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公开(公告)号：US09682707B1
公开(公告)日：2017-06-20
申请号：US14837548
申请日：2015-08-27
Applicant: Google Inc.
Inventor： David Harrison Silver , David Ian Franklin Ferguson
CPC classification number: B60W30/16 , B60W2550/308 , G05D1/0088 , G06K9/00805
Abstract: Aspects of the disclosure relate controlling autonomous vehicles or vehicles having an autonomous driving mode. More particularly, these vehicles may identify and respond to other vehicles engaged in a parallel parking maneuver by receiving sensor data corresponding to objects in an autonomous vehicle's environment and including location information for the objects over time. An object corresponding to another vehicle in a lane in front of the first vehicle may be identified from the sensor data. A pattern of actions of the other vehicle identified form the sensor data is used to determine that the second vehicle is engaged in a parallel parking maneuver based on a pattern of actions exhibited by the other vehicle identified from the sensor data. The determination is then used to control the autonomous vehicle.
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公开(公告)号：US09321461B1
公开(公告)日：2016-04-26
申请号：US14472795
申请日：2014-08-29
Applicant: Google Inc.
Inventor： David Harrison Silver , David Ian Franklin Ferguson
CPC classification number: G05D1/0088 , B60W30/0956 , B60W30/10 , B62D6/00 , G01C21/32 , G05D1/0214 , G08G1/16
Abstract: Aspects of the disclosure relate to determining whether a feature of map information. For example, data identifying an object detected in a vehicle's environment and including location coordinates is received. This information is used to identify a corresponding feature from pre-stored map information based on a map location of the corresponding feature. The corresponding feature is defined as a curve and associated with a tag identifying a type of the corresponding object. A tolerance constraint is identified based on the tag. The curve is divided into two or more line segments. Each line segment has a first position. The first position of a line segment is changed in order to determine a second position based on the location coordinates and the tolerance constraint. A value is determined based on a comparison of the first position to the second position. This value indicates a likelihood that the corresponding feature has changed.
Abstract translation: 本公开的方面涉及确定地图信息的特征。 例如，接收识别在车辆环境中检测到的物体并包括位置坐标的数据。 该信息用于基于相应特征的地图位置来从预先存储的地图信息中识别对应的特征。 相应的特征被定义为曲线并且与标识相应对象的类型的标签相关联。 基于标签识别公差约束。 该曲线分为两个或更多个线段。 每个线段具有第一位置。 改变线段的第一位置以便基于位置坐标和公差约束确定第二位置。 基于第一位置与第二位置的比较来确定值。 此值表示相应功能已更改的可能性。
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公开(公告)号：US09684836B1
公开(公告)日：2017-06-20
申请号：US14939232
申请日：2015-11-12
Applicant: Google Inc.
Inventor： David Ian Franklin Ferguson , David Harrison Silver
CPC classification number: G06K9/00798 , B60W30/12 , G05D1/0088 , G05D1/0246 , G06K9/209 , G06K9/6202 , G06K9/6288 , G06T7/74 , G06T2207/30256
Abstract: A vehicle is provided that may combine multiple estimates of an environment into a consolidated estimate. The vehicle may receive first data indicative of the region of interest in an environment from a sensor of the vehicle. The first data may include a first accuracy value and a first estimate of the region of interest. The vehicle may also receive second data indicative of the region of interest in the environment, and the second data may include a second accuracy value and a second estimate of the region of interest. Based on the first data and the second data, the vehicle may combine the first estimate of the region of interest and the second estimate of the region of interest.
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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
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。
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公开(公告)号：US09557736B1
公开(公告)日：2017-01-31
申请号：US14681424
申请日：2015-04-08
Applicant: Google Inc.
Inventor： David Harrison Silver , Jur Pieter Van Den Berg , Nathaniel Fairfield
CPC classification number: G05D1/0231 , G05D1/0088 , G05D1/0234 , G06K9/00812 , G08G1/015 , G08G1/052 , G08G1/09626 , G08G1/096725 , G08G1/096758 , G08G1/096783 , G08G1/165
Abstract: Aspects of the disclosure relate to an autonomous vehicle that may detected other nearby vehicles and identify them as parked or unparked. This identification may be based on visual indicia displayed by the detected vehicles as well as traffic control factors relating to the detected vehicles. Detected vehicles that are in a known parking spot may automatically be identified as parked. In addition, detected vehicles that satisfy conditions that are indications of being parked may also be identified as parked. The autonomous vehicle may then base its control strategy on whether or not a vehicle has been identified as parked or not.
Abstract translation: 本公开的方面涉及可以检测其他附近车辆并将其识别为停放或未起飞的自主车辆。 该识别可以基于由检测到的车辆显示的视觉标记以及与检测到的车辆相关的交通控制因素。 已知停车位的检测车辆可能会自动被识别为停放。 此外，满足待停车指示的条件的检测车辆也可以被识别为停放。 然后，自主车辆可以基于其车辆是否已经被识别为停放的控制策略。
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Patent Agency Ranking
公开(公告)号：US20160187887A1
公开(公告)日：2016-06-30
申请号：US15062853
申请日：2016-03-07
Applicant: Google Inc.
Inventor： David Ian Ferguson , David Harrison Silver
CPC classification number: G05D1/0212 , B60W30/12 , B60W40/00 , G01S19/48 , G05D1/00 , G05D1/021 , G05D1/0231 , G05D1/0246 , G05D1/0274 , G06K9/00798
Abstract: Disclosed herein are methods and systems for using prior maps for estimation of lane boundaries or other features within an environment. An example method may include receiving a location of a plurality of detected points on a roadway in an environment of an autonomous vehicle, determining, from a prior map of the roadway, a location of a plurality of reference points from a boundary marker on the roadway that correspond to the detected points on the roadway, determining distances between the detected points and the corresponding reference points based on the location of the detected points in the environment and the location of the reference points from the prior map of the roadway, determining a confidence buffer representing a threshold amount of variation associated with the prior map based at least in part on the distances between the detected points and the corresponding reference points, selecting one or more of the detected points such that the distance between a selected detected point and a corresponding reference point is less than the confidence buffer, and using the selected points to direct the autonomous vehicle along the roadway.
Abstract translation: 这里公开了用于使用先前的地图估计车道边界或环境内的其他特征的方法和系统。 示例性方法可以包括在自主车辆的环境中的道路上接收多个检测点的位置，从道路的先前地图确定多个参考点从道路上的边界标记的位置 对应于道路上的检测点，基于环境中检测到的点的位置和来自先前的道路图的参考点的位置来确定检测点与相应参考点之间的距离，确定置信度 缓冲器至少部分地基于所检测的点与相应的参考点之间的距离表示与先前映射相关联的变化阈值量，选择一个或多个检测点，使得所选择的检测点与对应的点之间的距离 参考点小于置信缓冲区，并使用选定的点来指导自治 车辆沿道路。
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公开(公告)号：US09285230B1
公开(公告)日：2016-03-15
申请号：US14135818
申请日：2013-12-20
Applicant: Google Inc.
Inventor： David Harrison Silver , David Ian Ferguson
CPC classification number: G01C21/26 , G01S13/86 , G01S13/865 , G01S13/867 , G01S13/89 , G01S13/931 , G01S17/023 , G01S17/89 , G01S17/936 , G01S2013/9382 , G01S2013/9389 , G01S2013/9396 , G06K9/00798
Abstract: Methods and systems for detecting road curbs are described herein. A vehicle's computing system may receive point clouds collected in an incremental order as the vehicle navigates a path. The point clouds may include data points representative of the vehicle's environment at a given timepoint and include associated position information indicative of the vehicle's position at the timepoint. Based on the associated position information in the point clouds, the computing system may process the point clouds into a dense point cloud representation and may determine features of the representation. The computing system may provide the features to a classification system that is configured to output an estimate of whether the features are representative of a road curb. Based on the output of the classification system, the computing system may determine whether the given data points represent one or more road curbs in the vehicle's environment.
Abstract translation: 本文描述了用于检测路缘的方法和系统。 当车辆导航路径时，车辆的计算系统可以接收以增量顺序收集的点云。 点云可以包括表示在给定时间点处的车辆环境的数据点，并且包括指示车辆在时间点处的位置的相关联的位置信息。 基于点云中的相关位置信息，计算系统可以将点云处理成密点点云表示，并且可以确定表示的特征。 计算系统可以向分类系统提供特征，该分类系统被配置为输出特征是否代表道路路缘的估计。 基于分类系统的输出，计算系统可以确定给定的数据点是否表示车辆环境中的一个或多个道路路缘。
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8.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
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。
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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
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。
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10.发明授权公开(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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