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公开(公告)号：US11520345B2
公开(公告)日：2022-12-06
申请号：US16781893
申请日：2020-02-04
Applicant: NVIDIA Corporation
Inventor： Davide Marco Onofrio , Hae-Jong Seo , David Nister , Minwoo Park , Neda Cvijetic
Abstract: In various examples, a path perception ensemble is used to produce a more accurate and reliable understanding of a driving surface and/or a path there through. For example, an analysis of a plurality of path perception inputs provides testability and reliability for accurate and redundant lane mapping and/or path planning in real-time or near real-time. By incorporating a plurality of separate path perception computations, a means of metricizing path perception correctness, quality, and reliability is provided by analyzing whether and how much the individual path perception signals agree or disagree. By implementing this approach—where individual path perception inputs fail in almost independent ways—a system failure is less statistically likely. In addition, with diversity and redundancy in path perception, comfortable lane keeping on high curvature roads, under severe road conditions, and/or at complex intersections, as well as autonomous negotiation of turns at intersections, may be enabled.
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公开(公告)号：US11474519B2
公开(公告)日：2022-10-18
申请号：US16286330
申请日：2019-02-26
Applicant: NVIDIA Corporation
Inventor： Gary Hicok , Michael Cox , Miguel Sainz , Martin Hempel , Ratin Kumar , Timo Roman , Gordon Grigor , David Nister , Justin Ebert , Chin Shih , Tony Tam , Ruchi Bhargava
Abstract: A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.
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公开(公告)号：US20220301186A1
公开(公告)日：2022-09-22
申请号：US17678835
申请日：2022-02-23
Applicant: NVIDIA Corporation
Inventor： David Nister , Soohwan Kim , Yue Wu , Minwoo Park , Cheng-Chieh Yang
IPC: G06T7/215 , G06T7/60 , G06V10/422
Abstract: In various examples, an ego-machine may analyze sensor data to identify and track features in the sensor data using. Geometry of the tracked features may be used to analyze motion flow to determine whether the motion flow violates one or more geometrical constraints. As such, tracked features may be identified as dynamic features when the motion flow corresponding to the tracked features violates the one or more static constraints for static features. Tracked features that are determined to be dynamic features may be clustered together according to their location and feature track. Once features have been clustered together, the system may calculate a detection bounding shape for the clustered features. The bounding shape information may then be used by the ego-machine for path planning, control decisions, obstacle avoidance, and/or other operations.
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公开(公告)号：US11435756B2
公开(公告)日：2022-09-06
申请号：US17108965
申请日：2020-12-01
Applicant: NVIDIA Corporation
Inventor： Michael Grabner , Jeremy Furtek , David Nister
Abstract: Systems and methods for performing visual odometry more rapidly. Pairs of representations from sensor data (such as images from one or more cameras) are selected, and features common to both representations of the pair are identified. Portions of bundle adjustment matrices that correspond to the pair are updated using the common features. These updates are maintained in register memory until all portions of the matrices that correspond to the pair are updated. By selecting only common features of one particular pair of representations, updated matrix values may be kept in registers. Accordingly, matrix updates for each common feature may be collectively saved with a single write of the registers to other memory. In this manner, fewer write operations are performed from register memory to other memory, thus reducing the time required to update bundle adjustment matrices and thus speeding the bundle adjustment process.
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公开(公告)号：US11195331B2
公开(公告)日：2021-12-07
申请号：US16820164
申请日：2020-03-16
Applicant: NVIDIA Corporation
Inventor： Dongwoo Lee , Junghyun Kwon , Sangmin Oh , Wenchao Zheng , Hae-Jong Seo , David Nister , Berta Rodriguez Hervas
Abstract: A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.
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Patent Agency Ranking
公开(公告)号：US11182916B2
公开(公告)日：2021-11-23
申请号：US16728598
申请日：2019-12-27
Applicant: NVIDIA Corporation
Inventor： Yilin Yang , Bala Siva Sashank Jujjavarapu , Pekka Janis , Zhaoting Ye , Sangmin Oh , Minwoo Park , Daniel Herrera Castro , Tommi Koivisto , David Nister
Abstract: In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.
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127.发明授权公开(公告)号：US11170299B2
公开(公告)日：2021-11-09
申请号：US16813306
申请日：2020-03-09
Applicant: NVIDIA Corporation
Inventor： Junghyun Kwon , Yilin Yang , Bala Siva Sashank Jujjavarapu , Zhaoting Ye , Sangmin Oh , Minwoo Park , David Nister
Abstract: In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN.
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公开(公告)号：US20210342608A1
公开(公告)日：2021-11-04
申请号：US17377053
申请日：2021-07-15
Applicant: NVIDIA Corporation
Inventor： Nikolai Smolyanskiy , Ryan Oldja , Ke Chen , Alexander Popov , Joachim Pehserl , Ibrahim Eden , Tilman Wekel , David Wehr , Ruchi Bhargava , David Nister
Abstract: A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.
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公开(公告)号：US20210325892A1
公开(公告)日：2021-10-21
申请号：US17356337
申请日：2021-06-23
Applicant: NVIDIA Corporation
Inventor： David Nister , Hon-Leung Lee , Julia Ng , Yizhou Wang
IPC: G05D1/02 , B60W30/09 , G05D1/08 , B60W30/095
Abstract: In various examples, a current claimed set of points representative of a volume in an environment occupied by a vehicle at a time may be determined. A vehicle-occupied trajectory and at least one object-occupied trajectory may be generated at the time. An intersection between the vehicle-occupied trajectory and an object-occupied trajectory may be determined based at least in part on comparing the vehicle-occupied trajectory to the object-occupied trajectory. Based on the intersection, the vehicle may then execute the first safety procedure or an alternative procedure that, when implemented by the vehicle when the object implements the second safety procedure, is determined to have a lesser likelihood of incurring a collision between the vehicle and the object than the first safety procedure.
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130.发明申请公开(公告)号：US20210295171A1
公开(公告)日：2021-09-23
申请号：US16824199
申请日：2020-03-19
Applicant: NVIDIA Corporation
Inventor： Alexey Kamenev , Nikolai Smolyanskiy , Ishwar Kulkarni , Ollin Boer Bohan , Fangkai Yang , Alperen Degirmenci , Ruchi Bhargava , Urs Muller , David Nister , Rotem Aviv
Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.
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