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    Determining the stationary state of detected vehicles
    11.
    发明授权
    Determining the stationary state of detected vehicles 有权
    确定检测到的车辆的静止状态

    公开(公告)号：US09558659B1

    公开(公告)日：2017-01-31

    申请号：US14523253

    申请日：2014-10-24

    Applicant: Google Inc.

    Inventor： David Harrison Silver ,  David Ian Franklin Ferguson

    IPC: B60W30/00 ,  G08G1/01

    CPC classification number: G08G1/0125 ,  B60W30/00 ,  G06K9/00805 ,  G06K9/00825 ,  G08G1/09623 ,  G08G1/09626 ,  G08G1/096725 ,  G08G1/096758 ,  G08G1/096783

    Abstract: Aspects of the disclosure relate to an autonomous vehicle that may detect other nearby vehicles and designate stationary vehicles as being in one of a short-term stationary state or a long-term stationary state. This determination may be made based on various indicia, including visible indicia displayed by the detected vehicle and traffic control factors relating to the detected vehicle. For example, the autonomous vehicle may identify a detected vehicle as being in a long-term stationary state based on detection of hazard lights being displayed by the detected vehicle, as well as the absence of brake lights being displayed by the detected vehicle. The autonomous vehicle may then base its control strategy on the stationary state of the detected vehicle.

    Abstract translation: 本公开的方面涉及可以检测其他附近车辆并且将固定车辆指定为处于短期静止状态或长期静止状态之一的自主车辆。 该确定可以基于各种标记进行，包括由检测到的车辆显示的可见标记和与检测到的车辆有关的交通控制因素。 例如，自主车辆可以基于检测到的车辆显示的危险灯的检测以及检测到的车辆显示的刹车灯的情况，将检测到的车辆识别为处于长期静止状态。 然后，自主车辆可以将其控制策略基于检测到的车辆的静止状态。

    REAL-TIME ACTIVE EMERGENCY VEHICLE DETECTION
    12.
    发明申请
    REAL-TIME ACTIVE EMERGENCY VEHICLE DETECTION 审中-公开
    实时主动应急车辆检测

    公开(公告)号：US20160252905A1

    公开(公告)日：2016-09-01

    申请号：US14471640

    申请日：2014-08-28

    Applicant: Google Inc.

    Inventor： Yuandong Tian ,  Wan-Yen Lo ,  David Ian Franklin Ferguson

    IPC: G05D1/00

    CPC classification number: G06K9/00825 ,  B60W30/00

    Abstract: A system and method is provided for detecting and responding to emergency vehicles. In one aspect, one or more computing devices may identify a set of light sources from an image based at least in part on one or more templates, and may filter the set of light sources in order to identify one or more light sources corresponding to a potential emergency vehicle. Moreover, the one or more computing devices may determine whether any of the one or more light sources is flashing and whether any of the one or more light sources is associated with a particular type of the potential emergency vehicle. Further, the one or more computing devices may maneuver a vehicle based on the determination to yield in response to at least one of the one or more flashing light sources and the particular type of the emergency vehicle.

    Abstract translation: 提供了一种用于检测和应对紧急车辆的系统和方法。 在一个方面，一个或多个计算设备可以至少部分地基于一个或多个模板从图像识别一组光源，并且可以对该组光源进行滤波，以便识别与一个或多个光源对应的一个或多个光源 潜在的应急车辆 此外，一个或多个计算设备可以确定一个或多个光源中的任何一个是否闪烁，以及一个或多个光源中的任何一个是否与特定类型的潜在应急车辆相关联。 此外，一个或多个计算设备可以基于响应于一个或多个闪光光源和特定类型的紧急车辆中的至少一个的确定来操纵车辆。

    Construction object detection
    13.
    发明授权
    Construction object detection 有权
    施工对象检测

    公开(公告)号：US09424475B1

    公开(公告)日：2016-08-23

    申请号：US14488792

    申请日：2014-09-17

    Applicant: Google Inc.

    Inventor： Wan-Yen Lo ,  David Ian Franklin Ferguson

    IPC: G06K9/00 ,  G06K9/46

    CPC classification number: G06K9/00805 ,  G06K9/00536 ,  G06K9/00791 ,  G06K9/00798 ,  G06K9/00818 ,  G06K9/00825 ,  G06K9/4604 ,  G06K9/4652

    Abstract: Aspects of the disclosure relate to identifying construction objects. As an example, an image captured by a camera associated with a vehicle as the vehicle is driven along a roadway may be received. This image may be converted into a first channel corresponding to an average brightness contribution from red, blue and green channels of the image. The image may also be converted into a second channel corresponding to a contribution of a color from the red and the green channels of the image. A template may then be used to identify a region of the image corresponding to a potential construction object from the first channel and the second channel.

    Abstract translation: 本公开的方面涉及识别施工对象。 作为示例，可以接收通过与作为车辆的车辆相关联的照相机拍摄的图像沿着道路被驱动。 该图像可以被转换成对应于图像的红色，蓝色和绿色通道的平均亮度贡献的第一通道。 图像也可以被转换成对应于来自图像的红色和绿色通道的颜色的贡献的第二通道。 然后可以使用模板来识别来自第一通道和第二通道的对应于潜在施工对象的图像的区域。

    Real-time road flare detection using templates and appropriate color spaces
    14.
    发明授权
    Real-time road flare detection using templates and appropriate color spaces 有权
    使用模板和适当的颜色空间实时道路耀斑检测

    公开(公告)号：US09286520B1

    公开(公告)日：2016-03-15

    申请号：US13942747

    申请日：2013-07-16

    Applicant: Google Inc.

    Inventor： Wan-Yen Lo ,  David Ian Franklin Ferguson ,  Abhijit Ogale

    IPC: H04N7/00 ,  G06K9/00

    CPC classification number: G06K9/00798 ,  B60R1/00 ,  G06K9/00791 ,  G06K9/00805 ,  G06K9/00845 ,  G06K9/4647 ,  G06K9/4652 ,  G06K9/52 ,  G06K9/6215 ,  G06K2009/4666 ,  G06T7/20 ,  G06T7/408 ,  G06T2207/30256

    Abstract: Methods and systems for real-time road flare detection using templates and appropriate color spaces are described. A computing device of a vehicle may be configured to receive an image of an environment of the vehicle. The computing device may be configured to identify a given pixels in the plurality of pixels having one or more of: (i) a red color value greater than a green color value, and (ii) the red color value greater than a blue color value. Further, the computing device may be configured to make a comparison between one or more characteristics of a shape of an object represented by the given pixels in the image and corresponding one or more characteristics of a predetermined shape of a road flare; and determine a likelihood that the object represents the road flare.

    Abstract translation: 描述了使用模板和适当颜色空间进行实时道路耀斑检测的方法和系统。 车辆的计算装置可以被配置为接收车辆的环境的图像。 计算设备可以被配置为识别具有以下中的一个或多个的多个像素中的给定像素：（i）大于绿色值的红色值，以及（ii）大于蓝色值的红色值 。 此外，计算装置可以被配置为对由图像中的给定像素表示的对象的形状的一个或多个特征与道路耀斑的预定形状的相应一个或多个特征进行比较; 并确定对象表示道路耀斑的可能性。

    Filtering noisy/high-intensity regions in laser-based lane marker detection
    15.
    发明授权
    Filtering noisy/high-intensity regions in laser-based lane marker detection 有权
    在基于激光的车道标记检测中过滤噪声/高强度区域

    公开(公告)号：US09261881B1

    公开(公告)日：2016-02-16

    申请号：US13956871

    申请日：2013-08-01

    Applicant: Google Inc.

    Inventor： David Ian Franklin Ferguson ,  David Harrison Silver

    IPC: G01C22/00 ,  G05D1/02

    CPC classification number: B60W30/12 ,  B60W10/04 ,  B60W10/18 ,  B60W10/20 ,  B60W30/10 ,  B60W30/18163 ,  B60W2420/42 ,  B60W2420/52 ,  B60W2550/10 ,  B60W2550/12 ,  B60W2710/18 ,  B60W2710/20 ,  B60W2720/10 ,  B60W2900/00 ,  G01S17/936 ,  G05D1/0088 ,  G05D1/0236 ,  G05D2201/0213

    Abstract: An autonomous vehicle may be configured to receive, using a computer system, a plurality of remission signals from a portion of a lane of travel in an environment in response to at least one sensor of the vehicle sensing the portion of the lane of travel. A given remission signal of the plurality of remission signals may include a remission value indicative of a level of reflectiveness for the portion of the lane of travel. The vehicle may also be configured to compare the plurality of remission signals to a known remission value indicative of a level of reflectiveness for a lane marker in the lane of travel. Based on the comparison, the vehicle may additionally be configured to determine whether the portion of the lane of travel in the environment is indicative of a presence of the lane marker.

    Abstract translation: 自主车辆可以被配置为响应于感测车道行驶部分的车辆的至少一个传感器，使用计算机系统从环境中的行驶车道的一部分接收多个缓冲信号。 多个缓解信号的给定缓解信号可以包括指示行进路线部分的反射程度的缓解值。 车辆还可以被配置为将多个缓解信号与已知的缓冲值进行比较，所述已知缓解值指示行车道中的车道标记的反射水平。 基于比较，车辆可以另外被配置为确定环境中的行驶车道的部分是否指示车道标记的存在。

    Predicting trajectories of objects based on contextual information
    16.
    发明授权
    Predicting trajectories of objects based on contextual information 有权
    基于上下文信息预测对象的轨迹

    公开(公告)号：US09248834B1

    公开(公告)日：2016-02-02

    申请号：US14505007

    申请日：2014-10-02

    Applicant: Google Inc.

    Inventor： David Ian Franklin Ferguson ,  David Harrison Silver ,  Stéphane Ross ,  Nathaniel Fairfield ,  Ioan-Alexandru Sucan

    IPC: G08G1/16 ,  B60W30/09

    CPC classification number: B60W30/09 ,  B60W30/0953 ,  B60W30/0956 ,  B60W30/18154 ,  B60W2550/10 ,  B60W2550/30 ,  B60W2600/00 ,  B60W2720/24 ,  G05D1/0088 ,  G05D1/0214

    Abstract: Aspects of the disclosure relate to detecting and responding to objects in a vehicle's environment. For example, an object may be identified in a vehicle's environment, the object having a heading and location. A set of possible actions for the object may be generated using map information describing the vehicle's environment and the heading and location of the object. A set of possible future trajectories of the object may be generated based on the set of possible actions. A likelihood value of each trajectory of the set of possible future trajectories may be determined based on contextual information including a status of the detected object. A final future trajectory is determined based on the determined likelihood value for each trajectory of the set of possible future trajectories. The vehicle is then maneuvered in order to avoid the final future trajectory and the object.

    Abstract translation: 本公开的方面涉及检测和响应车辆环境中的物体。 例如，可以在车辆的环境中识别对象，物体具有标题和位置。 可以使用描述车辆环境的地图信息和对象的标题和位置来生成对象的一组可能的动作。 可以基于一组可能的动作来生成对象的一组可能的未来轨迹。 可以基于包括检测到的对象的状态的上下文信息来确定可能的未来轨迹集合中的每个轨迹的似然值。 基于针对可能的未来轨迹集合中的每个轨迹的确定的似然值来确定最终的未来轨迹。 然后车辆被操纵以避免最终的未来轨迹和物体。

    Estimating road lane geometry using lane marker observations
    17.
    发明授权
    Estimating road lane geometry using lane marker observations 有权
    使用车道标记观察估计道路几何

    公开(公告)号：US08948958B1

    公开(公告)日：2015-02-03

    申请号：US14270653

    申请日：2014-05-06

    Applicant: Google Inc.

    Inventor： David Ian Franklin Ferguson ,  Jiajun Zhu

    IPC: G01C21/26 ,  G01S17/89 ,  G05D1/00 ,  B60W10/20 ,  G05D1/02

    CPC classification number: G05D1/00 ,  B60W10/20 ,  B60W2710/20 ,  G01S17/89 ,  G05D1/021

    Abstract: Aspects of the disclosure relate generally to detecting the edges of lane lines. Specifically, a vehicle driving on a roadway may use a laser to collect data for the roadway. A computer may process the data received from the laser in order to extract the points which potentially reside on two lane lines defining a lane. The extracted points are used by the computer to determine a model of a left lane edge and a right lane edge for the lane. The model may be used to estimate a centerline between the two lane lines. All or some of the model and centerline estimates, may be used to maneuver a vehicle in real time and also to update or generate map information used to maneuver vehicles.

    Abstract translation: 本公开的方面通常涉及检测车道线的边缘。 特别地，在道路上行驶的车辆可以使用激光来收集道路的数据。 计算机可以处理从激光器接收的数据，以便提取可能驻留在限定车道的两条车道线上的点。 提取的点被计算机用于确定车道的左车道边缘和右车道边缘的模型。 该模型可用于估计两条车道线之间的中心线。 所有或一些模型和中心线估计可用于实时地操纵车辆，并且还可以更新或生成用于操纵车辆的地图信息。

    Object and ground segmentation from a sparse one-dimensional range data
    19.
    发明授权
    Object and ground segmentation from a sparse one-dimensional range data 有权
    物体和地面分割从稀疏的一维范围数据

    公开(公告)号：US08825260B1

    公开(公告)日：2014-09-02

    申请号：US13948207

    申请日：2013-07-23

    Applicant: Google Inc.

    Inventor： David Harrison Silver ,  David Ian Franklin Ferguson

    IPC: G01S17/93

    CPC classification number: G01S17/936 ,  B60W30/00 ,  G01S13/86 ,  G01S13/865 ,  G01S13/931 ,  G01S17/023 ,  G01S2013/936

    Abstract: Methods and systems for object and ground segmentation from a sparse one-dimensional range data are described. A computing device may be configured to receive scan data representing points in an environment of a vehicle. The computing device may be configured to determine if a test point in the scan data is likely to be an obstacle or ground by comparing the point to other points in the scan data to determine if specific constraints are violated. Points that do not pass these tests are likely to be above the ground, and therefore likely belong to obstacles.

    Abstract translation: 描述了从稀疏一维范围数据对象和地面分割的方法和系统。 计算设备可以被配置为接收表示车辆环境中的点的扫描数据。 计算设备可以被配置为通过将点与扫描数据中的其他点进行比较来确定扫描数据中的测试点是否可能是障碍物或地面，以确定是否违反特定的约束。 不通过这些测试的点可能在地面以上，因此可能属于障碍物。

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