• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.93-99
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• 2020

A majority of car crash is affected by careless driving that causes extensive economic and social costs, as well as injuries and fatalities. Thus, the research of precise crash detection systems is very significant issues in automotive safety. A lot of crash detection algorithms have been developed, but the coverage of these algorithms has been limited to few scenarios. Road scenes and situations need to be considered in order to expand the scope of a collision detection system to include a variety of collision modes. The proposed algorithm effectively handles the x, y, and z axes of the sensor, while considering time and suggests a method suitable for various real worlds. To reduce nuisance and false crash detection events, the algorithm discriminated between driving mode and parking mode. The performance of the suggested algorithm was evaluated under various scenarios, and it successfully discriminated between driving and parking modes, and it adjusted crash detection events depending on the real scenario. The proposed algorithm is expected to efficiently manage the space and lifespan of the storage device by allowing the vehicle's black box system to store only necessary crash event's videos.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.148-156
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• 2003

Many car manufacturers have frequently adopted an aggressive inflator and a lower threshold speed for airbag deployment in order to meet an injury requirement for unbolted occupant at high speed crash test. Consequently, today's occupant safety restraint system has a weakness due to an airbag induced injury at low speed crash event. This paper proposes a new crash algorithm to improve the weakness by suppressing airbag deployment at low speed crash event in case of belted condition. The proposed algorithm consists of two major blocks-crash severity algorithm and deployment logic block. The first block decides crash severity with two levels by means of velocity and crash energy calculation from acceleration signal. The second block implemented by simple AND/OR logic combines the crash severity level and seat belt status information to generate firing commands for airbag and belt pretensioner. Furthermore, it can be extended to adopt additional sensor information from passenger presence detection sensor and safing sensor. A simulation using real crash data for a 1,800cc passenger vehicle has been conducted to verify the performance of proposed algorithm.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.5
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• pp.45-54
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• 1998

For proper release of side airbags, the onset of crash should be detected first. After crash detection, the algorithm has to make a decision whether the side airbag deployment is necessary. If the deployment is necessary, proper timing has to be provided for the maximum protection of driver or passenger. The side airbag release algorithm should be robust against the statistical deviations which are inherent to experimental crash test data. Deterministic optimization algorithms cannot be used for the side aribag release algorithm since the objective function cannot be expressed in a closed form. From this background, genetic algorithm has been used for the optimization. The optimization requires moderate amount of computation and gives satisfactory results.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.179-185
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• 2006

Airbag deployment has been responsible for huge death, incidental injuries and broken bones due to low crash severity and wrong deployment decision. This misfortune has led the authorities and the industries to pursue uniquely designed airbags incorporating crash-sensing technologies. This paper provides a thorough discussion underlying crash sensing algorithm approaches for the subject matter. Unfortunately, most algorithms used for crash sensing still have some problems. They either deploy at low severity or fail to trigger the airbag on time. In this work, the crash-sensing algorithm is studied by analyzing the data obtained from the variables such as (i) change of velocity, (ii) speed of the vehicle and (iii) acceleration. The change of velocity is used to detect crash while speed of the vehicle provides relevant information for deployment decision. This paper also demonstrates crash severity with respect to the changing speed of the vehicle. Crash sensing simulations were carried out using Simulink, Stateflow, SimMechanics and Virtual Reality toolboxes. These toolboxes are also used to validate the results obtained from the simulated experiments of crash sensing, airbag deployment decision and its crash severity detection of the proposed system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.2
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• pp.42-54
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• 2011

This study presented an algorithm to detect zigzag driving maneuver that is highly associated with vehicle crash occurrence. In general, the zigzag driving results from the driver's inattention including drowsy driving and driving while intoxicated. Therefore, the technology to detect such unsafe driving maneuver will provide us with a valuable opportunity to prevent crash in the road. The proposed detection algorithm used angular velocity data obtained from a gyro sensor. Performance evaluations of the algorithm presented promising results for the actual implementation in practice. The outcome of this study can be used as novel information contents under the ubiquitous transportation systems environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1671-1677
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• 2018

In this research, the collision detection system based on insect's visual interneuron has been designed. The lobula giant movement detector (LGMD) corresponds to the movement value that increases in direct collision process. If the collision is detected by the LGMD only, it could generate a crash warning even in a non-collision situation, resulting in a lot of false alarms. Directionally sensitive movement detectors (DSMD) are directionally sensitive algorithm based on the elementary movement detectors (EMD) in four directions (up, down, left, and right). In this paper, we propose an efficient VLSI architecture for a realtime collision detection system that is robust to the surrounding environment while improving accuracy. The proposed architecture is synthesized with Dongbu Hightech 110nm standard cell library and shows 333MHz of maximum operating frequency and requires 8400 gates with about 16.5KB of internal memories.

• Spatial Information Research
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• v.22 no.1
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• pp.75-84
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• 2014

Power lines are one of the main obstacles causing an aircraft crash and thus their realtime detection is significantly important during flight. To avoid such flight obstacles, the use of LIDAR has been recently increasing thanks to its advantages that it is less sensitive to weather conditions and can operate in day and night. In this study, we suggest a fast method to detect power lines from LIDAR data for flight obstacle avoidance. The proposed method first extracts non-ground points by eliminating the points reflected from ground surfaces using a filtering process. Second, we calculate the eigenvalues for the covariance matrix from the coordinates of the generated non-ground points and obtain the ratio of eigenvalues. Based on the ratio of eigenvalues, we can classify the points on a linear structure. Finally, among them, we select the points forming horizontally long straight as power-line points. To verify the algorithm, we used both real and simulated data as the input data. From the experimental results, it is shown that the average detection rate and time are 80% and 0.2 second, respectively. If we would improve the method based on the experiment results from the various flight scenario, it will be effectively utilized for a flight obstacle avoidance system.