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公开(公告)号：US20210110178A1
公开(公告)日：2021-04-15
申请号：US17128492
申请日：2020-12-21
Applicant: NEC Laboratories America, Inc.
Inventor： Yi-Hsuan Tsai , Kihyuk Sohn , Buyu Liu , Manmohan Chandraker , Jong-Chyi Su
IPC: G06K9/00 , G06K9/62 , B60W30/095 , B60W30/09 , B60W10/18 , B60W10/20 , G08G1/16 , B60W50/00 , G06N3/08 , G06N3/04
Abstract: Systems and methods for obstacle detection are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes one or more road scenes having obstacles. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.
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公开(公告)号：US20250115254A1
公开(公告)日：2025-04-10
申请号：US18905738
申请日：2024-10-03
Applicant: NEC Laboratories America, Inc.
Inventor： Buyu Liu , Francesco Pittaluga , Bingbing Zhuang , Manmohan Chandraker , Samuel Sohn
Abstract: Systems and methods for a hybrid motion planner for autonomous vehicles. A multi-lane intelligent driver model (MIDM) can predict trajectory predictions from collected data by considering adjacent lanes of an ego vehicle. A multi-lane hybrid planning driver model (MPDM) can be trained using open-loop ground truth data and close-loop simulations to obtain a trained MPDM. The trained MPDM can predict planned trajectories with collected data and the trajectory predictions to generate final trajectories for the autonomous vehicles. The final trajectories can be employed to control the autonomous vehicles.
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公开(公告)号：US12131557B2
公开(公告)日：2024-10-29
申请号：US17521193
申请日：2021-11-08
Applicant: NEC Laboratories America, Inc.
Inventor： Buyu Liu , Bingbing Zhuang , Manmohan Chandraker
IPC: G06T7/10 , B60W10/18 , B60W10/20 , B60W30/02 , B60W30/09 , G06F18/214 , G06V20/56 , G06V30/262
CPC classification number: G06V20/588 , B60W10/18 , B60W10/20 , B60W30/02 , B60W30/09 , G06F18/2155 , G06T7/10 , G06V30/274 , B60W2420/403 , G06T2207/10024 , G06T2207/20081 , G06T2207/30256
Abstract: A computer-implemented method for road layout prediction is provided. The method includes segmenting, by a first processor-based element, an RGB image to output pixel-level semantic segmentation results for the RGB image in a perspective view for both visible and occluded pixels in the perspective view based on contextual clues. The method further includes learning, by a second processor-based element, a mapping from the pixel-level semantic segmentation results for the RGB image in the perspective view to a top view of the RGB image using a road plane assumption. The method also includes generating, by a third processor-based element, an occlusion-aware parametric road layout prediction for road layout related attributes in the top view.
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公开(公告)号：US12131422B2
公开(公告)日：2024-10-29
申请号：US17963471
申请日：2022-10-11
Applicant: NEC Laboratories America, Inc.
Inventor： Bingbing Zhuang , Samuel Schulter , Yi-Hsuan Tsai , Buyu Liu , Nanbo Li
CPC classification number: G06T15/20 , G06T15/00 , G06T17/00 , G06V10/774 , G06V10/82 , G06V20/41 , G06T2200/08 , G06T2210/56
Abstract: A method for achieving high-fidelity novel view synthesis and 3D reconstruction for large-scale scenes is presented. The method includes obtaining images from a video stream received from a plurality of video image capturing devices, grouping the images into different image clusters representing a large-scale 3D scene, training a neural radiance field (NeRF) and an uncertainty multilayer perceptron (MLP) for each of the image clusters to generate a plurality of NeRFs and a plurality of uncertainty MLPs for the large-scale 3D scene, applying a rendering loss and an entropy loss to the plurality of NeRFs, performing uncertainty-based fusion to the plurality of NeRFs to define a fused NeRF, and jointly fine-tuning the plurality of NeRFs and the plurality of uncertainty MLPs, and during inference, applying the fused NeRF for novel view synthesis of the large-scale 3D scene.
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公开(公告)号：US11816901B2
公开(公告)日：2023-11-14
申请号：US17187157
申请日：2021-02-26
Applicant: NEC Laboratories America, Inc.
Inventor： Sriram Nochur Narayanan , Buyu Liu , Ramin Moslemi , Francesco Pittaluga , Manmohan Chandraker
IPC: G06V20/56 , B60W60/00 , G06N3/08 , G06F18/214 , B60W30/095 , G06V10/82
CPC classification number: G06V20/56 , B60W30/0953 , B60W30/0956 , B60W60/0027 , G06F18/214 , G06N3/08 , G06V10/82 , B60W2420/42 , B60W2554/4045
Abstract: Methods and systems for training a trajectory prediction model and performing a vehicle maneuver include encoding a set of training data to generate encoded training vectors, where the training data includes trajectory information for agents over time. Trajectory scenarios are simulated based on the encoded training vectors, with each simulated trajectory scenario representing one or more agents with respective agent trajectories, to generate simulated training data. A predictive neural network model is trained using the simulated training data to generate predicted trajectory scenarios based on a detected scene.
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Patent Agency Ranking
公开(公告)号：US11455813B2
公开(公告)日：2022-09-27
申请号：US17096111
申请日：2020-11-12
Applicant: NEC Laboratories America, Inc.
Inventor： Buyu Liu , Bingbing Zhuang , Samuel Schulter , Manmohan Chandraker
IPC: G06V30/422 , G06T7/00 , G06V40/12
Abstract: Systems and methods are provided for producing a road layout model. The method includes capturing digital images having a perspective view, converting each of the digital images into top-down images, and conveying a top-down image of time t to a neural network that performs a feature transform to form a feature map of time t. The method also includes transferring the feature map of the top-down image of time t to a feature transform module to warp the feature map to a time t+1, and conveying a top-down image of time t+1 to form a feature map of time t+1. The method also includes combining the warped feature map of time t with the feature map of time t+1 to form a combined feature map, transferring the combined feature map to a long short-term memory (LSTM) module to generate the road layout model, and displaying the road layout model.
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公开(公告)号：US20220147746A1
公开(公告)日：2022-05-12
申请号：US17521193
申请日：2021-11-08
Applicant: NEC Laboratories America, Inc.
Inventor： Buyu Liu , Bingbing Zhuang , Manmohan Chandraker
Abstract: A computer-implemented method for road layout prediction is provided. The method includes segmenting, by a first processor-based element, an RGB image to output pixel-level semantic segmentation results for the RGB image in a perspective view for both visible and occluded pixels in the perspective view based on contextual clues. The method further includes learning, by a second processor-based element, a mapping from the pixel-level semantic segmentation results for the RGB image in the perspective view to a top view of the RGB image using a road plane assumption. The method also includes generating, by a third processor-based element, an occlusion-aware parametric road layout prediction for road layout related attributes in the top view.
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公开(公告)号：US20210276547A1
公开(公告)日：2021-09-09
申请号：US17187157
申请日：2021-02-26
Applicant: NEC Laboratories America, Inc.
Inventor： Sriram Nochur Narayanan , Buyu Liu , Ramin Moslemi , Francesco Pittaluga , Manmohan Chandraker
IPC: B60W30/095 , B60W60/00 , G06K9/62 , G06N3/08 , G06K9/00
Abstract: Methods and systems for training a trajectory prediction model and performing a vehicle maneuver include encoding a set of training data to generate encoded training vectors, where the training data includes trajectory information for agents over time. Trajectory scenarios are simulated based on the encoded training vectors, with each simulated trajectory scenario representing one or more agents with respective agent trajectories, to generate simulated training data. A predictive neural network model is trained using the simulated training data to generate predicted trajectory scenarios based on a detected scene.
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公开(公告)号：US10915792B2
公开(公告)日：2021-02-09
申请号：US16535681
申请日：2019-08-08
Applicant: NEC Laboratories America, Inc.
Inventor： Yi-Hsuan Tsai , Kihyuk Sohn , Buyu Liu , Manmohan Chandraker , Jong-Chyi Su
Abstract: Systems and methods for domain adaptation are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.
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公开(公告)号：US20230154104A1
公开(公告)日：2023-05-18
申请号：US17963471
申请日：2022-10-11
Applicant: NEC Laboratories America, Inc.
Inventor： Bingbing Zhuang , Samuel Schulter , Yi-Hsuan Tsai , Buyu Liu , Nanbo Li
IPC: G06T15/20 , G06T17/00 , G06V10/774 , G06V20/40 , G06V10/82
CPC classification number: G06T15/20 , G06T17/00 , G06V10/774 , G06V20/41 , G06V10/82 , G06T2210/56 , G06T2200/08
Abstract: A method for achieving high-fidelity novel view synthesis and 3D reconstruction for large-scale scenes is presented. The method includes obtaining images from a video stream received from a plurality of video image capturing devices, grouping the images into different image clusters representing a large-scale 3D scene, training a neural radiance field (NeRF) and an uncertainty multilayer perceptron (MLP) for each of the image clusters to generate a plurality of NeRFs and a plurality of uncertainty MLPs for the large-scale 3D scene, applying a rendering loss and an entropy loss to the plurality of NeRFs, performing uncertainty-based fusion to the plurality of NeRFs to define a fused NeRF, and jointly fine-tuning the plurality of NeRFs and the plurality of uncertainty MLPs, and during inference, applying the fused NeRF for novel view synthesis of the large-scale 3D scene.
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