公开(公告)号：US20210065567A1

公开(公告)日：2021-03-04

申请号：US17097679

申请日：2020-11-13

Applicant: Honeywell International Inc.

Inventor： Amit SRIVASTAV ,  Rajesh CHENCHU

IPC: G08G5/06 ,  B64D47/08 ,  G06K9/00

Abstract: Disclosed are systems, methods, and non-transitory computer-readable medium for vehicle collision notification and avoidance. One system may include attaching at least one camera to a vehicle, receiving vehicle information and geographic information regarding the vehicle and at least one other vehicle. The system may also include predicting a next position of the vehicle and the other vehicle, and determine whether the vehicle will collide with the other vehicle based on a comparison of the next position of the vehicle and the next position of the other vehicle. The system may also include generating a graphical representation of the collision of the vehicle and the other vehicle and may transmit the graphic representation to a user device.

公开(公告)号：US20220103578A1

公开(公告)日：2022-03-31

申请号：US17097558

申请日：2020-11-13

Applicant: Honeywell International Inc.

Inventor： Amit SRIVASTAV ,  Rajesh CHENCHU ,  Nayyar Azam Khan RAO ,  Phani Ammi Raju POTHULA ,  Vijayshankaran IYER

IPC: H04L29/06 ,  H04L12/24 ,  H04L12/26 ,  G06N20/00 ,  H04L29/08

Abstract: A method of monitoring network traffic of a connected vehicle. The method includes receiving network traffic information from a vehicle gateway, the network traffic information including malicious and/or benign information. The method also includes storing the network traffic information on a data server and periodically updating the network traffic information stored on the data server. The method further includes: pre-processing the network traffic information, the pre-processing the network traffic information including filtering and normalizing the network traffic information; generating a learning model based on the pre-processed network traffic information, the learning model being generated by an artificial intelligence learning; updating the learning model based on additional network traffic information, the additional network traffic information including real-time network data; in accordance with the updated learning model, detecting an anomaly event in the incoming network data; and generating a notification and/or blocking one or more packets associated with the incoming network data.