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As an effective approach for the design, implementation and test of control systems, hardware-in-the-loop (HIL) test has been used in many research areas. This paper describes a real-time HIL simulation test for an automotive electronic control system. The HIL system proposed in this paper consists of three parts: real-time target hardware, electronic control unit (ECU) of the automotive electronic control systems and a signal-conditioning unit which regulates the voltage levels between real-time target and ECU. The HIL simulation evaluates mechanical and electronic behaviors in real time using off-line simulation models by interfacing real-target with electrical control units via interface box. The model has been developed by MATLAB/Simulink. The model is composed of mechanical part which predicts dynamic behaviors and electronic part to calculate the motor speeds, current and electronic loads under the various conditions.

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 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 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 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 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.

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 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.