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公开(公告)号：US20200334900A1
公开(公告)日：2020-10-22
申请号：US16385921
申请日：2019-04-16
Applicant: NVIDIA Corporation
Inventor： Philippe Bouttefroy , David Nister , Ibrahim Eden
Abstract: In various examples, locations of directional landmarks, such as vertical landmarks, may be identified using 3D reconstruction. A set of observations of directional landmarks (e.g., images captured from a moving vehicle) may be reduced to 1D lookups by rectifying the observations to align directional landmarks along a particular direction of the observations. Object detection may be applied, and corresponding 1D lookups may be generated to represent the presence of a detected vertical landmark in an image.
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Patent Agency Ranking
公开(公告)号：US10769840B2
公开(公告)日：2020-09-08
申请号：US16051219
申请日：2018-07-31
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 improvements for processing the point cloud data that has been collected. The processing improvements include using a three dimensional polar depth map to assist in performing nearest neighbor analysis on point cloud data for object detection, trajectory detection, freespace detection, obstacle detection, landmark detection, and providing other geometric space parameters.
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公开(公告)号：US20200210726A1
公开(公告)日：2020-07-02
申请号：US16728595
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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公开(公告)号：US20190266779A1
公开(公告)日：2019-08-29
申请号：US16051219
申请日：2018-07-31
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 improvements for processing the point cloud data that has been collected. The processing improvements include using a three dimensional polar depth map to assist in performing nearest neighbor analysis on point cloud data for object detection, trajectory detection, freespace detection, obstacle detection, landmark detection, and providing other geometric space parameters.
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公开(公告)号：US20190250622A1
公开(公告)日：2019-08-15
申请号：US16269921
申请日：2019-02-07
Applicant: NVIDIA Corporation
Inventor： David Nister , Anton Vorontsov
CPC classification number: G05D1/0214 , B60R1/00 , B60R2300/30 , B60W30/08 , G05D1/0231 , G05D1/0242 , G05D1/0255 , G05D1/0257 , G06K9/00791 , G06K9/00805 , G06K9/00825
Abstract: In various examples, sensor data representative of a field of view of at least one sensor of a vehicle in an environment is received from the at least one sensor. Based at least in part on the sensor data, parameters of an object located in the environment are determined. Trajectories of the object are modeled toward target positions based at least in part on the parameters of the object. From the trajectories, safe time intervals (and/or safe arrival times) over which the vehicle occupying the plurality of target positions would not result in a collision with the object are computed. Based at least in part on the safe time intervals (and/or safe arrival times) and a position of the vehicle in the environment a trajectory for the vehicle may be generated and/or analyzed.
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公开(公告)号：US20190243371A1
公开(公告)日：2019-08-08
申请号：US16265780
申请日：2019-02-01
Applicant: NVIDIA Corporation
Inventor： David Nister , Hon-Leung Lee , Julia Ng , Yizhou Wang
IPC: G05D1/02
CPC classification number: G05D1/0214 , B60W30/09 , B60W30/095 , B60W2520/06 , B60W2520/10 , B60W2520/14 , B60W2520/16 , B60W2520/18 , B60W2550/10 , B60W2550/20 , G05D1/0221 , G05D1/0223 , G05D1/0231 , G05D1/0242 , G05D1/0255 , G05D1/0257 , G05D1/027 , G05D1/0278 , G05D1/0289 , G05D1/0891 , G05D2201/0213
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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