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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 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 control algorithm based on a skid steering for a Robotic Vehicle with Articulated Suspension (RVAS). The RVAS is a kind of unmanned ground vehicle based on a skid steering using independent in-wheel drive at each wheel. The driving control algorithm consists of four parts: a speed controller for following a desired speed, a lateral motion controller that computes a yaw moment input to track a desired yaw rate or a desired trajectory according to the control mode, a longitudinal tire force distribution algorithm that determines an optimal desired longitudinal tire force and a wheel torque controller that determines a wheel torque command at each wheel in order to keep the slip ratio at each wheel below a limit value as well as to track the desired tire force. The longitudinal and vertical tire force estimators are required for the optimal tire force distribution and wheel slip control.

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.

This paper presents a closed-loop evaluation of the Vehicle Stability Control (VSC) systems using a vehicle simulator. Human driver-VSC interactions have been investigated under realistic operating conditions in the laboratory. Braking control inputs for vehicle stability enhancement have been directly derived from the sliding control law based on vehicle planar motion equations with differential braking. A driving simulator which consists of a three-dimensional vehicle dynamic model, interface between human driver and vehicle simulator, three-dimensional animation program and a visual display has been validated using actual vehicle driving test data. Real-time human-in-the loop simulation results in realistic driving situations have shown that the proposed controller reduces driving effort and enhances vehicle stability.

An estimation algorithm for vehicle driving load has been proposed in this paper. Driving load is an important factor in a vehicle's longitudinal motion control. An approach using an observer is introduced to estimate driving load based on inexpensive RPM sensors currently being used in production vehicles. Also, the new torque estimation technique using neural network has been incorporated in this estimation algorithm to achieve better performance over variations in the automotive power transmissions process. The effectiveness of the observer-based method is demonstrated through the use of a nonlinear full vehicle simulation model in various scenarios. The proposed method using an observer has good performance, both over modeling error in powertrain system and under the uncertain environment of a running vehicle.

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 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.

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.

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.

CW/CA (Collision Warning /Collision Avoidance) Systems have been an active research and development area as interests and demands for the advanced vehicle increase. A CW/CA ‘Hardware-in-the-Loop Simulation (HiLS)’ system has been designed and used to test a CW/CA algorithm, radar sensors, and warning displays under realistic operating conditions in the laboratory. A CW/CA algorithm has two parts. One is a distance decision algorithm that determines the critical warning and braking distance and the other is a brake control algorithm for collision avoidance. The CW/CA HiLS system consists of a controller in which a DSP chip is installed, a preceding vehicle simulator, a radar sensor and a warning display. The controller calculates velocities of the preceding and following vehicles, relative distance and relative velocity of the vehicles using vehicle simulation models. The relative distance and velocity are applied to the vehicle simulator that is controlled by a DC motor.