Chang Mook Kang

In this paper, we deal with comparative evaluation of dynamic and kinematic vehicle models for autonomous driving control systems. Comparative lane coefficients estimation performance of each model is evaluated according to the tire slip angle. Each estimation makes use of clothoidal road constraints, respectively. We observe that the kinematic model is robust against unknown vehicle parameters tire-road condition or cornering stiffness for autonomous driving systems under lane keeping situation

In this paper, a novel multirate lane-keeping system (LKS) is introduced by using a kinematic-based model considering a look-ahead distance. First, we developed a kinematic lateral motion model for LKS with respect to the road. To increase a system damping of vehicle, the unique look-ahead output measurement matrix was introduced. The decentralized multirate lane-keeping control scheme with a multirate Kalman filter was also applied to resolve the asynchronous and irregular sampling time of mult

This article addresses path generation and predictive control for waypoints tracking. By considering irregularity of waypoints given by the leading vehicle, the waypoints-following vehicle is made to track a clothoid path that assures safe and comfortable driving while keeping track of the waypoints in the least squares sense. In the generation of such a clothoid path, the path generation scheme utilized the clothoidal constraint and weighted least squares curve fitting. Predictive control is ap

Predictive virtual lane has been known that it can cope with up to several sampling period camera failure providing improved lateral control performance of lane keeping system. In this paper, we propose an improved lane estimation scheme using a vehicle kinematic lateral motion model and clothoidal road constraints to resolve a longer failure than that considered in the previous study. The lane estimation system gives virtual lane information to the lateral control system. The developed algorith

In this paper, we designed a unique structure and developed a novel linear parameter varying (LPV) formulation design for a vehicle-model-based state observer. The design problem for a vehicle state observer was totally reformulated into an LPV system that depended on online accessible time-varying parameters. The proposed LPV formulation, which is presented for the first time in this paper, did not require high computational costs compared to a conventional approach using an extended Kalman fil

This paper proposes a cascaded backstepping control method with an augmented observer for the lateral control of an autonomous vehicle. The proposed cascaded backstepping control structure consists of an inner-loop electric power steering (EPS) system and an outer-loop lane-keeping system (LKS). The outer-loop controller for LKS calculates and provides the desired steering angle to the inner-loop EPS system for maintaining the vehicle at the center of the lane. Subsequently, the inner-loop contr

In this paper, we consider on-road vehicle localization with the global positioning system (GPS) under long term failure of a vision sensor. When the output of vision sensor is not available due to its malfunction and/or environmental conditions, it is necessary for the lateral control system to maintain its stability before the driver takes over control authority. We propose how to estimate lane coefficients with GPS data and the latest normal lane coefficients in the presence of failure. This

In this paper, we propose a novel scheme to control the lateral motion of a vehicle using the kinematic model of vehicle. The kinematic lateral motion control problem for lane keeping system is reformulated as the linear parameter varying (LPV) system which depends on online accessible time varying parameters. Both vehicle speed and look-ahead distance are considered as varying parameters which reflect actual driving situations. The LPV scheme becomes an optimal control gain scheduling problem s

In this paper, we presents a comparative study of dynamic and kinematic vehicle models based on interacting multiple model(IMM) filter. It is known that the characteristics of kinematic and dynamic lateral motion models vary according to driving conditions. From the IMM filter, we can obtain the stochastically best blended state of the vehicle. Not only the performance of IMM filter but also the reliability of both kinematic and dynamic lateral motion models were evaluated according to the steer

<div class="section abstract"><div class="htmlview paragraph">In this paper, we propose a vision based lateral control scheme for autonomous lane change system on highways. Three main techniques are proposed, to improve the lane keeping/lane change performance, and to reduce the ripple in the yaw rate on highways. First, we propose a model based lane prediction method to cope with the momentary failure of lane detection. Second, we innovate an approach to steering wheel angle control

In this paper, we propose kinematic vehicle lateral motion model based lane keeping system considering look-ahead distance. The state-space model based on the kinematic vehicle lateral motion model is derived and we design the lane keeping system(LKS) based on the kinematic model. The kinematic model based LKS is robust against unknown vehicle parameters variation. Furthermore, to consider look-ahead distance in the kinematic vehicle lateral motion model, we designed output measurement matrix us

A nonlinear backstepping control is proposed for the coupled normal form of nonlinear systems. The proposed method is designed by combining the sliding‐mode control and backstepping control with a disturbance observer (DOB). The key idea behind the proposed method is that the linear terms of state variables of the second subsystem are lumped into the virtual input in the first subsystem. A DOB is developed to estimate the external disturbances. Auxiliary state variables are used to avoid amplifi

For advanced driver assistance system (ADAS) which are related to risk assessment or collision avoidance, predicting object vehicle's path is needed beyond the precise and reliable sensor data to improve the performance of path prediction. This paper proposes an object vehicle path prediction method using parametric interpolation. To obtain a precise and reliable sensor data, multirate sensor data fusion was applied. After that, by using the parametric interpolation, we can predict the object ve