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    VIEW SYNTHESIS FOR SELF-DRIVING
    1.
    发明申请
    VIEW SYNTHESIS FOR SELF-DRIVING 有权

    公开(公告)号：US20250118009A1

    公开(公告)日：2025-04-10

    申请号：US18903348

    申请日：2024-10-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Bingbing Zhuang ,  Ziyu Jiang ,  Buyu Liu ,  Manmohan Chandraker ,  Shanlin Sun

    IPC: G06T15/08 ,  G01S17/89 ,  G06V10/774 ,  G06V20/52

    Abstract: A computer-implemented method for synthesizing an image includes capturing data from a scene and fusing grid-based representations of the scene from different encodings to inherit beneficial properties of the different encodings, The encodings include Lidar encoding and a high definition map encoding. Rays are rendered from fused grid-based representations. A density and color are determined for points in the rays. A volume rendering is employed for the rays with the density and color. An image is synthesized from the volume rendered rays with the density and the color.

    GENERATING ADVERSARIAL DRIVING SCENARIOS FOR AUTONOMOUS VEHICLES
    2.
    发明申请
    GENERATING ADVERSARIAL DRIVING SCENARIOS FOR AUTONOMOUS VEHICLES 有权

    公开(公告)号：US20250115278A1

    公开(公告)日：2025-04-10

    申请号：US18905695

    申请日：2024-10-03

    Applicant: NEC Laboratories America, Inc.

    Inventor： Francesco Pittaluga ,  Buyu Liu ,  Manmohan Chandraker ,  Kaiyuan Zhang

    IPC: B60W60/00

    Abstract: Systems and methods for generating adversarial driving scenarios for autonomous vehicles. An artificial intelligence model can compute an adversarial loss function by minimizing the distance between predicted adversarial perturbed trajectories and corresponding generated neighbor future trajectories from input data. A traffic violation loss function can be computed based on observed adversarial agents adhering to driving rules from the input data. A comfort loss function can be computed based on the predicted driving characteristics of adversarial vehicles relevant to comfort of hypothetical passengers from the input data. A planner module can be trained for autonomous vehicles based on a combined loss function of the adversarial loss function, the traffic violation loss function and the comfort loss function to generate adversarial driving scenarios. An autonomous vehicle can be controlled based on trajectories generated in the adversarial driving scenarios.

    DIVIDE-AND-CONQUER FOR LANE-AWARE DIVERSE TRAJECTORY PREDICTION
    4.
    发明申请
    DIVIDE-AND-CONQUER FOR LANE-AWARE DIVERSE TRAJECTORY PREDICTION 有权

    公开(公告)号：US20220144256A1

    公开(公告)日：2022-05-12

    申请号：US17521139

    申请日：2021-11-08

    Applicant: NEC Laboratories America, Inc.

    Inventor： Sriram Nochur Narayanan ,  Ramin Moslemi ,  Francesco Pittaluga ,  Buyu Liu ,  Manmohan Chandraker

    IPC: B60W30/09 ,  G06F16/29 ,  G06N3/08 ,  B60W30/095 ,  G08G1/16

    Abstract: A method for driving path prediction is provided. The method concatenates past trajectory features and lane centerline features in a channel dimension at an agent's respective location in a top view map to obtain concatenated features thereat. The method obtains convolutional features derived from the top view map, the concatenated features, and a single representation of the training scene the vehicle and agent interactions. The method extracts hypercolumn descriptor vectors which include the convolutional features from the agent's respective location in the top view map. The method obtains primary and auxiliary trajectory predictions from the hypercolumn descriptor vectors. The method generates a respective score for each of the primary and auxiliary trajectory predictions. The method trains a vehicle trajectory prediction neural network using a reconstruction loss, a regularization loss objective, and an IOC loss objective responsive to the respective score for each of the primary and auxiliary trajectory predictions.

    Multi-modal test-time adaptation
    6.
    发明授权
    Multi-modal test-time adaptation 有权

    公开(公告)号：US12254681B2

    公开(公告)日：2025-03-18

    申请号：US17903393

    申请日：2022-09-06

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yi-Hsuan Tsai ,  Bingbing Zhuang ,  Samuel Schulter ,  Buyu Liu ,  Sparsh Garg ,  Ramin Moslemi ,  Inkyu Shin

    IPC: G06K9/00 ,  G01S17/89 ,  G06V10/776 ,  G06V10/80

    Abstract: Systems and methods are provided for multi-modal test-time adaptation. The method includes inputting a digital image into a pre-trained Camera Intra-modal Pseudo-label Generator, and inputting a point cloud set into a pre-trained Lidar Intra-modal Pseudo-label Generator. The method further includes applying a fast 2-dimension (2D) model, and a slow 2D model, to the inputted digital image to apply pseudo-labels, and applying a fast 3-dimension (3D) model, and a slow 3D model, to the inputted point cloud set to apply pseudo-labels. The method further includes fusing pseudo-label predictions from the fast models and the slow models through an Inter-modal Pseudo-label Refinement module to obtain robust pseudo labels, and measuring a prediction consistency for the pseudo-labels. The method further includes selecting confident pseudo-labels from the robust pseudo labels and measured prediction consistencies to form a final cross-modal pseudo-label set as a self-training signal, and updating batch parameters utilizing the self-training signal.

    Human detection in scenes
    7.
    发明授权
    Human detection in scenes 有权

    公开(公告)号：US11610420B2

    公开(公告)日：2023-03-21

    申请号：US17128565

    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 ,  G06V40/10 ,  G06K9/62 ,  G06N3/04 ,  G06N3/088 ,  G06V10/25

    Abstract: Systems and methods for human 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 humans in one or more different scenes. 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.

    Parametric top-view representation of scenes
    8.
    发明授权
    Parametric top-view representation of scenes 有权

    公开(公告)号：US11373067B2

    公开(公告)日：2022-06-28

    申请号：US16526073

    申请日：2019-07-30

    Applicant: NEC Laboratories America, Inc.

    Inventor： Samuel Schulter ,  Ziyan Wang ,  Buyu Liu ,  Manmohan Chandraker

    IPC: G06K9/62 ,  B60R11/04 ,  G05D1/02 ,  G06N3/02 ,  G06V20/56 ,  H04N5/32 ,  B60W50/14 ,  B60W60/00 ,  G06N3/04 ,  G06N3/08 ,  G06V10/82 ,  H04N5/232

    Abstract: A method for implementing parametric models for scene representation to improve autonomous task performance includes generating an initial map of a scene based on at least one image corresponding to a perspective view of the scene, the initial map including a non-parametric top-view representation of the scene, implementing a parametric model to obtain a scene element representation based on the initial map, the scene element representation providing a description of one or more scene elements of the scene and corresponding to an estimated semantic layout of the scene, identifying one or more predicted locations of the one or more scene elements by performing three-dimensional localization based on the at least one image, and obtaining an overlay for performing an autonomous task by placing the one or more scene elements with the one or more respective predicted locations onto the scene element representation.

    CONSTRUCTION ZONE SEGMENTATION
    10.
    发明申请
    CONSTRUCTION ZONE SEGMENTATION 有权

    公开(公告)号：US20210110209A1

    公开(公告)日：2021-04-15

    申请号：US17128612

    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: G06K9/62 ,  G06T7/11 ,  G06K9/00 ,  G06K9/32 ,  G06N3/08

    Abstract: Systems and methods for construction zone segmentation 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 construction zones scenes having various objects. 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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