公开(公告)号：WO2016018636A1

公开(公告)日：2016-02-04

申请号：PCT/US2015/040698

申请日：2015-07-16

Applicant: GOOGLE INC.

Inventor： GUTMANN, Jens-steffen, Ralf ,  WENDEL, Andreas ,  FAIRFIELD, Nathaniel ,  DOLGOV, Dmitri, A. ,  BURNETTE, Donald, Jason

IPC: G05D1/02 ,  G08G1/16

CPC classification number: B60W30/09 ,  G08G1/09623 ,  G08G1/09626 ,  G08G1/096725 ,  G08G1/096783 ,  G08G1/096791

Abstract: Aspects of the disclosure relate to determining whether a vehicle 100 should continue through an intersection 502. For example, the one or more of the vehicle's computers 110 may identify a time when the traffic signal light 526 will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point 350 of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.

Abstract translation: 本公开的方面涉及确定车辆100是否应该通过交叉口502继续。例如，车辆计算机110中的一个或多个可以识别交通信号灯526将从黄色变为红色的时间。 一个或多个计算机还可以在交通信号灯从黄色变为红色时估计车辆的位置。 可以识别交叉点的起始点350。 基于车辆的估计位置是否至少在交通信号灯从黄色变为红色时起始点的阈值距离，计算机可以确定车辆是否应该继续通过交叉路口。

公开(公告)号：WO2014168944A1

公开(公告)日：2014-10-16

申请号：PCT/US2014/033325

申请日：2014-04-08

Applicant: GOOGLE INC.

Inventor： FERGUSON, David Ian Franklin ,  BURNETTE, Donald, Jason

IPC: G08G1/0968 ,  G01C21/00

CPC classification number: G05D1/0214 ,  B60W10/04 ,  B60W10/20 ,  B60W30/00 ,  B60W30/143 ,  B60W2710/20 ,  B60W2720/106 ,  G01C21/26 ,  G01C21/32 ,  G01C21/3407 ,  G01S19/42 ,  G01S2013/936 ,  G05D1/00 ,  G05D1/0088 ,  G05D1/0236 ,  G05D1/0246 ,  G05D1/0255 ,  G05D1/0257 ,  G05D1/0276 ,  G05D2201/0213 ,  G06K9/00 ,  G06K9/00798 ,  G06K9/00818

Abstract: Aspects of the present disclosure relate to differentiating between active and inactive construction zones. In one example, this may include identifying a construction object (810-890) associated with a construction zone. The identified construction object may be used to map the area (910) of the construction zone. Detailed map information (136) may then be used to classify the activity of the construction zone. The area of the construction zone and the classification may be added to the detailed map information. Subsequent to adding the construction zone and the classification to the detailed map information, the construction object (or another construction object) may be identified. The location of the construction object may be used to identify the construction zone and classification from the detailed map information. The classification of the classification may be used to operate a vehicle (101) having an autonomous mode.

Abstract translation: 本公开的方面涉及区分主动和非活动结构区域。 在一个示例中，这可以包括识别与施工区域相关联的施工对象（810-890）。 所识别的施工对象可用于映射施工区域的区域（910）。 然后可以使用详细的地图信息（136）来对施工区域的活动进行分类。 施工区的区域和分类可以添加到详细的地图信息中。 在将施工区域和分类添加到详细地图信息之后，可以识别施工对象（或另一施工对象）。 施工对象的位置可以用于从详细的地图信息中识别施工区域和分类。 分类的分类可以用于操作具有自主模式的车辆（101）。

公开(公告)号：WO2014003860A2

公开(公告)日：2014-01-03

申请号：PCT/US2013/033315

申请日：2013-03-21

Applicant: GOOGLE INC.

Inventor： BURNETTE, Donald, Jason ,  FERGUSON, David, I.

IPC: B60W30/10 ,  B60W30/12

CPC classification number: G06K9/00798 ,  G06K9/2036

Abstract: Aspects of the disclosure relate generally to detecting lane markers. More specifically, laser scan data may be collected by moving a laser (310, 311) along a roadway (500). The laser scan data may include data points (740, 750, 760) describing the intensity and location information of objects within range of the laser. Each beam of the laser may be associated with a respective subset of data points. For a single beam, the subset of data points may be further divided into sections (910, 920, 930). For each section, the average intensity and standard deviation may be used to determine a threshold intensity. A set of lane marker data points may be generated by comparing the intensity of each data point to the threshold intensity for the section in which the data point appears and based on the elevation of the data point. This set may be stored for later use or otherwise made available for further processing.

Abstract translation: 本公开的方面通常涉及检测车道标记。 更具体地，可以通过沿着道路（500）移动激光器（310,311）来收集激光扫描数据。 激光扫描数据可以包括描述激光器范围内物体的强度和位置信息的数据点（740,750,760）。 激光器的每个光束可以与数据点的相应子集相关联。 对于单个波束，数据点的子集可以被进一步划分为部分（910,920,930）。 对于每个部分，可以使用平均强度和标准偏差来确定阈值强度。 可以通过将每个数据点的强度与数据点出现的部分的阈值强度进行比较，并且基于数据点的高程来生成一组车道标记数据点。 该集合可以存储以供以后使用或以其他方式可用于进一步处理。

公开(公告)号：EP2812222A2

公开(公告)日：2014-12-17

申请号：EP13810454.2

申请日：2013-03-21

Applicant: Google Inc.

Inventor： BURNETTE, Donald, Jason ,  FERGUSON, David, I.

IPC: B60W30/10 ,  B60W30/12

CPC classification number: G06K9/00798 ,  G06K9/2036

Abstract: Aspects of the disclosure relate generally to detecting lane markers. More specifically, laser scan data may be collected by moving a laser (310, 311) along a roadway (500). The laser scan data may include data points (740, 750, 760) describing the intensity and location information of objects within range of the laser. Each beam of the laser may be associated with a respective subset of data points. For a single beam, the subset of data points may be further divided into sections (910, 920, 930). For each section, the average intensity and standard deviation may be used to determine a threshold intensity. A set of lane marker data points may be generated by comparing the intensity of each data point to the threshold intensity for the section in which the data point appears and based on the elevation of the data point. This set may be stored for later use or otherwise made available for further processing.

Abstract translation: 本公开的方面通常涉及检测车道标记。 更具体地，可以通过沿着道路移动激光来收集激光扫描数据。 激光扫描数据可以包括描述激光器范围内物体的强度和位置信息的数据点。 激光器的每个光束可以与数据点的相应子集相关联。 对于单个波束，数据点的子集可以进一步划分为多个部分。 对于每个部分，可以使用平均强度和标准偏差来确定阈值强度。 可以通过将每个数据点的强度与数据点出现的部分的阈值强度进行比较，并且基于数据点的高程来生成一组车道标记数据点。 该集合可以存储以供以后使用或以其他方式可用于进一步处理。

公开(公告)号：EP2984642A1

公开(公告)日：2016-02-17

申请号：EP14782618.4

申请日：2014-04-08

Applicant: Google Inc.

Inventor： FERGUSON, David Ian Franklin ,  BURNETTE, Donald, Jason

IPC: G08G1/0968 ,  G01C21/00

CPC classification number: G05D1/0214 ,  B60W10/04 ,  B60W10/20 ,  B60W30/00 ,  B60W30/143 ,  B60W2710/20 ,  B60W2720/106 ,  G01C21/26 ,  G01C21/32 ,  G01C21/3407 ,  G01S19/42 ,  G01S2013/936 ,  G05D1/00 ,  G05D1/0088 ,  G05D1/0236 ,  G05D1/0246 ,  G05D1/0255 ,  G05D1/0257 ,  G05D1/0276 ,  G05D2201/0213 ,  G06K9/00 ,  G06K9/00798 ,  G06K9/00818

Abstract: Aspects of the present disclosure relate to differentiating between active and inactive construction zones. In one example, this may include identifying a construction object (810-890) associated with a construction zone. The identified construction object may be used to map the area (910) of the construction zone. Detailed map information (136) may then be used to classify the activity of the construction zone. The area of the construction zone and the classification may be added to the detailed map information. Subsequent to adding the construction zone and the classification to the detailed map information, the construction object (or another construction object) may be identified. The location of the construction object may be used to identify the construction zone and classification from the detailed map information. The classification of the classification may be used to operate a vehicle (101) having an autonomous mode.

公开(公告)号：EP3175311A1

公开(公告)日：2017-06-07

申请号：EP15827437.3

申请日：2015-07-16

Applicant: Google Inc.

Inventor： GUTMANN, Jens-steffen, Ralf ,  WENDEL, Andreas ,  FAIRFIELD, Nathaniel ,  DOLGOV, Dmitri, A. ,  BURNETTE, Donald, Jason

IPC: G05D1/02 ,  G08G1/16

CPC classification number: B60W30/09 ,  G08G1/09623 ,  G08G1/09626 ,  G08G1/096725 ,  G08G1/096783 ,  G08G1/096791

Abstract: Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.

Abstract translation: 本公开的各方面涉及确定车辆是否应该继续通过交叉路口。 例如，车辆的一台或多台计算机可识别交通信号灯将从黄色变为红色的时间。 一个或多个计算机还可以在交通信号灯从黄色变为红色时估计车辆的位置。 交点的起点可以被识别。 基于在交通信号灯将从黄色变为红色时车辆的估计位置是否至少超过起点的阈值距离，计算机可以确定车辆是否应该继续通过交叉口。