This paper describes a Forward Collision Warning (FCW) system based on the radar driven fusion with camera. The objective of FCW system is to provide an appropriate alert with satisfying the evaluation scenarios of US-NCAP and a driver acceptance. For this purpose, this paper proposed a data fusion algorithm and a collision warning algorithm. The data fusion algorithm generates information of fusion target depending on the confidence of camera sensor. The collision warning algorithm calculates indexes and determines an appropriate alert-timing by using analysis results of manual driving data. The FCW system with the proposed data fusion and collision warning algorithm was investigated via scenarios of US-NCAP and a real-road driving. It is shown that the proposed FCW system can improve the accuracy of an alarm-timing and reduce the false alarm in real roads.

Small overlap crash caused fatal injury in real-world crash. IIHS(Insurance Institute for Highway Safety) proposed the small overlap test. The objective of this study is to analyze dummy injury criteria and dummy excursion on the sled reinforced body angle. Result of the comparisons of dummy injury criteria of a head, neck, and chest was best correlation between sled and vehicle test on base $angle+3^{\circ}$. However, lower extremity was not correlation because sled test could not copy of intrusion. There were a correlation between dummy movement and sled reinforced body angle. Sled reinforced body angle affects the lateral direction of excursion more than longitudinal excursion.

Some GM (General Motors) vehicles are using a GMLAN (General Motors Local Area Network) communication protocol for control and diagnostics. The airbag control module uses vehicle speed information from the GMLAN to record the vehicle speed as pre-crash information. In order to use the vehicle speed information for crash reconstruction purposes, it helps to be able to understand the accuracy of the data. The actual vehicle speed is not expected to be the same as the GMLAN indicated speed in some situations like a spin or if there is hard braking. This paper compares the actual vehicle speed and vehicle speed information during specific vehicle maneuvers. Actual vehicle speed is calculated from a GPS sensor, while GMLAN vehicle speed is calculated from transmission output sensor by the Engine control module (ECM). Vehicle maneuvers defined as Mode #1, Mode #2, Mode #3. The Mode #1 maneuver simulates wheel lock-up and skidding f by hard-braking at a specific speed. The Mode #2 maneuver simulates a 90degree turn using a J-turn maneuver at a specific speed. The Mode#3 maneuver simulates a 180 degree turn using a spin type of maneuver at a specific speed. The study then compares the GMLAN speed and GPS speed to see what speed difference exists between them. The results of this paper are applicable to GM vehicles only. This paper catalogs the performance and limitations of two vehicles as useful reference for crash reconstructions where there is a need to understand the speed indicated in the pre-crash section of the SDM data.

The main purpose of this research is to predict dynamic behavior of door locking system for side impact safety and the design process to avoid door opening is introduced. The equations of motion that represent the system are obtained from the energy equation. From them, the motion of door handle is predicted by using Runge-Kutta $4^{th}$ order method and the simulation result is compared with the real crash data. Also, the design guide to define the properties of door locking system from the standpoint of avoiding door opening phenomenon is introduced.

For the robust passenger NCAP 5star and the stable neck injury performance, a new concept of passenger airbag has been required strongly. Especially, the deployment stability and the vent hole control technology of the passenger airbag should be improved. According to these requirements, the deployment stability technique has been studied and the 'Active Vent' technology has been developed. As a result, these technologies have led to achieve the robust NCAP rating and are applied to the production vehicles.

This paper describes an investigation into coordinated control of electronic stability control (ESC) and active roll control system (ARS). The coordinated control is suggested to improve the vehicle stability and agility features by yaw rate control. The proposed integrated chassis control algorithm consists of a supervisor, control algorithms, and a coordinator. The supervisor monitors the vehicle status and determines desired vehicle motions such as a desired yaw rate and desired roll motion based on control modes to improve vehicle stability. According to the corresponding the desired vehicle dynamics, the control algorithm calculated a desired yaw moment and desired roll moment, respectively. Based on the desired yaw moment and the desired roll moment, the coordinator determines the brake pressures and the ARC motor torques based on control strategies. Closed loop simulations with a driver-vehicle-controller system were conducted to investigate the performance of the proposed control strategy using CarSim vehicle dynamics software and the integrated controller coded using Matlab/Simulink.

This paper presents a lateral collision risk index between an ego vehicle and a rear-side vehicle. The lateral collision risk is designed to represent a lateral collision risk and provide the appropriate threshold value of activation of the lateral collision management system such as the Blind Spot Detection(BSD). The lateral collision risk index is designed using the Time to Line Crossing(TLC) and the longitudinal collision index at the predicted TLC. TLC and the longitudinal collision index are calculated with the signals from the exterior sensor such as the radar equipped on the rear-side of a vehicle and a vision sensor which detects the distance and time to the lane departure. For the robust situation assessment, the perception of driving environment determining whether the road is straighten or curved should be determined. The relative motion estimation method has been proposed with the road information via the integrated estimator using the environment sensors and vehicle sensor. A lateral collision risk index was composed with the estimated relative motion considering the relative yaw angle. The performance of the proposed lateral collision risk index is investigated via computer simulations conducted using the vehicle dynamics software CARSIM and Matlab/Simulink.

When it comes to commercial vehicles, their unique characteristics - center of gravity, size, weight distribution - make them particularly vulnerable to rollover. On top of that, conventional heavy vehicle brake exhibits longer actuation delays caused in part by long air lines from brake pedal to tires. This paper describes rollover prevention algorithm that copes with the characteristics of commercial vehicles. In regard of compensating for high actuating delay, predicted rollover index with short preview time has been designed. Moreover, predicted rollover index with longer preview time has been calculated by using road curvature information based on environment information. When rollover index becomes larger than specific threshold value, desired braking force is calculated in order to decrease the index. At the same time, braking force is distributed to each tire to make yaw rate track desired value.

This paper presents a development of the Advanced Emergency Braking (AEB) Algorithm for passenger vehicles. The AEB is the system to slow the vehicle and mitigate the severity of an impact when a rear end collision probability is increased. To mitigate a rear end collision, the AEB comprises of a millimeter wave radar sensor, CCD camera and vehicle parameters of which are processed to judge the likelihood of a collision occurring. The main controller of the AEB algorithm is composed of the two control stage: upper and lower level controller. By using the collected obstacle information, the upper level controller of the main controller decides the control mode based not only on parametric division, but also on physical collision capability. The lower level controller determines warning level and braking level to maintain the longitudinal safety. To decide the braking level, Last Ponit To Brake and Steer (LPTB/LPTS) are compared with current driving statues. To demonstrate the control performance of the proposed AEBS algorithm's, closed-loop simulation of the AEBS was conducted by using the Matlab simlink and CarSim software.

This paper describes development of 4 Wheel Drive (4WD) Electric Vehicle (EV) based driving control algorithm for severe driving situation such as icy road or disturbance. The proposed control algorithm consists three parts : a supervisory controller, an upper-level controller and optimal torque vectoring controller. The supervisory controller determines desired dynamics with cornering stiffness estimator using recursive least square. The upper-level controller determines longitudinal force and yaw moment using sliding mode control. The yaw moment, particularly, is calculated by integration of a side-slip angle and yaw rate for the performance and robustness benefits. The optimal torque vectoring controller determines the optimal torques each wheel using control allocation method. The numerical simulation studies have been conducted to evaluated the proposed driving control algorithm. It has been shown from simulation studies that vehicle maneuverability and lateral stability performance can be significantly improved by the proposed driving controller in severe driving situations.

LKAS(Lane Keeping Assistance System) main function is to support the driver in keeping the vehicle within the current lane. Therefore, this system is able to reduce the driver workload with assisting the driver during driving. In this paper, we presented on study for test procedures and evaluation methods of the LKAS. The vehicle test conducted on straight road, left curve, right curve and four different types of lane under various vehicle speeds. This study proposed the LKAS system test procedures and methods that we are able to identify LKAS driving mechanism and performance.