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公开(公告)号：US20220314993A1
公开(公告)日：2022-10-06
申请号：US17218051
申请日：2021-03-30
Applicant: Zoox, Inc.
Inventor： Gerrit Bagschik , Andrew Scott Crego , Gowtham Garimella , Michael Haggblade , Andraz Kavalar , Kai Zhenyu Wang
Abstract: Techniques for top-down scene discrimination are discussed. A system receives scene data associated with an environment proximate a vehicle. The scene data is input to a convolutional neural network (CNN) discriminator trained using a generator and a classification of the output of the CNN discriminator. The CNN discriminator generates an indication of whether the scene data is a generated scene or a captured scene. If the scene data is data generated scene, the system generates a caution notification indicating that a current environmental situation is different from any previous situations. Additionally, the caution notification is communicated to at least one of a vehicle system or a remote vehicle monitoring system.
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公开(公告)号：US12103558B1
公开(公告)日：2024-10-01
申请号：US17333602
申请日：2021-05-28
Applicant: Zoox, Inc.
Inventor： Andraz Kavalar
IPC: B60W60/00 , B60W30/095
CPC classification number: B60W60/0016 , B60W30/0953 , B60W30/0956 , B60W2720/106
Abstract: Techniques are discussed herein for executing and evaluating simulations and/or emulations based on parameterized scenarios, such as driving scenarios used to analyze and evaluate the responses of autonomous vehicle controllers. A surrogate model may be determined by simulating points in a simulation parameter space with a simulator to determine the surrogate model while capturing the main features of the simulation model. Additionally, relevant areas of the parameter space, such as, areas containing parameters associated with events occurring infrequently during simulations, may be quickly identified using Bayesian optimization techniques. An iterative evaluation of an acquisition function may delineate a contour associated with the region of interest, and the surrogate model may be evaluated to determine a probability of returning a result corresponding to a parameter being within the contour. The probabilities may be further analyzed to determine scenario/vehicle performance metrics access the scenario parameter space.
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公开(公告)号：US11858514B2
公开(公告)日：2024-01-02
申请号：US17218051
申请日：2021-03-30
Applicant: Zoox, Inc.
Inventor： Gerrit Bagschik , Andrew Scott Crego , Gowtham Garimella , Michael Haggblade , Andraz Kavalar , Kai Zhenyu Wang
CPC classification number: B60W30/18009 , B60W30/0956 , B60W50/14 , B60W60/0011 , G06N3/045 , G06N3/08 , G06N3/088 , G06V20/58 , B60W2556/10
Abstract: Techniques for top-down scene discrimination are discussed. A system receives scene data associated with an environment proximate a vehicle. The scene data is input to a convolutional neural network (CNN) discriminator trained using a generator and a classification of the output of the CNN discriminator. The CNN discriminator generates an indication of whether the scene data is a generated scene or a captured scene. If the scene data is data generated scene, the system generates a caution notification indicating that a current environmental situation is different from any previous situations. Additionally, the caution notification is communicated to at least one of a vehicle system or a remote vehicle monitoring system.
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Patent Agency Ranking
公开(公告)号：US20220379919A1
公开(公告)日：2022-12-01
申请号：US17332745
申请日：2021-05-27
Applicant: Zoox, Inc.
Inventor： Gerrit Bagschik , Andraz Kavalar
Abstract: Techniques for analyzing a parameter space are discussed. Techniques may include receiving policy data for evaluating a vehicle controller. The techniques may further include determining, using a Bayesian optimization and based at least in part on the vehicle controller, parameter sets associated with adverse events. The adverse events may be associated with a violation of the policy data. The techniques may associate, based on exposure data, parameter bounds of the adverse events and probabilities of the adverse events in a driving environment. A safety metric may be determined based on the Bayesian optimization. The techniques may also include weighting an impact of an adverse event based on the safety metric.
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公开(公告)号：US11940793B1
公开(公告)日：2024-03-26
申请号：US17187721
申请日：2021-02-26
Applicant: Zoox, Inc.
Inventor： Andraz Kavalar
CPC classification number: G05D1/0088 , B60W60/001 , G05B17/02 , G05D1/0276
Abstract: Validating a component of an autonomous vehicle may comprise determining, via simulation, a likelihood that operation of the component will result in an adverse event. Such simulations may be based on log data developed from real world driving events to, for example, accurately model a likelihood that a scenario will occur during real-world driving. Because adverse events may be exceedingly rare, the techniques may include modifying a probability distribution associated the likelihood that a scenario is simulated, determining a metric associated with an adverse event (e.g., a likelihood that operating the vehicle or updating a component thereof will result in an adverse event), and applying a correction to the metric based on the modification to the probability distribution.
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公开(公告)号：US11810225B2
公开(公告)日：2023-11-07
申请号：US17218010
申请日：2021-03-30
Applicant: Zoox, Inc.
Inventor： Gerrit Bagschik , Andrew Scott Crego , Gowtham Garimella , Michael Haggblade , Andraz Kavalar , Kai Zhenyu Wang
Abstract: Techniques for top-down scene generation are discussed. A generator component may receive multi-dimensional input data associated with an environment. The generator component may generate, based at least in part on the multi-dimensional input data, a generated top-down scene. A discriminator component receives the generated top-down scene and a real top-down scene. The discriminator component generates binary classification data indicating whether an individual scene in the scene data is classified as generated or classified as real. The binary classification data is provided as a loss to the generator component and the discriminator component.
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公开(公告)号：US11767030B1
公开(公告)日：2023-09-26
申请号：US17207451
申请日：2021-03-19
Applicant: Zoox, Inc.
IPC: B60W60/00 , G06V20/56 , G06F18/2415
CPC classification number: B60W60/0011 , B60W60/0015 , G06F18/24155 , G06V20/56
Abstract: Techniques are discussed herein for determining truncated simulation regions within a parameter space of simulation scenarios, such as driving scenarios used to analyze and evaluate the responses of autonomous vehicle controllers. Using non-sampling-based parameter selection techniques, parameterized scenarios may be executed as simulations to determine the truncated simulation region. Sampling-based parameter selection techniques may be used to determine additional parameterized scenarios, which may be compared to the truncated simulation region. Parameterized scenarios within the truncated simulation region may be executed as simulations and scenarios outside of the truncated simulation region may be excluded, and the aggregated results may be analyzed to determine scenario/vehicle performance metrics across the scenario parameter space.
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公开(公告)号：US20220319057A1
公开(公告)日：2022-10-06
申请号：US17218010
申请日：2021-03-30
Applicant: Zoox, Inc.
Inventor： Gerrit Bagschik , Andrew Scott Crego , Gowtham Garimella , Michael Haggblade , Andraz Kavalar , Kai Zhenyu Wang
Abstract: Techniques for top-down scene generation are discussed. A generator component may receive multi-dimensional input data associated with an environment. The generator component may generate, based at least in part on the multi-dimensional input data, a generated top-down scene. A discriminator component receives the generated top-down scene and a real top-down scene. The discriminator component generates binary classification data indicating whether an individual scene in the scene data is classified as generated or classified as real. The binary classification data is provided as a loss to the generator component and the discriminator component.
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