-
公开(公告)号：US11579629B2
公开(公告)日：2023-02-14
申请号：US16514404
申请日：2019-07-17
Applicant: NVIDIA Corporation
Inventor： Yue Wu , Pekka Janis , Xin Tong , Cheng-Chieh Yang , Minwoo Park , David Nister
Abstract: In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.
-
公开(公告)号：US20220413509A1
公开(公告)日：2022-12-29
申请号：US17900622
申请日：2022-08-31
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.
-
53.发明申请公开(公告)号：US20220379917A1
公开(公告)日：2022-12-01
申请号：US17328052
申请日：2021-05-24
Applicant: NVIDIA Corporation
Inventor： Birgit Henke , David Nister , Julia Ng
IPC: B60W60/00
Abstract: A trajectory for an autonomous machine may be evaluated for safety based at least on determining whether the autonomous machine would be capable of occupying points of the trajectory in space-time while still being able to avoid a potential future collision with one or more objects in the environment through use of one or more safety procedures. To do so, a point of the trajectory may be evaluated for conflict based at least on a comparison between points in space-time that correspond to the autonomous machine executing the safety procedure(s) from the point and arrival times of the one or more objects to corresponding position(s) in the environment. A trajectory may be sampled and evaluated for conflicts at various points throughout the trajectory. Based on results of one or more evaluations, the trajectory may be scored, eliminated from consideration, or otherwise considered for control of the autonomous machine.
-
公开(公告)号：US20220351524A1
公开(公告)日：2022-11-03
申请号：US17864026
申请日：2022-07-13
Applicant: NVIDIA Corporation
Inventor： Trung Pham , Berta Rodriguez Hervas , Minwoo Park , David Nister , Neda Cvijetic
IPC: G06V20/56 , G06N3/04 , G05B13/02 , G06T5/00 , G06T3/40 , G06T7/11 , G06T11/20 , G06K9/62 , G06N3/08 , G06V30/262
Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.
-
公开(公告)号：US20220340149A1
公开(公告)日：2022-10-27
申请号：US17726407
申请日：2022-04-21
Applicant: NVIDIA Corporation
Inventor： David Nister , Cheng-Chieh Yang , Yue Wu
Abstract: In various examples, an end-to-end perception evaluation system for autonomous and semi-autonomous machine applications may be implemented to evaluate how the accuracy or precision of outputs of machine learning models—such as deep neural networks (DNNs)—impact downstream performance of the machine when relied upon. For example, decisions computed by the system using ground truth output types may be compared to decisions computed by the system using the perception outputs. As a result, discrepancies in downstream decision making of the system between the ground truth information and the perception information may be evaluated to either aid in updating or retraining of the machine learning model or aid in generating more accurate or precise ground truth information.
-
Patent Agency Ranking
公开(公告)号：US20220253706A1
公开(公告)日：2022-08-11
申请号：US17723195
申请日：2022-04-18
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.
-
公开(公告)号：US11301697B2
公开(公告)日：2022-04-12
申请号：US16938473
申请日：2020-07-24
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
Abstract: Various types of systems or technologies can be used to collect data in a 3D space. For example, LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improved techniques for processing the point cloud data that has been collected. The improved techniques include mapping 3D point cloud data points into a 2D depth map, fetching a group of the mapped 3D point cloud data points that are within a bounded window of the 2D depth map; and generating geometric space parameters based on the group of the mapped 3D point cloud data points. The generated geometric space parameters may be used for object motion, obstacle detection, freespace detection, and/or landmark detection for an area surrounding a vehicle.
-
公开(公告)号：US20220108465A1
公开(公告)日：2022-04-07
申请号：US17522624
申请日：2021-11-09
Applicant: NVIDIA Corporation
Inventor： Yilin Yang , Bala Siva 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—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.
-
公开(公告)号：US20220092855A1
公开(公告)日：2022-03-24
申请号：US17457825
申请日：2021-12-06
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.
-
60.发明申请公开(公告)号：US20220019893A1
公开(公告)日：2022-01-20
申请号：US17449310
申请日：2021-09-29
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.
-
-
-
-
-
-
-
-
-
Search Results
176
records
(took 0.026 seconds)
