trajectory prediction deep learning model that does not have large restrictions on the prediction range. The parameters suitable for the trajectory prediction deep learning model of this study are presented, and the model's test performance are presented.

• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.647-654
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• 2010

In a service environment, the spatial layout is an important factor that has a great impact on customers' behavioral characteristics including wayfinding and purchasing. Previous studies have shown a gap between marketing, focusing solely on profitability and satisfaction, and architecture, looking only into efficiency of pedestrian flow. To balance such disparity, this study suggests an integrated approach for assessing behavioral patterns in complex service environments. With the objective that complex service environments should aim to increase its profitability and efficiency while guaranteeing customer satisfaction, quantitative metrics was developed for evaluation. The metrics was defined to use data from behavior observation including path tracking, population counting, and gaze analysis, while previous studies have relied on abstract survey methods that were prone to sampling errors and loss of data. For validation of the metrics in a real world setting, a case study was conducted at 4 train stations in Korea. In the case study, experiments were conducted to gather the required data in all 4 train stations, while their physical layouts were also analyzed. With the results from the case study, comparative evaluation of the 4 train stations in terms of behavioral efficiency was possible, together with a discussion on the effect of their physical settings.

• KIEAE Journal
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• v.10 no.5
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• pp.13-22
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• 2010

The old city centers of local metropolitans have lost their functions as CBD in korea. Those old city centers have an only role as a gate connected to the new CBD. This study aims to present regeneration stratigies of old city centers through Osaka case study. This research has been focused on the physical and environmental factors in urban regeneration. There are 4 strategies for old city centers. First, the strategy to attract people to the old city centers is high-density and mixed-use development having functions like shopping, entertainment, residence. This kind of development makes local metropolitan cities compact cities to protect urban sprawl. Second, strategy to give old city centers an identity is to conserve traditional culture and structures and to revitalize retail market. Third is to make pedestrian-friendly street system. Osaka ism't pedestrian friendly but remodelling the connect the pedestrian path to the culture facilities. Fourth is to have water and green environment. Green space is the strong factor that pull people to old city centers.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.67-79
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• 2017

Increasing transportation dependence on the metro system has lead to the convenience of passengers becoming as important as the transportation capacity. In this study, a pedestrian simulator has been developed that can quantitatively assess the pedestrian environment in terms of attributes such as speed and distance. The simulator consists of modules designed for 3D indoor map authoring and algorithmic pedestrian modeling. Module functions for 3D indoor map authoring include 3D spatial modeling, network generation, and evaluation of obtained results. The pedestrian modeling algorithm executes functions such as conducting a path search, allocation of users, and evaluation of level of service (LOS). The primary objective behind developing the said functions is to apply and analyze various scenarios repeatedly, such as before and after the improvement of the pedestrian environment, and to integrate the spatial information database with the dynamic information database. Furthermore, to demonstrate the practical applicability of the proposed simulator in the future, a test-bed was constructed for a currently operational metro station and the quantitative index of the proposed improvement effect was calculated by analyzing the walking speed of pedestrians before and after the improvement of the passage. The possibility of database extension for further analysis has also been discussed in this study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.37-49
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• 2015

In Space Syntax, the greater the degree of integration between separate links, the greater the links' accessibility from the target network. As such, planning pedestrian walks so that links with high degrees of integration are connected, or else inducing high integration value land use are both valid options. The travel distribution model reflects how walking demand, or more specifically, the pedestrian, partakes in route choosing behavior that minimizes select criteria, notably level of discomfort, as measured using travel distance and time. The model thus demonstrates travel patterns associated with demand pertaining to minimization of discomfort experienced by the pedestrian. This research introduces a method that integrates Space Syntax and the pedestrian travel distribution model. The integrated model will determine whether regions with high degrees of integration are actually being used as pivots for pedestrian demand movement, as well as to explain whether the degree of integration is sustained at an appropriate level while considering actual movement demand. As a means to develop the integrated model, a method that combines display of the visibility of the space syntax network and road-divided links is proposed. The pedestrian travel distribution model also includes an alternative path finding mechanism between origin and destination, which allows for uniform allocation of demand.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.49-54
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• 2017

Pathfinding for pedestrians provided by various navigation programs is based on a shortest path search algorithm. There is no big difference in their guide results, which makes the path quality more important. Multiple criteria should be included in the search cost to calculate the path quality, which is called a multi-criteria pathfinding. In this paper we propose a user adaptive pathfinding algorithm in which the cost function for a multi-criteria pathfinding is defined as a weighted sum of multiple criteria and the weights are learned automatically by Perceptron learning. Weight learning is implemented in two ways: short-term weight learning that reflects weight changes in real time as the user moves and long-term weight learning that updates the weights by the average value of the entire path after completing the movement. We use the weight update method with momentum for long-term weight learning, so that learning speed is improved and the learned weight can be stabilized. The proposed method is implemented as an app and is applied to various movement situations. The results show that customized pathfinding based on user preference can be obtained.