Abstract-This paper presents the development of the modeling and recognition of human driving behavior based on a stochastic switched auto-regressive exogenous (SS-ARX) model. First, a parameter estimation algorithm for the SS-ARX model with multiple measured input-output sequences is developed based on the expectation-maximization (EM) algorithm. This can be achieved by extending the parameter estimation technique for the conventional hidden Markov model (HMM). Second, the developed parameter estimation algorithm is applied to driving data with the focus being on driver's collision avoidance behavior. The driving data were collected using a driving simulator based on the CAVE virtual environment, which is a stereoscopic immersive virtual reality (VR) system. Then, the parameter set for each driver is obtained and certain driving characteristics are identified from the viewpoint of switched control mechanism. Finally, the performance of the SS-ARX model as a behavior recognizer is examined. The results show that the SS-ARX model holds remarkable potential to function as a behavior recognizer.
Abstract-This paper presents a modeling strategy of human driving behavior based on the controller switching model focusing on the driver's collision avoidance maneuver. The driving data are collected by using the three-dimensional driving simulator based on CAVE, which provides stereoscopic immersive virtual environment. In our modeling, the control scenario of the human driver, that is, the mapping from the driver's sensory information to the operation of the driver such as acceleration, braking and steering, is expressed by Piecewise Polynomial (PWP) model. Since the PWP model includes both continuous behaviors given by polynomials and discrete logical conditions, it can be regarded as a class of Hybrid Dynamical System (HDS). The identification problem for the PWP model is formulated as the Mixed Integer Linear Programming (MILP) by transforming the switching conditions into binary variables. From the obtained results, it is found that the driver appropriately switches the 'control law' according to the sensory information. Also, the driving characteristics of the beginner driver and the expert driver are compared and discussed. These results enable us to capture not only the physical meaning of the driving skill, but also the decision-making aspect (switching conditions) in the driver's collision avoidance maneuver.
Twenty-four micro-solar cells connected in series have been fabricated on semi-insulating (SI) GaAs substrates for application in the fields of micro-electromechanical systems. The array was formed in an area of 0.8 × 1.0 mm 2 , and exhibited an open-circuit voltage of 22.5 V under illumination of 5 mW at the wavelength of 815 nm. Calculation and experiment has demonstrated that, unlike conventional solar cells, the shunt resistance deteriorates the output characteristics far more seriously than the series resistance in the case of micro-solar cells. Leakage currents in both the unit diode and the substrate were evaluated separately. A quantitative estimate based on the measurements revealed that photocurrents generated in the surface of the SI substrate could function as a vital shunt resistance. Light-blocking metal films were successfully employed to obtain high output voltage.
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