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公开(公告)号：US10254759B1
公开(公告)日：2019-04-09
申请号：US15704969
申请日：2017-09-14
Applicant: Waymo LLC
Inventor： Aleksandra Faust , Matthieu Devin , Yu-hsin Joyce Chen , Franklin Morley , Vadim Furman , Carlos Alberto Fuertes Pascual
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing an interactive autonomous vehicle agent. One of the methods includes receiving a request to generate an experience tuple for a vehicle in a particular driving context. A predicted environment observation representing a predicted environment of the autonomous vehicle after the candidate action is taken by the autonomous vehicle in an initial environment is generated, including providing an initial environment observation and the candidate action as input to a vehicle behavior model neural network trained to generate predicted environment observations. An immediate quality value is generated from a context-specific quality model that generates immediate quality values that are specific to the particular driving context. An experience tuple comprising the initial environment observation, the candidate action, and the immediate quality value is generated and used as input to a reinforcement learning system for the autonomous vehicle.
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公开(公告)号：US11067988B1
公开(公告)日：2021-07-20
申请号：US16352174
申请日：2019-03-13
Applicant: Waymo LLC
Inventor： Aleksandra Faust , Matthieu Devin , Yu-hsin Joyce Chen , Franklin Morley , Vadim Furman , Carlos Alberto Fuertes Pascual
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing an interactive autonomous vehicle agent. One of the methods includes receiving a request to generate an experience tuple for a vehicle in a particular driving context. A predicted environment observation representing a predicted environment of the autonomous vehicle after the candidate action is taken by the autonomous vehicle in an initial environment is generated, including providing an initial environment observation and the candidate action as input to a vehicle behavior model neural network trained to generate predicted environment observations. An immediate quality value is generated from a context-specific quality model that generates immediate quality values that are specific to the particular driving context. An experience tuple comprising the initial environment observation, the candidate action, and the immediate quality value is generated and used as input to a reinforcement learning system for the autonomous vehicle.
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公开(公告)号：US20180102001A1
公开(公告)日：2018-04-12
申请号：US15288463
申请日：2016-10-07
Applicant: Waymo LLC
Inventor： Aleksandra Faust , Nathaniel Fairfield
CPC classification number: G07C5/0816 , B60K28/14 , B60R21/0132 , B60R2021/01252 , B60W2030/082 , B60W2520/105 , B60W2550/10 , B60W2550/402 , B60Y2302/05 , G05D1/0088 , G06N7/005
Abstract: Aspects of the disclosure relate to detecting vehicle collisions. In one example, one or more computing devices may receive acceleration data of a vehicle and the expected acceleration data of the vehicle over a period of time. The one or more computing devices may determine a change in the vehicle's acceleration over the period of time, where the change in the vehicle's acceleration over the period of time is the difference between the expected acceleration data and the acceleration data. The one or more computing devices may detect an occurrence when the change in the vehicle's acceleration is greater than a threshold value and assign the occurrence into a collision category. Based on the assigned collision category, the one or more computing devices may perform a responsive action.
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公开(公告)号：US10311658B2
公开(公告)日：2019-06-04
申请号：US15288463
申请日：2016-10-07
Applicant: Waymo LLC
Inventor： Aleksandra Faust , Nathaniel Fairfield
Abstract: Aspects of the disclosure relate to detecting vehicle collisions. In one example, one or more computing devices may receive acceleration data of a vehicle and the expected acceleration data of the vehicle over a period of time. The one or more computing devices may determine a change in the vehicle's acceleration over the period of time, where the change in the vehicle's acceleration over the period of time is the difference between the expected acceleration data and the acceleration data. The one or more computing devices may detect an occurrence when the change in the vehicle's acceleration is greater than a threshold value and assign the occurrence into a collision category. Based on the assigned collision category, the one or more computing devices may perform a responsive action.
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