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    • 27. 发明申请
    • Driving Action Estimating Device, Driving Support Device, Vehicle Evaluating System, Driver Model Creating Device, and Driving Action Determining Device
    • 驾驶行动估计装置,驾驶支援装置,车辆评估系统,驾驶员模型制作装置及驾驶动作确定装置
    • US20090234552A1
    • 2009-09-17
    • US12087130
    • 2006-12-27
    • Kazuya TakedaKatunobu ItouChiyomi MiyajimaKoji OzawaHirokazu NomotoKazuaki FujiiSeiichi Suzuki
    • Kazuya TakedaKatunobu ItouChiyomi MiyajimaKoji OzawaHirokazu NomotoKazuaki FujiiSeiichi Suzuki
    • B60W40/08B60W30/16
    • B60W40/09B60W30/16G08G1/166G08G1/167
    • 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.
    • 创建具有较高精度的驾驶员模型作为正常状态下驾驶状况的评估标准。 此外,使用可以容易地创建并且可以更准确地表示驾驶员的驾驶特性的驾驶员模型来估计驾驶动作。 通过检测驾驶员的生物体信息,识别驾驶员是否处于通常状态。 然后,在驾驶者驾驶时收集行驶状况(本车辆信息,例如加速器,制动器,方向盘的操作量,车速,车辆间距离,加速度等)的数据,以及 根据驾驶条件数据,提取指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,在不使驱动程序知道的情况下,可以自动创建正常时间的驱动程序模型。 此外,仅在正常状态下驾驶的情况下作为驾驶动作而在正常时间基于驾驶员的生物体信息生成驾驶员模型,因此驾驶员模型变得更加精确和中立。 此外,通过对驱动器模型使用GMM(高斯混合模型),可以容易地创建每个驱动器的驱动器模型,此外,通过计算来最大化条件概率,可以容易地估计和输出驱动操作动作。