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This paper presents a methodology of trajectory planning for the surrounding-aware lane change maneuver of autonomous vehicles based on a data-driven method. The lateral motion is planned by sampling candidate patterns which are defined based on quintic polynomial functions over time. Based on the cost evaluation among the sampled candidates, the optimal lateral motion pattern is selected as a reference and tracked by the controller. The longitudinal motion is planned and controlled using Model Predictive Control (MPC) which is an optimal control method designed considering the surrounding traffic information. To realize the lane change motion similar to the human driving behavior in the surrounding traffic situation, the human driving pattern is modeled in the form of motion parameters and considered in planning the lateral and longitudinal motion.

This paper represents a parking lot occupancy detection and parking control algorithm for the autonomous valet parking system. The parking lot occupancy detection algorithm determine the occupancy of the parking space, using LiDAR sensors mounted at each side of front bumper. Euclidean minimum spanning tree (EMST) method is used to cluster that information. After that, a global parking map, which includes all parking lots and access road, is constructed offline to figure out which cluster is located in a parking space. By doing this, searching for available parking lots has been finished. The proposed parking control algorithm consists of a reference path generation, a path tracking controller, and a parking process controller. At first, route points of the reference path are determined under the consideration of the minimum turning radius and minimum safety margin with near parking.

This paper presents an automated driving control algorithm for the control of an autonomous vehicle. In order to develop a highly automated driving control algorithm, one of the research issues is to determine a safe driving envelope with the consideration of probable risks. While human drivers maneuver the vehicle, they determine appropriate steering angle and acceleration based on the predictable trajectories of the surrounding vehicles. Therefore, not only current states of surrounding vehicles but also predictable behaviors of that should be considered in determining a safe driving envelope. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, the safe driving envelope over a finite prediction horizon is defined in consideration of probabilistic prediction of future positions of surrounding vehicles.

This paper presents a methodology of correlation between subjective and objective measures of vehicle on-center handling performance. The subjective measure is a professional test driver's rating of vehicle handling, while the objective measure assesses the handling performance via vehicle dynamic responses. Vehicle test data obtained from field testing has been analyzed to investigate links between the objective and subjective measures. Fifty-six physical parameters have been derived from on-centering hysteresis curves. Statistical tools are employed to obtain good correlation between driver rating and physical parameters. Using an interaction formula, a statistical model which relates the driver rating and principal physical parameters has been obtained. The proposed methodology will be used to show the physical parameters influence on subjective assessment and even to predict the subjective assessment of a vehicle handling performance.

This paper presents the design and evaluation of an emergency driving support (EDS) algorithm. The control objective is to assist driver's collision avoidance maneuver to overcome a hazardous situation. To support driver, electrically controllable chassis components such as motor driven power steering (MDPS) and differential braking and surrounding sensor systems such as radar and camera are used. The EDS algorithm is designed for 3 parts: monitoring, decision, and control. The proposed EDS algorithm recognizes a collision danger using minimum lateral acceleration to avoid collision and time-to-collision (TTC) and driver's intention using sensor systems. The control mode is determined using the indices from monitoring process and the collision avoidance trajectory is derived with trapezoidal acceleration profile (TAP).

This paper describes a driving motor torque distribution strategy of six-wheel-driven electric vehicles for optimized energy consumption. In this research, this strategy minimizes motoring power consumption and maximizes regenerative braking power under given required power condition. The torque distribution controller consists of total required motor torque calculation part, upper and optimal torque calculation part, lower level controller. The upper level controller determines total required torque of vehicle. And the torque is determined by acceleration pedal input of driver and vehicle velocity. The lower level controller calculates energy consumption in given condition and distributes motor torque to driving motor minimizing energy consumption. In distributing optimal motor torque, it is important to get accurate characteristics of driving motor and performance constraint.

This paper describes a lane departure avoidance system to help the driver avoid the lane departure during drowsiness or inattention. The lane departure avoidance system proposed in this paper consists of unintended lane departure decision part, upper level controller part and lower level controller part. The index used in unintended lane departure decision part is proposed to monitor a driver's intention with steering behaviors. The desired dynamics is calculated in upper level controller part. When the desired dynamics is calculated, it is considered to guarantee a driver's safety and smooth ride feel simultaneously as possible. The lower level controller distributes the desired control input to actuators, motor driven power steering (MDPS) module and vehicle stability control (VSC) module. The proposed lane departure avoidance system has been evaluated via human driver model-in the loop simulation.

This paper describes a driving controller to improve vehicle lateral stability and maneuverability for a six wheel driving / six wheel steering (6WD/6WS) vehicle. The driving controller consists of upper and lower level controller. The upper level controller based on sliding control theory determines front, middle steering angle, additional net yaw moment and longitudinal net force according to reference velocity and steering of a manual driving, remote control and autonomous controller. The lower level controller takes desired longitudinal net force, yaw moment and tire force information as an input and determines additional front steering angle and distributed longitudinal tire force on each wheel. This controller is based on optimal distribution control and has considered the friction circle related to vertical tire force and friction coefficient acting on the road and tire.

This paper presents an integration of longitudinal and lateral human driver model for evaluation of vehicle active safety systems. The integrated human driver model consists of 3 parts; recognition, decision, action which represents a real driver's driving process. The recognition part and action part of the driver model has a few parameters that can represent real driver's characteristics in the driving situation. For example, preview distance, neuromuscular system, warning index and time to collision. Also, these parameters are extracted based on real driver's manual driving data. The decision part is made up with lateral and longitudinal human driver models. The lateral human driver model is developed to represent steering behavior of human driver using finite preview optimal control method. The longitudinal human driver model represents human driver's throttle and brake control behavior relative to preceding vehicle motion and road shape.

The performance of an integrated Vehicle Stability Control (VSC) system depends on not only control logic itself, but also the performance of state estimator and control threshold. In conventional VSCs, a control threshold is designed by vehicle characteristics and is centered on average drivers. A VSC algorithm with variable control threshold has been investigated in this study. The control threshold can be determined by phase plane analysis of side slip angle and angular velocity. Vehicle side slip angle estimator has been evaluated using test data. Estimated side slip angle has been used in the determination of the control threshold. The performance of the proposed VSC algorithm has been investigated by human-in-the-loop simulation using a vehicle simulator. The simulation results show that the control threshold has to be determined with respect to the driver steering characteristics.