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This paper describes an optimal torque vectoring strategy for 4WD electric vehicles (EV) in order to improve vehicle maneuverability, lateral stability and at the same time prevent vehicle rollover. The 4WD EV is driven using an in-line motor at a front driving shaft and in-wheel motors at rear wheels. Many previous studies have been conducted to determine a desired traction force and a yaw moment input for human driver's intention or vehicle stability control. The driving control algorithm consists of three parts: a supervisory controller that determines the control mode, admissible control region, and desired dynamics, such as the desired speed and yaw rate, an upper-level controller that computes the traction force input and yaw moment input to track the desired dynamics and an optimal torque vectoring algorithm that determines actuator commands, such as the front in-line motor, rear in-wheel motors and independent brake modules.

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 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 describes a driving control algorithm based on skid steering for a Robotic Vehicle with Articulated Suspension (RVAS). The driving control algorithm consists of four parts; speed controller for tracking of the desired speeds, yaw rate controller which computes a yaw moment input to track desired yaw rates, longitudinal tire force distribution which determines an optimal desired longitudinal tire force and wheel torque controller which determines a wheel torque command at each wheel to keep slip ratio at each wheel below a limit value as well as track the desired tire force. Longitudinal and vertical tire force estimators are designed for optimal tire force distribution and wheel slip control. The dynamic model of RVAS for simulation study is validated using vehicle test data.