• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.70-74
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• 2022

This paper describes the development of a target vehicle state estimation algorithm using vehicle-to-vehicle (V2V) communication. Perceiving the state of the target vehicle has great importance for successful autonomous driving and has been studied using various sensors and methods for many years. V2V communication has advantage of not being constrained by surrounding circumstances relative to other sensors. In this paper, we adopt the V2V signal for estimating the target vehicle state. Since applying only the V2V signal is improper by its low frequency and latency, the signal is used as additional measured data to improve the estimation accuracy. We estimate the target vehicle state using Extended Kalman filter (EKF); a point mass model was utilized in process update to predict the state of next step. The process update is followed by measurement update when ego vehicle receives V2V information. The proposed study evaluated state estimation by comparing input V2V information in an experiment where the ego vehicle follows the target vehicle behind it.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.448-461
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• 2019

Changes in gravity and buoyancy of a Full Ocean Depth Autonomous Underwater Vehicle (FOD-AUV) during its descending and ascending process must be considered very carefully compared with a Human Occupied Vehicle (HOV) or a Remotely Pperated Vehicle (ROV) whose activities rely on human decision. We firstly designed a two-step weight dropping pattern to achieve a high descending and ascending efficiency and a gravity-buoyancy balance at designed depth. The static equations showed that gravity acceleration, seawater density and displacement are three key aspects affecting the balance. Secondly, we try our best to analysis the gravity and buoyancy changing according to the previous known scientific information, such as anomaly of gravity acceleration, changing of seawater states. Finally, we drew conclusion that gravity changes little (no more than 0.1kgf, it is impossible to give a accurate value). A density-depth relationship at the Challenger Deep was acquired and the displacement changing of the FOD-AUV was calculated preciously.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1103-1108
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• 2007

This paper describes a robust model-based roll state estimator for application to the detection of impending vehicle rollover. The roll state estimator is based on a 2-D bicycle model and a roll model to estimate the maneuver-induced vehicle roll motion. The measurement signals are lateral acceleration, yaw rate, steering angle, and vehicle speed. Vehicle mass is adapted to obtain robust performance of the estimator. Computer simulation is conducted to evaluate the proposed roll state estimator by using a validated vehicle simulator. It is shown that the roll state estimator shows robust performance without exact vehicle mass information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.263-266
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• 2011

Drivers check always vehicle state for own safety and is necessary to understand about vehicle state. If driver know vehicle state, because driver request vehicle state at vehicle specialty company, driver pay a lot of money and waste a lot of time. Now, drivers have checked vehicle state by using variety features of smart phone due to progress of IT(Information Technology). but existing smartphone vehicle diagnostic system learned professional knowledge of vehicle and know vehicle state. drives not need vehicle diagnostic. to overcome these disadvantages, there use easily drivers by using smartphone and request system to know own all vehicle state. In this paper, there implement vehicle diagnostic system with smartphone based on android OS to use easily this system and know check information of vehicle supplies replacement cycles, vehicle internal problems, Eco driving by using OBD-II data to receive by OBD-II Protocol convert Bluetooth connector.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• International Journal of Railway
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• v.1 no.3
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• pp.94-98
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• 2008

Static inverters are essential devices onboard of rolling stock. State-of-the-art static inverters have an impact on both rail infrastructure and vehicle maintenance due to their new topology with new features. The paper describes two important aspects as examples of new features available in state-of-the-art static inverters: active input current control and the effects on the rail infrastructure as well as the detection of the state of charge and the state of health of batteries to simplify vehicle maintenance.

• ETRI Journal
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• v.36 no.3
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• pp.439-449
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• 2014

In this paper, a novel vision-based nighttime vehicle detection approach is presented, combining state machines and downhill simplex optimization. In the proposed approach, vehicle detection is modeled as a sequential state transition problem; that is, vehicle arrival, moving, and departure at a chosen detection area. More specifically, the number of bright pixels and their differences, in a chosen area of interest, are calculated and fed into the proposed state machine to detect vehicles. After a vehicle is detected, the location of the headlights is determined using the downhill simplex method. In the proposed optimization process, various headlights were evaluated for possible headlight positions on the detected vehicles; allowing for an optimal headlight position to be located. Simulation results were provided to show the robustness of the proposed approach for nighttime vehicle and headlight detection.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.688-698
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• 2012

This paper deals with ROLSO (Reduced Order Luenberger State Observer) design to prevent jackknifing phenomenon of articulated vehicles consisting of the tractor and semi-trailer by using GPS. In addition, by applying the regulator system using ROLSO feedback system, simulation's result presents that articulated vehicle's states are stabilized than the human's PR time (Preception Response time) rapidly. This simulation verifies that the automatic control of articulated vehicle's can be applied for the accident prevention for the time that the driver is unable to manage with the sudden accident. For this simulation, by using the equation of planar motion, the modeling of the articulated vehicle was performed. This modeling was expressed in the state space model. And FOLSO (Full Order Luenberger State Observer), ROLSO were designed by using the state space model of an articulated vehicle's dynamics.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2960-2967
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• 2021

In this work, a new hardening model is proposed for the depth-dependent hardness of ion-irradiated polycrystals with obvious grain size effect. Dominant hardening mechanisms are addressed in the model, including the contribution of dislocations, irradiation-induced defects and grain boundaries. Two versions of the hardening model are compared, including the linear and square superposition models. A succinct parameter calibration method is modified to parametrize the models based on experimentally obtained hardness vs. indentation depth curves. It is noticed that both models can well characterize the experimental data of unirradiated polycrystals; whereas, the square superposition model performs better for ion-irradiated materials, therefore, the square superposition model is recommended. In addition, the new model separates the grain size effect from the dislocation hardening contribution, which makes the physical meaning of fitted parameters more rational when compared with existing hardness analysis models.

• Proceedings of the Korean DIstribution Association Conference
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• 2003.02a
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• pp.109-120
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• 2003

As the logistics industrial environment becomes more complex and its scale becomes increase, the vehicle fleet scheduling system has become recognized the necessity as a major strategy in the logistics field. The vehicle fleet scheduling system is computerized package that find the vehicle routes and schedules to accomplish the required service to customers using vehicles. This paper introduces the state of the art of vehicle fleet scheduling system in Korea and foreign country and the future research subject are presented.