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首页> 外国专利> DRIVE BEHAVIOR ESTIMATING DEVICE, DRIVE SUPPORTING DEVICE, VEHICLE EVALUATING SYSTEM, DRIVER MODEL MAKING DEVICE, AND DRIVE BEHAVIOR JUDGING DEVICE

DRIVE BEHAVIOR ESTIMATING DEVICE, DRIVE SUPPORTING DEVICE, VEHICLE EVALUATING SYSTEM, DRIVER MODEL MAKING DEVICE, AND DRIVE BEHAVIOR JUDGING DEVICE

机译：驾驶行为评估设备，驾驶支持设备，车辆评估系统，驾驶模型制作设备以及驾驶行为判断设备

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A driver model with higher precision is created as an evaluation standard for a driving condition in a normal condition. Further, a driving action is estimated using a driver model which can be created easily and can represent driving characteristics of a driver more precisely.;By detecting biometric information of a driver, whether a driver is in a usual condition or not is recognized. Then, data of driving conditions (own vehicle information such as, for example, operation amounts of accelerator, brake, and steering wheel, vehicle speed, inter-vehicle distance, acceleration, and the like) are collected while the driver is driving, and from the driving condition data, a part indicating that the driver operates in a usual condition is extracted to create a driver model. Thus, without making the driver aware, a driver model for normal times can be created automatically. Further, the driver model is created taking only a case of driving in a normal condition as a driving action in normal times based on biometric information of the driver, and hence the driver model becomes more precise and neutral.;Further, by using a GMM (Gaussian mixture model) for the driver model, a driver model for each driver can be created easily, and moreover, by calculation to maximize a conditional probability, a driving operation action is easily estimated and outputted.
机译：创建具有更高精确度的驾驶员模型作为正常情况下驾驶条件的评估标准。此外，使用驾驶员模型来估计驾驶行为，该驾驶员模型可以容易地创建并且可以更精确地表示驾驶员的驾驶特性。通过检测驾驶员的生物特征信息，识别驾驶员是否处于正常状况。然后，在驾驶员驾驶时收集驾驶条件的数据（诸如加速器，制动器和方向盘的操作量，诸如车辆速度，车辆间距离，加速度等的自身车辆信息），并且从驾驶状态数据中提取表示驾驶员在通常状态下操作的部分，以创建驾驶员模型。因此，在不通知驾驶员的情况下，可以自动创建正常时间的驾驶员模型。此外，基于驾驶员的生物特征信息仅以正常情况下的驾驶情况作为正常时间的驾驶动作来创建驾驶员模型，因此驾驶员模型变得更加精确和中立。对于驾驶员模型（高斯混合模型），可以容易地创建每个驾驶员的驾驶员模型，此外，通过最大化条件概率的计算，可以容易地估计和输出驾驶员的驾驶行为。

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