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    • 1. 发明授权
    • Determining when to drive autonomously
    • 确定何时自主驾驶
    • US08718861B1
    • 2014-05-06
    • US13444215
    • 2012-04-11
    • Michael Steven MontemerloHyman Jack MurveitChristopher Paul UrmsonDmitri A. DolgovPhilip Nemec
    • Michael Steven MontemerloHyman Jack MurveitChristopher Paul UrmsonDmitri A. DolgovPhilip Nemec
    • G05D1/00
    • G05D1/0061B60W30/00G05D2201/0213
    • Aspects of the disclosure relate generally to determining whether an autonomous vehicle should be driven in an autonomous or semiautonomous mode (where steering, acceleration, and braking are controlled by the vehicle's computer). For example, a computer may maneuver a vehicle in an autonomous or a semiautonomous mode. The computer may continuously receive data from one or more sensors. This data may be processed to identify objects and the characteristics of the objects. The detected objects and their respective characteristics may be compared to a traffic pattern model and detailed map information. If the characteristics of the objects deviate from the traffic pattern model or detailed map information by more than some acceptable deviation threshold value, the computer may generate an alert to inform the driver of the need to take control of the vehicle or the computer may maneuver the vehicle in order to avoid any problems.
    • 本公开的方面通常涉及确定自主车辆是否应以自主或半自主模式(其中转向,加速和制动由车辆的计算机控制)驱动。 例如,计算机可以以自主或半自主的方式操纵车辆。 计算机可以连续地从一个或多个传感器接收数据。 可以处理该数据以识别对象和对象的特征。 可以将检测到的对象及其各自的特征与交通模式模型和详细地图信息进行比较。 如果物体的特征偏离交通模式模型或详细地图信息超过一些可接受的偏差阈值,则计算机可以产生警报以通知驾驶员需要控制车辆,或者计算机可以操纵 车辆为了避免任何问题。
    • 9. 发明授权
    • Object detection and classification for autonomous vehicles
    • 自主车辆的物体检测和分类
    • US08874372B1
    • 2014-10-28
    • US13440555
    • 2012-04-05
    • Jiajun ZhuMichael Steven MontemerloChristopher Paul UrmsonAndrew Chatham
    • Jiajun ZhuMichael Steven MontemerloChristopher Paul UrmsonAndrew Chatham
    • G06K9/00
    • G06K9/00805G01C21/26G08G1/09626G08G1/096725G08G1/096741G08G1/096775
    • Aspects of the disclosure relate generally to safe and effective use of autonomous vehicles. More specifically, objects detected in a vehicle's surroundings may be detected by the vehicle's various sensors and identified based on their relative location in a roadgraph. The roadgraph may include a graph network of information such as roads, lanes, intersections, and the connections between these features. The roadgraph may also include the boundaries of areas, including for example, crosswalks or bicycle lanes. In one example, an object detected in a location corresponding to a crosswalk area of the roadgraph may be identified as a person. In another example, an object detected in a location corresponding to a bicycle area of the roadgraph and identified as a bicycle. By identifying the type of object in this way, an autonomous vehicle may be better prepared to react to or simply avoid the object.
    • 本公开的方面一般涉及安全和有效地使用自主车辆。 更具体地,可以由车辆的各种传感器检测在车辆周围检测到的物体,并且基于它们在路标图中的相对位置来识别。 道路图可以包括信息的图形网络,例如道路,车道,交叉路口以及这些特征之间的连接。 道路图也可以包括区域的边界,包括例如人行横道或自行车道。 在一个示例中,在与路灯图的人行横道区域相对应的位置中检测到的对象可以被识别为人。 在另一示例中,在与路灯图的自行车区域相对应的位置中检测到的物体,并被识别为自行车。 通过以这种方式识别对象的类型,自主车辆可以更好地准备反应或者简单地避开物体。