• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.259-260
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• 2012

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.332-347
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• 2017

Analysis of the interaction between the automated vehicles and manual vehicles is very important in analyzing the performance of automated cooperative driving environments. In particular, the automated vehicle platooning can affect the driving behavior of adjacent manual vehicles. The purpose of this study is to analyze the lane change behavior of the manual vehicles in automated vehicle platonning environment and to conduct the experiment and questionnaire surveys in three stages. In the first stage, a video questionnaire survey was conducted, and responsive behaviors of manual vehicles were investigated. In second stage, the driving simulator experiments were conducted to investigate the lane change behaviors of in automated vehicle platonning environments. To analyze the lane change behavior of the manual vehicles, lane change durations and acceleration noise, which are indicators of traffic flow stability, were used. The driving behavior of manual vehicles were compared across different market penetration rates (MPR) of automated vehicles and human factors. Lastly, NASA-TLX (NASA Task Load Index) was used to evaluate the workload of the manual vehicle drivers. As a result of the analysis, it was identified that manual vehicle drivers had psychological burdens while driving in automated vehicle platonning environments. Lane change durations were longer when the MPR of the automated vehicles increased, and acceleration noise were increased in the case of 30-40 years old or female drivers. The results from this study can be used as a fundamental for more realistic traffic simulations reflecting the interaction between the automated vehicles and manual vehicles. It is also expected to effectively support the establishment of valuable transportation management strategy in automated vehicle environments.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.6
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• pp.194-207
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• 2000

In this paper, we suggest a vision-based lane change control system, which can be applied on the straight road, without additional sensors such as a yaw rate sensor and a lateral accelerometer. In order to reduce the image processing time, we predict a reference line position during lane change using the lateral dynamics and the inverse perspective mapping. The sliding mode control algorithm with a boundary layer is adopted to overcome variations of parameters that significantly affects a vehicle`s lateral dynamics and to reduce chattering phenomenon. However, applying the sliding mode control to the system with a long sampling interval, the stability of a control system may seriously be affected by the sampling interval. Therefore, in this paper, a look ahead offset has been used instead of a lateral offset to reduce the effect of the long sampling interval due to the image processing time. The control algorithm is developed to follow the desired trajectory designed in advance. In the design of the desired trajectory, we take account of the constraints of lateral acceleration and lateral jerk for ride comfort. The performance of the suggested control system is evaluated in simulations as well as field tests.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.1
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• pp.167-175
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• 2006

Directional stability is important performance in vehicle and tire design. The current methods to analyze this is generally based on linear concept. Using the existing concept, it cannot realistically explain the subjective assessment at all because it is hard to practically represent the nonlinear behaviour of a complex vehicle system in reality. In this paper, new method to analyze directional stability is introduced. At first, directional stability of vehicle is categorized into yaw, rear axle, and roll stability. In order to objectify these items, driver perceptual parameters based on subjective assessment are used. Using the perceptual parameters, it can successfully explain the transient maneuver of vehicle and extract objective parameters for directional stability. Finally, these objective parameters are successfully validated through two handling tests, lane change and severe lane change. The correlation results show that there exists a good correlation between subjective assessment and the proposed objective parameters.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.139-146
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• 2021

Ahead of the commercialization of autonomous vehicles, it's application should be considered into the current transportation infrastructure. Under limited traffic circumstances, effective set of lane change rules alone could bring benefits to the autonomous driving system. In this study, a cooperative movement (local platooning) plan with limited vehicles associated as pocket driving, aiming at effective movement between vehicles in urban environment was proposed. Under congested roadway condition, the gaussian gap between vehicles was introduced to secure gap acceptance for safe lane change maneuver. Proposed lane change method showed 86.6% delay reduction along with traffic volume improvement. This result could be considered as a crucial factor in designing a next-generation roadway infrastructure with autonomous driving.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.65-70
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• 2022

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2D
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• pp.227-234
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• 2009

At present, the length of deceleration lane at freeway diverging areas are designed based on the design speed of main lines and ramps. This is possible on assumption that diverging vehicles decelerate at deceleration section after moving to shoulder lane in advance. But with high diverging volume, several vehicles will try to change to exit lane at the same time. This will cause the distribution of main lane flows or some vehicles may encounter short deceleration length because they miss the proper time to change the lane. The purpose of this study is to establish a design guideline of the length of deceleration section considering the volume of diverging traffic. Also, the results of analysis by the FRESIM simulation model shows that some improvements in respect of delays, speeds and speed deviations of mainline and deceleration lane.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.3
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• pp.168-175
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• 1996

Except the collision avoidance performance related to the rapid lane change, the 4WS vehicle has better dynamic stability and handling performance than the conventional 2WS vehicle which has close relation with the driver's safety, a 4WS conrol method with the effect of steering wheel angular velocity is proposed based on the fact that the driver steers abruptly the steering wheel to avoid the collision. And the effects of the proposed 4WS control method are investigated on the dynamic stability and handling performance by using the ISO lane change test code.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.3
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• pp.16-22
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• 2011

This paper presents the potential of in-vehicle data recorder system for monitoring aggressive driving patterns and providing feedback to drivers on their on road behaviour. This system can detect 10 risky types of drivers' driving patterns such as aggressive lane change, sudden brakes and turns with acceleration etc. Vehicle dynamics simulation and vehicle road test have been performed in order to develop driving pattern recognition algorithms. Recorder systems are installed to 50 buses in a single company. Drivers' driving behaviour are monitored for 1 month. The drivers' risky driving data collected by the system are analyzed. Aggressive lane change in 50km/h below is a cause in overwhelming majority of risky driving pattern.

• 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.