公开(公告)号：US20200377111A1

公开(公告)日：2020-12-03

申请号：US16426689

申请日：2019-05-30

Applicant: Honda Motor Co., Ltd

Inventor： Teruhisa Misu ,  Ashish Tawari ,  Sujitha Catherine Martin

IPC: B60W50/06 ,  G06N20/00

Abstract: A trainer device trains an automated driver system. The trainer device may include a vehicle manager that manages data associated with controlling a vehicle and a simulation manager that manages data associated with simulating the vehicle. The vehicle manager may analyze vehicle data to identify an intervention event, and the simulation manager obtains a portion of the vehicle data corresponding to the intervention event to generate simulation data, obtains user data associated with the simulation data, analyzes the user data to determine whether the user data satisfies a predetermined intervention threshold, and, on condition that the user data satisfies the predetermined intervention threshold, transmits the user data to the vehicle manager for modifying the first control data.

公开(公告)号：US20200039520A1

公开(公告)日：2020-02-06

申请号：US16055798

申请日：2018-08-06

Applicant: Honda Motor Co., Ltd.

Inventor： Teruhisa Misu ,  Yi-Ting Chen

IPC: B60W40/09 ,  G05D1/00 ,  B60W40/04

Abstract: A system and method for learning naturalistic driving behavior based on vehicle dynamic data that include receiving vehicle dynamic data and image data and analyzing the vehicle dynamic data and the image data to detect a plurality of behavioral events. The system and method also include classifying at least one behavioral event as a stimulus-driven action and building a naturalistic driving behavior data set that includes annotations that are based on the at least one behavioral event that is classified as the stimulus-driven action. The system and method further include controlling a vehicle to be autonomously driven based on the naturalistic driving behavior data set.

公开(公告)号：US09809165B1

公开(公告)日：2017-11-07

申请号：US15208223

申请日：2016-07-12

Applicant: HONDA MOTOR CO., LTD.

Inventor： Teruhisa Misu ,  Hajime Yamada

IPC: B60Q1/00 ,  B60Q9/00 ,  B60K37/02 ,  B60R1/00 ,  G06K9/00 ,  G06T19/00

CPC classification number: B60Q9/00 ,  B60K35/00 ,  B60K37/02 ,  B60K2350/2052 ,  B60R1/00 ,  B60R2300/205 ,  B60R2300/301 ,  B60R2300/80 ,  B60R2300/8086 ,  G06K9/00791 ,  G06T19/006

Abstract: A method and vehicle system for reducing driver distractions caused by a head-up display (HUD). The HUD projects one or more graphic elements onto a windshield in view of a driver of the vehicle. The vehicle system determines a content status of the one or more projected graphic elements. The vehicle system detects one or more objects within an environment of the vehicle. The vehicle system determines whether the content status of the one or more projected graphic elements satisfies a driver distraction criterion. The vehicle then notifies the driver of the one or more detected objects when the content status of the one or more projected graphic element satisfies the driver distraction criterion.

公开(公告)号：US20160117947A1

公开(公告)日：2016-04-28

申请号：US14521167

申请日：2014-10-22

Applicant: Honda Motor Co., Ltd.

Inventor： Teruhisa Misu

IPC: G09B9/04 ,  B60W50/14 ,  B60W50/08

CPC classification number: G09B9/04 ,  B60W50/085 ,  B60W50/14 ,  B60W2050/143 ,  B60W2050/146 ,  G09B19/16 ,  G09B19/167

Abstract: In one or more embodiments, driver awareness may be calculated, inferred, or estimated utilizing a saliency model, a predictive model, or an operating environment model. An awareness model including one or more awareness scores for one or more objects may be constructed based on the saliency model or one or more saliency parameters associated therewith. A variety of sensors or components may detect one or more object attributes, saliency, operator attributes, operator behavior, operator responses, etc. and construct one or more models accordingly. Examples of object attributes associated with saliency or saliency parameters may include visual characteristics, visual stimuli, optical flow, velocity, movement, color, color differences, contrast, contrast differences, color saturation, brightness, edge strength, luminance, a quick transient (e.g., a flashing light, an abrupt onset of a change in intensity, brightness, etc.).

Abstract translation: 在一个或多个实施例中，可以使用显着性模型，预测模型或操作环境模型来计算，推断或估计驾驶员意识。 可以基于显着性模型或与其相关联的一个或多个显着性参数来构建包括一个或多个对象的一个​​或多个感知分数的感知模型。 各种传感器或组件可以检测一个或多个对象属性，显着性，操作者属性，操作者行为，操作员响应等，并相应地构建一个或多个模型。 与显着性或显着性参数相关的对象属性的示例可以包括视觉特征，视觉刺激，光流，速度，运动，颜色，色差，对比度，对比度差异，颜色饱和度，亮度，边缘强度，亮度，快速瞬变（例如 ，闪烁的光，强度，亮度等的变化的突然开始）。