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Study on Effectiveness of Active Safety Devices on Low Friction Road Using Driving Simulator
Yoshimichi Terashima,Yuya Ishizaki,Ryuzo Hayashi,Pongsathorn Raksincharoensak,Masao Nagai 한국자동차공학회 2008 한국자동차공학회 Symposium Vol.2008 No.9
In recent years, various active safety devices have been developed for the prevention of accidents such as rear-end collision and skidding. The representative device for the collision prevention is the forward collision warning system, and the one for the stability control is the direct yaw-moment control (DYC) system using distributed braking forces. In this study, we aim to evaluate the performance of these devices by experiments using driving simulator reconducting critical and dangerous driving situations. Regarding the forward collision warning system, we propose a new algorithm of warning system adapted to road condition, and conduct experiments to verify its effectiveness with the driving simulator. The results of the experiment indicate that the proposed system is more effective than the conventional system. Regarding the DYC, we develop a DYC system for the driving simulator to verify the effectiveness of DYC on low friction road, including human driver in the closed-loop evaluation. The DYC algorithm is designed by model-matching control method. From the results of the experiment of obstacle avoidance, it reveals that the DYC system is very effective to stabilize the vehicle on low friction road.
Development of Steering Behavior Recognition Method by Using Sensing Data of Drive Recorder
Hideki Tsunai,Kozo Maeda,Ryuzo Hayashi,Pongsathorn Raksincharoensak,Masao Nagai 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
This paper proposes the steering behavior, e.g. lane keeping, lane changing, recognition method by using sensing data of drive recorder. To reduce traffic accidents, it is necessary to improve not only active safety technology, but also the driver awareness about driving safety. One of the methods to increase the driver awareness is to inform the degree of safety of driving behavior. To realize the system, it is necessary to develop the driving behavior recognition algorithm by sensing data of drive recorder. Therefore, this study focuses on lane change recognition method and develops the algorithm by sequential labeling method based on boosting framework; Boosted Conditional Random Fields. To develop the algorithm, four features are focused here to train the model: those are, velocity, steering wheel angle, moving variance and moving standard deviation. Finally, the recognition results are shown, and the best features in machine learning process for recognition algorithm are examined.