ROAD SURFACE CONDITIONS DETECTION BY DISTRIBUTED OPTIC FIBER SYSTEM

    公开(公告)号:US20230152150A1

    公开(公告)日:2023-05-18

    申请号:US17987007

    申请日:2022-11-15

    CPC classification number: G01H9/004 G01P3/36 G06N20/10 G06N3/08

    Abstract: A fiber optic sensing cable located along a side of a paved road and runs parallel to a driving direction is monitored by distributed fiber optic sensing (DFOS) using Rayleigh backscattering generated along the length of the optical sensor fiber cable under dynamic vehicle loads. The interaction of vehicles with roadway locations exhibiting distressed pavement generates unique patterns of localized signals that are identified/distinguished from signals resulting from vehicles operating on roadway exhibiting a smooth pavement surface. Machine learning methods are employed to estimate an overall road surface quality as well as localizing pavement damage. Power spectral density estimation, principal component analysis, support vector machine (SVM) combined with principal component analysis (PCA), local binary pattern (LBP), and convolutional neural network (CNN) are applied to develop the machine learning models.

    FREQUENCY-DRIFT COMPENSATION IN CHIRPED-PULSE-BASED DISTRIBUTED ACOUSTIC SENSING

    公开(公告)号:US20230146473A1

    公开(公告)日:2023-05-11

    申请号:US17967812

    申请日:2022-10-17

    CPC classification number: G01H9/004 G01D5/268 G01D5/35361 H04L27/2278

    Abstract: Aspects of the present disclosure directed to frequency drift compensation for coded-DAS systems that use chirped pulses as a probe signal. Our inventive approach estimates timing jitter by correlating the amplitude of the estimated Rayleigh impulse response of every frame with a reference frame, and then re-aligns each frame by the estimated timing jitter. As the amount of timing jitter varies within a frame, every frame is divided into blocks where all samples have similar timing jitter, and perform timing jitter estimation and compensation on a block-by-block, frame-by-frame basis using an overlap-and-save method. Tracking of a slowly changing channel is enabled by allowing the reference frame to be periodically updated.

    DYNAMIC ROAD TRAFFIC NOISE MAPPING USING DISTRIBUTED FIBER OPTIC SENSING (DFOS) OVER TELECOM NETWORK

    公开(公告)号:US20230125456A1

    公开(公告)日:2023-04-27

    申请号:US17968265

    申请日:2022-10-18

    Abstract: Aspects of the present disclosure describe dynamic road traffic noise mapping using DFOS over a telecommunications network that enables mapping of road traffic-induced noise at any observer location. DFOS is used to obtain instant traffic data including vehicle speed, volume, and vehicle types, based on vibration and acoustic signal along the length of a sensing fiber along with location information. A sound pressure level at a point of interest is determined, and traffic data associated with such point is incorporated into a reference noise emission database and a wave propagation theory for total sound pressure level prediction and mapping. Real-time wind speed using DFOS—such as distributed acoustic sensing (DAS)—is obtained to provide sound pressure adjustment due to the wind speed.

    DATA FUSION AND ANALYSIS ENGINE FOR VEHICLE SENSORS

    公开(公告)号:US20230112441A1

    公开(公告)日:2023-04-13

    申请号:US17961169

    申请日:2022-10-06

    Abstract: Systems and methods for data fusion and analysis of vehicle sensor data, including receiving a multiple modality input data stream from a plurality of different types of vehicle sensors, determining latent features by extracting modality-specific features from the input data stream, and aligning a distribution of the latent features of different modalities by feature-level data fusion. Classification probabilities can be determined for the latent features using a fused modality scene classifier. A tree-organized neural network can be trained to determine path probabilities and issue driving pattern judgments, with the tree-organized neural network including a soft tree model and a hard decision leaf. One or more driving pattern judgments can be issued based on a probability of possible driving patterns derived from the modality-specific features.

    SELF-SUPERVISED MULTIMODAL REPRESENTATION LEARNING WITH CASCADE POSITIVE EXAMPLE MINING

    公开(公告)号:US20230086023A1

    公开(公告)日:2023-03-23

    申请号:US17940599

    申请日:2022-09-08

    Abstract: A method for model training and deployment includes training, by a processor, a model to learn video representations with a self-supervised contrastive loss by performing progressive training in phases with an incremental number of positive instances from one or more video sequences, resetting the learning rate schedule in each of the phases, and inheriting model weights from a checkpoint from a previous training phase. The method further includes updating the trained model with the self-supervised contrastive loss given multiple positive instances obtained from Cascade K-Nearest Neighbor mining of the one or more video sequences by extracting features in different modalities to compute similarities between the one or more video sequences and selecting a top-k similar instances with features in different modalities. The method also includes fine-tuning the trained model for a downstream task. The method additionally includes deploying the trained model for a target application inference for the downstream task.

    RUT DETECTION FOR ROAD INFRASTRUCTURE

    公开(公告)号:US20230073055A1

    公开(公告)日:2023-03-09

    申请号:US17903383

    申请日:2022-09-06

    Abstract: A computer-implemented method for rut detection is provided. The method includes detecting, by a rut detection system, areas in a road-scene image that include ruts with pixel-wise probability values, wherein a higher value indicates a better chance of being a rut. The method further includes performing at least one of rut repair and vehicle rut avoidance responsive to the pixel-wise probability values. The detecting step includes performing neural network-based, pixel-wise semantic segmentation with context information on the road-scene image to distinguish rut pixels from non-rut pixels on a road depicted in the road-scene image.

    Deep face recognition based on clustering over unlabeled face data

    公开(公告)号:US11600113B2

    公开(公告)日:2023-03-07

    申请号:US17091066

    申请日:2020-11-06

    Abstract: A computer-implemented method for implementing face recognition includes obtaining a face recognition model trained on labeled face data, separating, using a mixture of probability distributions, a plurality of unlabeled faces corresponding to unlabeled face data into a set of one or more overlapping unlabeled faces that include overlapping identities to those in the labeled face data and a set of one or more disjoint unlabeled faces that include disjoint identities to those in the labeled face data, clustering the one or more disjoint unlabeled faces using a graph convolutional network to generate one or more cluster assignments, generating a clustering uncertainty associated with the one or more cluster assignments, and retraining the face recognition model on the labeled face data and the unlabeled face data to improve face recognition performance by incorporating the clustering uncertainty.

    Universal feature representation learning for face recognition

    公开(公告)号:US11580780B2

    公开(公告)日:2023-02-14

    申请号:US17091011

    申请日:2020-11-06

    Abstract: A computer-implemented method for implementing face recognition includes receiving training data including a plurality of augmented images each corresponding to a respective one of a plurality of input images augmented by one of a plurality of variations, splitting a feature embedding generated from the training data into a plurality of sub-embeddings each associated with one of the plurality of variations, associating each of the plurality of sub-embeddings with respective ones of a plurality of confidence values, and applying a plurality of losses including a confidence-aware identification loss and a variation-decorrelation loss to the plurality of sub-embeddings and the plurality of confidence values to improve face recognition performance by learning the plurality of sub-embeddings.

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