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公开(公告)号：US20210094540A1
公开(公告)日：2021-04-01
申请号：US16586853
申请日：2019-09-27
Applicant: Zoox, Inc.
Inventor： Gerrit Bagschik , Andrew Scott Crego , Antoine Ghislain Deux , Rodin Lyasoff , James William Vaisey Philbin , Marc Wimmershoff , Andreas Christian Reschka , Ashutosh Gajanan Rege , Sai Anurag Modalavalasa
IPC: B60W30/095 , G06K9/00 , G06T7/70
Abstract: Techniques for determining an error model associated with a system/subsystem of vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, errors can be introduced into operating regimes (scenarios) to validate the safe operation of such a system. By comparing captured and/or generated vehicle data with ground truth data, an error of the system can be statistically quantified and modeled. The statistical model can be used to introduce errors to the scenario to perturb the scenario to test, for example, a vehicle controller. Based on a simulation of the vehicle controlled in the perturbed scenario, a safety metric associated with the vehicle controller can be determined, as well as causes for any failures.
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公开(公告)号：US11734473B2
公开(公告)日：2023-08-22
申请号：US16708019
申请日：2019-12-09
Applicant: Zoox, Inc.
Inventor： Sai Anurag Modalavalasa , Gerrit Bagschik , Andrew Scott Crego , Antoine Ghislain Deux , Rodin Lyasoff , James William Vaisey Philbin , Ashutosh Gajanan Rege , Andreas Christian Reschka , Marc Wimmershoff
CPC classification number: G06F30/20 , G05D1/0027 , G05D1/0088 , G05D1/0214 , G06F18/23 , G06V20/56 , G06V20/58 , G07C5/0816 , G05D2201/0213
Abstract: Techniques for determining an error model based on vehicle data and ground truth data are discussed herein. To determine whether a complex system (which may be not capable of being inspected) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data. To provide safe operation of such a system, an error model can be determined that can provide a probability associated with perception data and a vehicle can determine a trajectory based on the probability of an error associated with the perception data.
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Patent Agency Ranking
公开(公告)号：US20210097148A1
公开(公告)日：2021-04-01
申请号：US16586838
申请日：2019-09-27
Applicant: Zoox, Inc.
Inventor： Gerrit Bagschik , Andrew Scott Crego , Antoine Ghislain Deux , Rodin Lyasoff , James William Vaisey Philbin , Marc Wimmershoff , Andreas Christian Reschka , Ashutosh Gajanan Rege
Abstract: Techniques for determining a safety metric associated with a vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data and associated with a scenario parameter to be adjusted. To validate safe operation of such a system, a scenario may be identified for inspection. Error metrics of a subsystem of the system can be quantified. The error metrics, in addition to stochastic errors of other systems/subsystems can be introduced to the scenario. The scenario parameter may also be perturbed. Any multitude of such perturbations can be instantiated in a simulation to test, for example, a vehicle controller. A safety metric associated with the vehicle controller can be determined based on the simulation, as well as causes for any failures.
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公开(公告)号：US11625513B2
公开(公告)日：2023-04-11
申请号：US16586838
申请日：2019-09-27
Applicant: Zoox, Inc.
Inventor： Gerrit Bagschik , Andrew Scott Crego , Antoine Ghislain Deux , Rodin Lyasoff , James William Vaisey Philbin , Marc Wimmershoff , Andreas Christian Reschka , Ashutosh Gajanan Rege
Abstract: Techniques for determining a safety metric associated with a vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data and associated with a scenario parameter to be adjusted. To validate safe operation of such a system, a scenario may be identified for inspection. Error metrics of a subsystem of the system can be quantified. The error metrics, in addition to stochastic errors of other systems/subsystems can be introduced to the scenario. The scenario parameter may also be perturbed. Any multitude of such perturbations can be instantiated in a simulation to test, for example, a vehicle controller. A safety metric associated with the vehicle controller can be determined based on the simulation, as well as causes for any failures.
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公开(公告)号：US11351995B2
公开(公告)日：2022-06-07
申请号：US16586853
申请日：2019-09-27
Applicant: Zoox, Inc.
Inventor： Gerrit Bagschik , Andrew Scott Crego , Antoine Ghislain Deux , Rodin Lyasoff , James William Vaisey Philbin , Marc Wimmershoff , Andreas Christian Reschka , Ashutosh Gajanan Rege , Sai Anurag Modalavalasa
IPC: B60W30/095 , G06T7/70 , G06V20/58
Abstract: Techniques for determining an error model associated with a system/subsystem of vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, errors can be introduced into operating regimes (scenarios) to validate the safe operation of such a system. By comparing captured and/or generated vehicle data with ground truth data, an error of the system can be statistically quantified and modeled. The statistical model can be used to introduce errors to the scenario to perturb the scenario to test, for example, a vehicle controller. Based on a simulation of the vehicle controlled in the perturbed scenario, a safety metric associated with the vehicle controller can be determined, as well as causes for any failures.
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公开(公告)号：US11648962B1
公开(公告)日：2023-05-16
申请号：US17152645
申请日：2021-01-19
Applicant: Zoox, Inc.
Inventor： Andrew Scott Crego , Antoine Ghislain Deux , Ali Ghasemzadehkhoshgroudi , Rodin Lyasoff , Andreas Christian Reschka
CPC classification number: B60W60/0015 , B60W30/09 , B60W60/0027 , G05D1/0061 , G05D1/0214 , G06N20/00 , B60W2520/10 , B60W2520/12 , B60W2554/4044 , B60W2554/4046 , B60W2554/801 , B60W2556/10 , B60W2720/106 , B60W2720/125 , G07C5/02
Abstract: Techniques for predicting safety metrics associated with near-miss conditions for a vehicle, such as an autonomous vehicle, are discussed herein. For instance, a training system identifies an object in an environment and determines a trajectory for the object. The training system may receive a trajectory for a vehicle and associate the trajectory for the object and the trajectory for the vehicle with an event involving the object and the vehicle. In examples, the training system determines a parameter associated with motion of the vehicle as indicated by the trajectory of the vehicle relative to the trajectory of the object, and the event. Then, the training system may determine a safety metric associated with the event that indicates whether the vehicle came within a threshold of a collision with the object during a time period associated with the event.
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公开(公告)号：US20210096571A1
公开(公告)日：2021-04-01
申请号：US16708019
申请日：2019-12-09
Applicant: Zoox, Inc.
Inventor： Sai Anurag Modalavalasa , Gerrit Bagschik , Andrew Scott Crego , Antoine Ghislain Deux , Rodin Lyasoff , James William Vaisey Philbin , Ashutosh Gajanan Rege , Andreas Christian Reschka , Marc Wimmershoff
Abstract: Techniques for determining an error model based on vehicle data and ground truth data are discussed herein. To determine whether a complex system (which may be not capable of being inspected) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data. To provide safe operation of such a system, an error model can be determined that can provide a probability associated with perception data and a vehicle can determine a trajectory based on the probability of an error associated with the perception data.
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