An autonomous driving system based on a precise digital map is developed. The system is implemented to the Hyundai's Tucsan fuel cell car, which has a camera, smart cruise control (SCC) and Blind spot detection (BSD) radars, 4-Layer LiDARs, and a standard GPS module. The precise digital map has various information such as lanes, speed bumps, crosswalks and land marks, etc. They can be distinguished as lane-level. The system fuses sensed data around the vehicle for localization and estimates the vehicle's location in the precise map. Objects around the vehicle are detected by the sensor fusion system. Collision threat assessment is performed by detecting dangerous vehicles on the precise map. When an obstacle is on the driving path, the system estimates time to collision and slow down the speed. The vehicle has driven autonomously in the Hyundai-Kia Namyang Research Center.

This paper presents safety assessment scenarios for cyclist autonomous emergency braking(AEB) system. To assess the safety performance of AEB in real traffic situation with limited number of scenarios, scenario should reflect the characteristics of real traffic collision cases. For this, statistic data of real traffic car-to-cyclist collision in Korea are analyzed. Many types of accidents are listed and categorized based on the movement of vehicle and cycle just before the collision. Then, the characteristics, main issues and limitations of each scenarios are discussed. Not only the test scenario itself but also the cost and time for the test are very important issues for the test scenarios to actually repeat the test for various systems. Also, the performance of AEB can be effected by the algorithm of AEB and the technical limitation of the sensors and hardwares. Therefore, required number of tests, possibility of dummy destruction and other technical issues are discussed for each scenarios. Based on these information, typical scenarios are selected. Also, using this information, vehicle speed range, cyclist speed and collision point are established. Proposed scenarios are verified and modified based on the vehicle test results. vehicle test was evaluated 5 times for each scenarios. Based on this results, final test scenarios are modified and proposed.

KNCAP is a program to evaluate the automobile safety, providing consumer vehicle safety assessment results. The safety evaluation tests are Frontal Impact, Offset Frontal Crash, Side Crash, Side Pole Crash, Rear Impact. This is the study of the offset frontal impact safety evaluation. Currently, IIHS is performing a small overlap test. NHTSA plans to implement the oblique moving deformable barrier test. Euro-NCAP plans to implement a mobile frontal impact test. Simulation is used to compare occupant behavior and injury. We have investigated whether the introduction of the test at KNCAP is necessary. The dummy model used in the simulation was the 50th percentile male Hybrid III dummy.

This paper presents the lateral stability criteria for integrated chassis control. To determine the intervention timing of chassis control system, the lateral stability criteria is needed. The proposed lateral stability criteria is based on velocity-yawrate gain domain to determine whether vehicle is stable. If the yawrate gain violates the proposed criteria, the stability of the vehicle is considered as unstable. Characteristic velocity and critical velocity are employed to distinguish lateral stability criteria. The inside of the two boundaries is stable and the outside is unstable. If yawrate gain of vehicle violates the lateral stability criteria, the chassis control begin to intervene. To validate the lateral stability criteria, both computer simulations and vehicle test are conducted with respect to circular turn scenario. The proposed lateral stability criteria makes it possible to reduce intervention of chassis control system.

In recent days, crash safety system based on vehicle-to-vehicle communication (V2V) has been legislated. This V2V based safety system collects information from nearby vehicles to predict any crash possibility. Thus, it requires accurate and reliable data. Regularly updated features of Basic Safety Message(BSM) will be used to test validity of various elements included in the BSM. Then, the focus was made on whether values of these elements had notable differences compared to previous values. Through this paper, the validation tool was implemented and the result from V2V OBU experiment was used to identify problems in the current model and additional features that need to be implemented in V2V OBU for more accurate BSM.