Drowsiness estimation from identified driver model

This paper proposes an estimation method for a driver\u27s drowsiness that uses an identified driver model. In the previous report, it was shown that a driver\u27s involuntary response to a car\u27s motion can be extracted by pre-filtering and provided to the real-time identification method. The identified driver model contains meaningful parameters related to the driver\u27s characteristics. In the present research, the correlation between the identified driver model and drowsiness was investigated using the results of an actual driving test. Then, the drowsiness-estimation function was constructed which evaluated the delay, gain, uncertainties, and time variation of the steering behaviour of the identified driver model. The driving test showed that the proposed method had sufficient precision to provide a warning of inattentive driving. © 2015 The Author(s). Published by Taylor & Francis

Real-time identification method of driver model with steering manipulation

This study proposes a method for real-time identification of a driver model. The proposed method requires only the yaw rate sensor, the steering angle sensor, and velocity sensors that are usually installed in the production car. The identification algorithm involves the division of the recorded data, prefiltering of the divided data, estimation of the driver\u27s desired response, and identification. The prefilter extracts the driver\u27s involuntary response that can be modelled in a simple form. The ideal car response that the driver attempts to track is estimated from the recorded data, and this response is provided to the identification algorithm of the feedback driver model for error tracking. These newly developed methods enable real-time identification under actual driving conditions. The driving simulator experiments and the actual driving tests were performed, and the proposed method was validated. The results show that the time history of the variation in the driver\u27s characteristics can be realised in real time using the proposed method. © 2013 Copyright Taylor and Francis Group, LLC

パイロットモデルのオンライン同定を核とした小型機・無人航空機の安全性向上システム

金沢大学理工研究域フロンティア工学系ドローンや自動運転自動車などの操縦者をリアルタイムモデリングし，モデルパラメータから操縦者状態を推定する手法の検討を行った．機体の運動や周囲環境の変化に対する操縦者の振る舞いをモデル化し，そのモデルパラメータで操縦者状態を評価する．操縦シミュレータ実験を行い，操縦者モデルを同定し解析した結果，ワークロード量とモデルの残差，モデル変動などとの相関があることが明らかになった．既存手法は心拍などの生体信号を計測してその変化を評価する．一方提案手法は，刺激に対する操縦者ダイナミクスの応答を評価する手法であるため，車両振動や外部環境の変化に適切に反応できているか直接評価することができる．The estimation algorithm of the operator condition using identified operator mode was discussed. The operator models of the drone and self-driving car were identified as a response model to the vehicle motion and the condition change around the car such as a distance to the forward car. The output of the operator model are control behavior and the movement of the viewpoint. These model structures enable to analyze the inner condition of the human operator.The flight and driving simulator were conducted and the operating experiments of a drone and self-driving car with and without subtask were performed. The relations between the identified model and the workload imposed by the subtask were analyzed. The result indicated that the residual and the model variations of the identified model have correlations with the workload.研究課題/領域番号:26420810, 研究期間(年度):2014-04-01 - 2017-03-3

Flight control adaptation of bumblebee Bombus ignitus

金沢大学 理工研究域 フロンティア工学

Flight Controller Design and Autonomous Flight Tests of 60cm-sized UAV

This paper describes the design of flight controller for a fixed-wing small-sized UAV and the autonomous flight tests. The UAV which has been developed by the authors has 0.6m span and its weight is 0.27kg. It cruises at 6-12m/s. The flight controller is composed of attitude stability augment systems, feedforward filters, and guidance systems. The flight controller is designed for longitudinal and lateral-directional motions, separately. The UAV has advantages in safety and portability due to its light weight and low cruising speed. In order to ensure robust stability, the attitude stability augment systems are designed for these motions with µ-synthesis. The feedforward filters are designed in order to shape the command from guidance system to the inner closed-loop appropriately. The longitudinal guidance system, which is designed with PID-control manner, keeps the UAV at a desired altitude. The lateral-directional guidance system guides the UAV to the pre-defined waypoints with avoiding known obstacles. It is designed using the Artificial Potential Field Method. These flight controllers are implemented on the small on-board computer we have also developed. Autonomous flight experiments show that the developed UAV is able to fly autonomously, passing over pre-defined waypoints, and that the UAV has the ability of avoiding the known obstacle