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This paper describes vehicle stability control (VSC) scheme to prevent rollover and to improve both maneuverability and lateral stability by integrating individual chassis control modules such as electronic stability control (ESC), active front steering (AFS) and continuous damping control (CDC). The proposed VSC system consists of an upper and lower level controller. The upper level controller determines a control mode such as rollover mitigation, maneuverability and lateral stability, and it also calculate desired values for its objectives. The lower level controller determines longitudinal and lateral tire forces as inputs of each control modules such as the ESC and AFS. From the simulation results, it is shown that the proposed VSC system can prevent vehicle rollover, while at the same time improving both maneuverability and lateral 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 is concerned with the torque distribution problem including slip limitation and actuator fault tolerance to improve vehicle lateral stability and maneuverability of six-wheeled skid-steered vehicles. The torque distribution algorithm to distribute wheel torque to each wheel of a skid-steered vehicle consists of an upper level control layer, a lower level control layer and an estimation layer. The upper level control layer is designed to obtain longitudinal net force and desired yaw moment, while the lower level control layer determines distributed driving and braking torques to six wheels. The algorithm takes vehicle speed, slip ratio and tire load information from the estimation layer, as well as actuator fault information from each in-wheel motor controller unit.

This paper proposes a reference steering wheel torque map and a torque tracking algorithm via steer-by-wire to achieve the targeted steering feel. The reference steering wheel torque map is designed using the measurement data of rack force and steering characteristic of a target performance of the vehicle at transition steering test. Since the target performance of the vehicle is only tested in nominal road condition, various road conditions such as disturbances and tire-road friction are not considered. Hence, the measurement data of the rack force that reflects the road conditions in the reference steering wheel torque map have been used. The rack force is the net force which consists of tire aligning moment, road friction force and normal force on the tire kingpin axis. A motor and a magnetorheological damper are used as actuators to generate the desired steering feel using the torque tracking algorithm.

This paper presents a stability monitoring algorithm with a combined slip tire model for maximized cornering speed of high-speed autonomous driving. It is crucial to utilize the maximum tire force with maintaining a grip driving condition in cornering situations. The model-free cruise controller has been designed to track the desired acceleration. The lateral motion has been regulated by the sliding mode controller formulated with the center of percussion. The controllers are suitable for minimizing the behavior errors. However, the high-level algorithm is necessary to check whether the intended motion is inside of the limit boundaries. In extreme diving conditions, the maximum tire force is limited by physical constraints. A combined slip tire model has been applied to monitor vehicle stability. In previous studies, vehicle stability was evaluated only by vehicle acceleration.

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.

This paper describes a robust Model Predictive Control (MPC) framework of lane change for automated driving vehicles. In order to develop a safe lane change for automated driving, the driving mode and lane change direction are determined considering environmental information, sensor uncertainties, and collision risks. The safety margin is calculated using predicted trajectories of surround and subject vehicles. The MPC based combined steering and longitudinal acceleration control law has been designed with extended bicycle model over a finite time horizon. A reachable set of vehicle state is calculated on-line to guarantee that MPC state and input constraints are satisfied in the presence of disturbances and uncertainties. The performance of the proposed algorithm has been conducted simulation studies.

This paper proposes a rear-wheel steering control method that can modify and improve the vehicle lateral response without tire model and parameter. The proposed control algorithm is a combination of steady-state and transient control. The steady state control input is designed to modify steady-state yaw rate response of the vehicle, i.e. understeer gradient of the vehicle. The transient control input is a feedback control to improve the transient response when the vehicle lateral behavior builds up. The control algorithm has been investigated via computer simulations. Compared to classical control methods, the proposed algorithm shows good vehicle lateral response such as small overshoot and fast response. Specifically, the proposed algorithm can alleviate stair-shaped response of the lateral acceleration.

This paper describes design and evaluation of a driving mode decision and lane change control algorithm of automated vehicle in merge situations on highway intersection. For the development of a highly automated driving control algorithm in merge situation, driving mode change from lane keeping to lane change is necessary to merge appropriately. In a merge situation, the driving objective is slightly different to general driving situation. Unlike general situation, the lane change should be completed in a limited travel distance in a merge situation. Merge mode decision is determined based on surrounding vehicles states and remained distance of merge lane. In merge mode decision algorithm, merge availability and desired merge position are decided to change lane safely and quickly. Merge availability and desired merge position are based on the safety distance that considers relative velocity and relative position of subject and surrounding vehicles.

This paper presents an integrated control of in-wheel motor (IWM) and electronic limited slip differential (eLSD) to enhance the vehicle lateral stability and maneuverability. The two actuators are utilized in the proposed controller to achieve separate purposes. The IWM controller is designed to modify the understeer gradient for enhanced handling characteristic and maneuverability. The eLSD controller is devised to improve the lateral stability to prevent oversteer in a severe maneuver. The proposed controller consists of a supervisor, upper-level controller and lower-level controller. The supervisor determines a target motion based on a target understeer gradient for IWM control and a yaw rate reference for eLSD control. The upper-level controller generates a desired yaw moment for the target motion. In the lower-level controller, the desired yaw moment is converted to the control inputs for IWMs in the two front wheels and eLSD at the rear axle.

This paper presents an integrated chassis control method for vehicle stability under various road friction conditions without information on tire-road friction. For vehicle stability, vehicle with an integrated chassis control needs to cope with the various road friction conditions. One of the chassis control method under various road conditions is to determine and/or limit control inputs based on tire-road friction coefficient. The tire-road friction coefficient, however, is difficult to estimate and still a challenging task. The key idea for the proposed method without the estimation of the tire-road friction coefficient is to analyze and control vehicle states based on a tire slip angle - tire force phase plane, i.e. based on these vehicle responses: tire forces and tire slip angles of front/rear wheels.

This paper describes an Integrated Chassis Control (ICC) strategy for improving high speed cornering performance by integration of Electronics Stability Control (ESC), Four Wheel Drive (4WD), and Active Roll Control System (ARS). In this study, an analysis of various chassis modules was conducted to prove the control strategies at the limits of handling. The analysis is focused to maximize the longitudinal velocity for minimum lap time and ensure the vehicle lateral stability in cornering. The proposed Integrated Chassis Control algorithm consists of a supervisor, vehicle motion control algorithms, and a coordinator. The supervisor monitors the vehicle status and determines desired vehicle motions such as a desired yaw rate, longitudinal acceleration and desired roll motion. The target longitudinal acceleration is determined based on the driver's intention and vehicle current state to ensure the vehicle lateral stability in high speed maneuvering.

This paper describes a hierarchical motion planning and control framework for overtaking maneuvers under racing circumstances. Unlike urban or highway autonomous driving conditions, race track driving requires longer prediction and planning horizons in order to respond to upcoming corners at high speed. In addition, the subject vehicle should determine the optimal action among possible driving modes when opponent vehicles are present. In order to meet these requirements and secure real time performance, a hierarchical architecture for decision making, motion planning, and control for an autonomous racing vehicle is proposed. The supervisor determines whether the subject vehicle should stay behind the preceding vehicle or overtake, and its direction when overtaking. Next, a high level trajectory planner generates the desired path and velocity profile in a receding horizon fashion.

This paper presents an Interacting Multiple Model (IMM) based side slip angle estimation method to estimate side slip angle under various road conditions for vehicle stability control. Knowledge of the side slip angle is essential enhancing vehicle handling and stability. For the estimation of the side slip angles in previous researches, prior knowledge of tire parameters and road conditions have been employed, and sometimes additional sensors have been needed. These prior knowledge and additional sensors, however, necessitates many efforts and make an application of the estimation algorithm difficult. In this paper, side slip angle has been estimated using on-board vehicle sensors such as yaw rate and lateral acceleration sensors. The proposed estimation algorithm integrates the estimates from multiple Kalman filters based on the multiple models with different parameter set.

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 development and performance verification of a driving control algorithm for a 6 wheel driving and 6 wheel steering (6WD/6WS) vehicle using a real-time simulator. This control algorithm is developed to improve vehicle stability and maneuverability under high speed driving conditions. The driving controller consists of stability decision, upper, lower level and wheel slip controller. The stability decision algorithm determines desired longitudinal acceleration and reference yaw rate in order to maintain lateral and roll stability using G-vectoring method. Upper level controller is designed to obtain reference longitudinal net force, yaw moment and front/middle steering angles. The longitudinal net force is calculated to satisfy the reference longitudinal acceleration by the PID control theory. The reference yaw moment is determined to satisfy the reference yaw rate using sliding control theory. Lower level controller determines distributed tractive/braking torques.

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 the integrated chassis control(ICC) of four-wheel drive(4WD), electronic stability control(ESC), electronic control suspension(ECS), and active roll stabilizer(ARS) for limit handling. The ICC consists of three layers: 1) a supervisor determines target vehicle states; 2) upper level controller calculates generalized forces; 3) lower level controller, which is contributed in this paper, optimally allocates the generalized force to chassis modules. The lower level controller consists of two integrated parts, 1) longitudinal force control part (4WD/ESC) and 2) vertical force control part (ECS/ARS). The principal concept of both algorithms is optimally utilizing the capability of the each tire by monitoring tire saturation, with tire combined slip. By monitoring tire saturation, 4WD/ESC integrated system minimizes the sum of the tire saturation, and ECS/ARS integrated system minimizes the variance of the tire saturation.

A methodology to design a model free cruise control algorithm(MFCC) is presented in this paper. General cruise control algorithms require lots of vehicle parameters to control the power train and the brake system, that makes control system complicate. Moreover, when the target vehicle is changed, the vehicle parameters should be reinvestigated in order to apply the cruise control algorithm to the subject vehicle. To overcome these disadvantages of the conventional cruise control algorithm, MFCC algorithm has been developed. The algorithm directly determines the throttle, brake inputs based on the reference model parameters such as clearance, relative velocity, and subject vehicle acceleration. This simple structure facilitates human centered design of cruise controller and makes it easy to apply control algorithm to various vehicles without reinvestigation of vehicle parameters.

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.