• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.6
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• pp.1-9
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• 2008

The current test methods are insufficient to evaluate and ensure the safety and reliability of vehicle system for all possible dynamic situation including the worst case such as rollover, spin-out and so on. Although the known NHTSA J-turn and Fish-hook steering maneuvers are applied for the vehicle performance assessment, they aren't enough to estimate other possible worst case scenarios. Therefore, it is crucial for us to verify the various worst cases including the existing severe steering maneuvers. This paper includes the procedure to search for other useful worst case based upon the existing worst case scenarios mentioned above and its application in simulation basis. The only human steering angle is selected as a design parameter here and optimized to maximize the index function to be expressed in terms of either roll angle or yaw rate. The obtained scenarios were enough to generate the worst case to meet NHTSA worst case definition (ex.2-inch wheel lift). Additionally, as an application, the worst case steering maneuver is acquired for the vehicle to operate with a simple ESP system. It has been concluded that the new procedure in this paper is adequate to create other feasible worst case scenarios for a vehicle system both with an intelligent safety control system and without it.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.225-231
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• 2012

In this paper, we propose a method for designing the path tracking controller using an approach angle concept for an underactuated autonomous underwater vehicle (AUV). The AUV is controlled by the surge speed and yaw rate: there is no side thruster. To solve this underactuated AUV problem in the path tracking, we introduce an approach angle concept which makes the AUV converge to the reference path. And we design the path tracking controller using the proposed approach angle. To design the path tracking controller, we obtain the new vehicle's error dynamics in the body-fixed frame, and then design the path tracking controller based on Lypunov direct method. Finally, some simulation results demonstrate the effectiveness of the proposed controller.

• 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.