公开(公告)号：US20240134038A1

公开(公告)日：2024-04-25

申请号：US17938876

申请日：2022-10-07

Applicant: Aptiv Technologies Limited

Inventor： Shan Zhang ,  Kanishka Tyagi ,  Steven Shaw ,  Narbik Manukian

IPC: G01S13/931 ,  B60W30/095 ,  G01S13/89 ,  G06N3/08

CPC classification number: G01S13/931 ,  B60W30/0956 ,  G01S13/89 ,  G06N3/08 ,  B60W60/0015 ,  B60W2420/52

Abstract: This document describes techniques and systems for stationary object detection and classification based on low-level radar data. Raw electromagnetic signals reflected off stationary objects and received by a radar system may be preprocessed to produce low-level spectrum data in the form of range-Doppler maps that retain all or nearly all the data present in the raw electromagnetic signals. The preprocessing may also filter non-stationary range-Doppler bins. The remaining low-level spectrum data represents stationary objects present in a field-of-view (FOV) of the radar system. The low-level spectrum data representing stationary objects can be fed to an end-to-end deep convolutional detection and classification network that is trained to classify and provide object bounding boxes for the stationary objects. The outputted classifications and bounding boxes related to the stationary objects may be provided to other driving systems to improve their functionality resulting in a safer driving experience.

公开(公告)号：US20220319328A1

公开(公告)日：2022-10-06

申请号：US17219760

申请日：2021-03-31

Applicant: Aptiv Technologies Limited

Inventor： Shan Zhang ,  Narbik Manukian

IPC: G08G1/16 ,  G06T7/62 ,  G06T7/70 ,  G06T7/571 ,  G06K9/00 ,  G06K9/62 ,  G01S13/86 ,  G01S13/931 ,  G01S7/41 ,  B60W60/00 ,  B60W30/09

Abstract: This document describes techniques, apparatuses, and systems for sensor fusion for object-avoidance detection, including stationary-object height estimation. A sensor fusion system may include a two-stage pipeline. In the first stage, time-series radar data passes through a detection model to produce radar range detections. In the second stage, based on the radar range detections and camera detections, an estimation model detects an over-drivable condition associated with stationary objects in a travel path of a vehicle. By projecting radar range detections onto pixels of an image, a histogram tracker can be used to discern pixel-based dimensions of stationary objects and track them across frames. With depth information, a highly accurate pixel-based width and height estimation can be made, which after applying over-drivability thresholds to these estimations, a vehicle can quickly and safely make over-drivability decisions about objects in a road.

公开(公告)号：US20230140890A1

公开(公告)日：2023-05-11

申请号：US17661223

申请日：2022-04-28

Applicant: Aptiv Technologies Limited

Inventor： Kanishka Tyagi ,  Yihang Zhang ,  Kaveh Ahmadi ,  Shan Zhang ,  Narbik Manukian

IPC: G01S13/89 ,  G06T3/40

CPC classification number: G01S13/89 ,  G06T3/4053 ,  G06T3/4046

Abstract: This document describes techniques and systems for machine-learning-based super resolution of radar data. A low-resolution radar image can be used as input to train a model for super resolution of radar data. A higher-resolution radar image, generated by an effective, but costly in terms of computing resources, traditional super resolution method, and the higher-resolution image can serve as ground truth for training the model. The resulting trained model may generate a high-resolution sensor image that closely approximates the image generated by the traditional method. Because this trained model needs only to be executed in feed-forward mode in the inference stage, it may be suited for real-time applications. Additionally, if low-level radar data is used as input for training the model, the model may be trained with more comprehensive information than can be obtained in detection level radar data.

公开(公告)号：US20230176190A1

公开(公告)日：2023-06-08

申请号：US18060088

申请日：2022-11-30

Applicant: Aptiv Technologies Limited

Inventor： Jens Westerhoff ,  Shan Zhang ,  Yihang Zhang ,  Narbik Manukian

IPC: G01S7/48 ,  G01S13/86 ,  G01S13/42 ,  G01S13/931 ,  G01S17/931

CPC classification number: G01S7/4802 ,  G01S13/42 ,  G01S13/865 ,  G01S13/931 ,  G01S17/931

Abstract: This document describes systems and techniques for determining a height of an object in a surrounding of a vehicle. In a first aspect, the systems and techniques include acquiring radar data for each of a plurality of vertically distributed antenna elements of a radar antenna. In additional aspects, the systems and techniques include estimating an elevation spectrum from the acquired radar data, extracting one or more features representative of the shape of the estimated elevation spectrum, and determining the height of the object using the extracted one or more features.

公开(公告)号：US20220335279A1

公开(公告)日：2022-10-20

申请号：US17230877

申请日：2021-04-14

Applicant: Aptiv Technologies Limited

Inventor： Kanishka Tyagi ,  Yihang Zhang ,  John Kirkwood ,  Shan Zhang ,  Sanling Song ,  Narbik Manukian

IPC: G06N3/063 ,  G06N20/00 ,  G06N3/08 ,  G01S13/931

Abstract: This document describes techniques and systems related to a radar system using a machine-learned model for stationary object detection. The radar system includes a processor that can receive radar data as time-series frames associated with electromagnetic (EM) energy. The processor uses the radar data to generate a range-time map of the EM energy that is input to a machine-learned model. The machine-learned model can receive as inputs extracted features corresponding to the stationary objects from the range-time map for multiple range bins at each of the time-series frames. In this way, the described radar system and techniques can accurately detect stationary objects of various sizes and extract critical features corresponding to the stationary objects.

公开(公告)号：US20230410490A1

公开(公告)日：2023-12-21

申请号：US17929612

申请日：2022-09-02

Applicant: Aptiv Technologies Limited

Inventor： Shan Zhang ,  Nianxia Cao ,  Kanishka Tyagi ,  Xiaohui Wang ,  Narbik Manukian

IPC: G06V10/80 ,  G01S13/86 ,  G01S17/86 ,  G01S13/931 ,  G01S17/931 ,  G06V20/56

CPC classification number: G06V10/806 ,  G01S13/865 ,  G01S13/867 ,  G01S17/86 ,  G01S13/931 ,  G01S17/931 ,  G06V20/56

Abstract: This document describes systems and techniques related to deep association for sensor fusion. For example, a model trained using deep machine learning techniques, may be used to generate an association score matrix that includes probabilities that tracks from different types of sensors are related to the same objects. This model may be trained using a convolutional recurrent neural network and include constraints not included in other training techniques. Focal loss can be used during training to compensate for imbalanced data samples and address difficult cases, and data expansion techniques can be used to increase the multi-sensor data space. Simple thresholding techniques can be applied to the association score matrix to generate an assignment matrix that indicates whether tracks from one sensor and tracks from another sensor match. In this manner, the track association process may be more accurate than current sensor fusion techniques, and vehicle safety may be increased.