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公开(公告)号：US20160035223A1
公开(公告)日：2016-02-04
申请号：US14448299
申请日：2014-07-31
Applicant: Google Inc.
Inventor： Jens-Steffen Ralf Gutmann , Andreas Wendel , Nathaniel Fairfield , Dmitri A. Dolgov , Donald Jason Burnette
IPC: G08G1/07
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: 本公开的方面涉及确定车辆是否应当经过十字路口。 例如，一个或多个车辆的计算机可以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。 可以确定交点的起点。 基于交通信号灯是否从黄色变为红色，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穿过交叉路口。
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Patent Agency Ranking
公开(公告)号：US09234618B1
公开(公告)日：2016-01-12
申请号：US13629378
申请日：2012-09-27
Applicant: Google Inc.
Inventor： Jiajun Zhu , David I. Ferguson , Dmitri A. Dolgov , Jonathan Baldwin Dowdall
CPC classification number: G01N21/27 , F16L55/00 , G01C3/08 , G01S7/4808 , G01S7/497 , G01S17/023 , G01S17/08 , G01S17/10 , G01S17/42 , G01S17/89 , G01S17/936 , G05D1/0088
Abstract: A light detection and ranging device associated with an autonomous vehicle scans through a scanning zone while emitting light pulses and receives reflected signals corresponding to the light pulses. The reflected signals indicate a three-dimensional point map of the distribution of reflective points in the scanning zone. A hyperspectral sensor images a region of the scanning zone corresponding to a reflective feature indicated by the three-dimensional point map. The output from the hyperspectral sensor includes spectral information characterizing a spectral distribution of radiation received from the reflective feature. The spectral characteristics of the reflective feature allow for distinguishing solid objects from non-solid reflective features, and a map of solid objects is provided to inform real time navigation decisions.
Abstract translation: 与自主车辆相关联的光检测和测距装置在发射光脉冲的同时扫描扫描区域并接收对应于光脉冲的反射信号。 反射信号表示扫描区域中反射点分布的三维点图。 高光谱传感器对与三维点图所示的反射特征对应的扫描区域进行成像。 来自高光谱传感器的输出包括表征从反射特征接收的辐射的光谱分布的光谱信息。 反射特征的光谱特征允许区分固体物体与非固体反射特征，并且提供固体物体的映射以通知实时导航决定。
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17.发明授权Systems and methods for determining whether a driving environment has changed 有权用于确定驾驶环境是否已改变的系统和方法公开(公告)号：US08949016B1
公开(公告)日：2015-02-03
申请号：US13630039
申请日：2012-09-28
Applicant: Google Inc.
Inventor： David I. Ferguson , Dmitri A. Dolgov
CPC classification number: G05D1/0274 , B60W30/00 , G01S17/89 , G05D1/024 , G05D2201/0213
Abstract: A autonomous driving computer system determines whether a driving environment has changed. One or more objects and/or object types in the driving environment may be identified as primary objects. The autonomous driving computer system may be configured to detect the primary objects and/or object types, and compare the detected objects and/or object types with the previous known location of the detected object and/or object types. The autonomous driving computer system may obtain several different metrics to facilitate the comparison. A confidence probability obtained from the comparison may indicate the degree of confidence that the autonomous driving computer system has in determining that the driving environment has actually changed.
Abstract translation: 自主驾驶计算机系统确定驾驶环境是否已经改变。 驾驶环境中的一个或多个对象和/或对象类型可以被识别为主要对象。 自主驾驶计算机系统可以被配置为检测主要对象和/或对象类型，并且将检测到的对象和/或对象类型与检测到的对象和/或对象类型的先前已知位置进行比较。 自主驾驶计算机系统可以获得几个不同的度量以便于比较。 从比较获得的置信度概率可以指示自主驾驶计算机系统在确定驾驶环境实际上改变时的置信度。
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18.发明申请MODIFYING BEHAVIOR OF AUTONOMOUS VEHICLES BASED ON SENSOR BLIND SPOTS AND LIMITATIONS 有权基于传感器盲点和限制的自动车辆修改行为公开(公告)号：US20140214255A1
公开(公告)日：2014-07-31
申请号：US13749793
申请日：2013-01-25
Applicant: Google Inc.
Inventor： Dmitri A. Dolgov , Christopher Paul Urmson
IPC: G05D1/02
CPC classification number: G05D1/0274 , B60W30/18154 , B60W2050/0095 , B60W2550/12 , G05D1/0248 , G05D1/0257 , G05D1/0276 , G05D2201/0213
Abstract: Aspects of the present disclosure relate generally to modeling a vehicle's view of its environment. This view need not include what objects or features the vehicle is actually seeing, but rather those areas that the vehicle is able to observe using its sensors if the sensors were completely un-occluded. For example, for each of a plurality of sensors of the object detection component, a computer may an individual 3D model of that sensor's field of view. Weather information is received and used to adjust one or more of the models. After this adjusting, the models may be aggregated into a comprehensive 3D model. The comprehensive model may be combined with detailed map information indicating the probability of detecting objects at different locations. A model of the vehicle's environment may be computed based on the combined comprehensive 3D model and detailed map information and may be used to maneuver the vehicle.
Abstract translation: 本公开的方面通常涉及对车辆对其环境的观点的建模。 该视图不需要包括车辆实际看到的物体或特征，而是车辆能够使用其传感器观察的区域，如果传感器完全不被遮挡。 例如，对于对象检测组件的多个传感器中的每一个，计算机可以是该传感器的视场的单独的3D模型。 接收天气信息并用于调整一个或多个模型。 经过这种调整，模型可能会聚合成一个全面的3D模型。 综合模型可以与指示在不同位置检测物体的概率的详细地图信息相结合。 车辆的环境模型可以基于综合的综合3D模型和详细的地图信息来计算，并且可以用于操纵车辆。
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19.发明申请Modifying Behavior of Autonomous Vehicle Based on Predicted Behavior of Other Vehicles 有权基于其他车辆预测行为修改自主车辆行为公开(公告)号：US20140142799A1
公开(公告)日：2014-05-22
申请号：US14165740
申请日：2014-01-28
Applicant: Google Inc.
Inventor： David Ian Franklin Ferguson , Dmitri A. Dolgov
IPC: G08G1/16
CPC classification number: G08G1/166 , B60W30/095 , B60W30/12 , B60W30/16 , B60W30/18163 , B60W50/0097 , B60W2050/143 , B60W2520/10 , B60W2550/402 , B60W2750/40 , G05D1/00 , G05D1/0088 , G05D2201/0213 , Y02T10/84
Abstract: A vehicle configured to operate in an autonomous mode could determine a current state of the vehicle and the current state of the environment of the vehicle. The environment of the vehicle includes at least one other vehicle. A predicted behavior of the at least one other vehicle could be determined based on the current state of the vehicle and the current state of the environment of the vehicle. A confidence level could also be determined based on the predicted behavior, the current state of the vehicle, and the current state of the environment of the vehicle. In some embodiments, the confidence level may be related to the likelihood of the at least one other vehicle to perform the predicted behavior. The vehicle in the autonomous mode could be controlled based on the predicted behavior, the confidence level, and the current state of the vehicle and its environment.
Abstract translation: 配置为以自主模式操作的车辆可以确定车辆的当前状态和车辆的当前状态。 车辆的环境包括至少一个其他车辆。 可以基于车辆的当前状态和车辆的环境的当前状态来确定至少一个其他车辆的预测行为。 还可以基于预测的行为，车辆的当前状态和车辆的当前状态来确定置信水平。 在一些实施例中，置信水平可以与至少一个其他车辆执行预测行为的可能性有关。 可以根据车辆的预测行为，置信水平和当前状态及其环境来控制自主模式下的车辆。
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公开(公告)号：US09671784B1
公开(公告)日：2017-06-06
申请号：US15016539
申请日：2016-02-05
Applicant: Google Inc.
Inventor： Dmitri A. Dolgov , Philip Nemec , Anne Kristiina Aula
CPC classification number: G05D1/021 , B60W30/16 , B60W2550/30 , B60W2550/308 , B60W2750/308 , B62D15/025 , B62D15/0255 , G05D1/00 , G05D1/0055 , G05D1/0287
Abstract: An autonomous vehicle detects a tailgating vehicle and uses various response mechanisms. A vehicle is identified as a tailgater based on whether its characteristics meet a variable threshold. When the autonomous vehicle is traveling at slower speeds, the threshold is defined in distance. When the autonomous vehicle is traveling at faster speeds, the threshold is defined in time. The autonomous vehicle responds to the tailgater by modifying its driving behavior. In one example, the autonomous vehicle adjusts a headway buffer (defined in time) from another vehicle in front of the autonomous vehicle. In this regard, if the tailgater is T seconds too close to the autonomous vehicle, the autonomous vehicle increases the headway buffer to the vehicle in front of it by some amount relative to T.
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