公开(公告)号：US20240241938A1

公开(公告)日：2024-07-18

申请号：US18579848

申请日：2023-02-07

Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Yong QI ,  Mingjun LIU

IPC: G06F21/33 ,  G06F7/58 ,  G06F21/45 ,  G06F21/60

CPC classification number: G06F21/33 ,  G06F7/588 ,  G06F21/45 ,  G06F21/602

Abstract: The present disclosure provides a security authentication method, system and device for IoV communication based on a national cryptographic algorithm. The method of the present disclosure includes: constructing, by an on-board unit, an identity authentication request message for transmitting to a road side unit; receiving, by the on-board unit, an identity authentication response message, and verifying a signature; and if the verification is successful, calculating, by the on-board unit, a session key and a hash value thereof, and constructing an acknowledgment message for transmitting to the road side unit, where the acknowledgment message is used in calculating a session key and a hash value thereof by the road side unit for contrast verification; if hash values are equal, security authentication is successful and a session key is generated; and if the hash values are not equal, the security authentication is failed.

公开(公告)号：US20220217170A1

公开(公告)日：2022-07-07

申请号：US17506607

申请日：2021-10-20

Applicant: Nanjing University of Science and Technology

Inventor： Yong QI ,  Jianye YU

IPC: H04L9/40 ,  G06N3/08

Abstract: An intrusion detection method and system for Internet of Vehicles based on Spark and combined deep learning are provided. The method includes the following steps: S1: setting up Spark distributed cluster; S2: initializing the Spark distributed cluster, constructing a convolutional neural network (CNN) and long short-term memory (LSTM) combined deep learning algorithm model, initializing parameters, and uploading collected data to a Hadoop distributed file system (HDFS); S3: reading the data from the HDFS for processing, and inputting the data to the CNN-LSTM combined deep learning algorithm model, for recognizing the data; and S4: dividing the data into multiple resilient distributed datasets (RDDs) for batch training with a preset number of iterations.

公开(公告)号：US20250078516A1

公开(公告)日：2025-03-06

申请号：US18681766

申请日：2023-04-20

Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Yong QI ,  Xin ZENG ,  Yuan ZHUANG

IPC: G06V20/54 ,  G06V10/75 ,  G06V10/80 ,  G06V20/70 ,  G08G1/017

Abstract: The present disclosure discloses a method and a system for detecting an abnormal traffic behavior. The method of the present disclosure includes: retaining an abnormal static target vehicle in a traffic surveillance video in a background through background modeling; performing abnormal target detection, and obtaining a cropped picture of an abnormal target vehicle and a cropped video clip through cropping; performing anomaly start time estimation, inputting the cropped picture and the cropped video clip to a network model combining twin cross-correlation with pseudo three-dimensional (P3D)-Attention, labeling a classification label on the cropped video clip, and determining a video frame when abnormal behavior occurs; and determining whether a to-be-matched vehicle is an abnormal target vehicle, and determining a start time and an end time of abnormal traffic behavior with reference to the video frame that is obtained when the abnormal behavior occurs.

公开(公告)号：US20240428361A1

公开(公告)日：2024-12-26

申请号：US18681976

申请日：2023-03-11

Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Yong QI ,  Borong LUAN ,  Zhuping ZHOU ,  Liu HE

IPC: G06Q50/40 ,  G06Q10/0631

Abstract: The present disclosure provides a coordinated and optimized dispatching method for electric buses and belongs to the technical field of smart buses. The present disclosure allows for comprehensive optimization of an electric bus dispatching strategy in time and space dimensions, establishment of a bi-level programming model for bus dispatching with consideration of a bus capacity, a transfer problem, and characteristics of electric buses, and solving of the model by a genetic algorithm. The present disclosure enables generation of a dispatching strategy for electric buses encompassing time and space aspects. The dispatching strategy is closer to an actual passenger flow situation and has better actual benefits.

公开(公告)号：US20240224041A1

公开(公告)日：2024-07-04

申请号：US18577181

申请日：2023-02-23

Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Yong QI ,  Yangwei SUN

IPC: H04W12/121 ,  G06N20/00 ,  H04L12/40

CPC classification number: H04W12/121 ,  G06N20/00 ,  H04L12/40 ,  H04L2012/40215

Abstract: The present disclosure belongs to the technical field of security of the Internet of vehicles, and discloses an intrusion detection method and device for an in-vehicle controller area network. The method of the present disclosure includes: digitizing and normalizing collected original data, obtaining preprocessed data, and dividing the preprocessed data into a training set and a test set; conducting feature selection on the preprocessed data through a particle swarm optimization (PSO)-light gradient boosting machine (GBM) bidirectional feature selection method; and classifying test set data subjected to the feature selection with a stacking integrated model, and obtaining an intrusion detection result. The present disclosure is configured to efficiently and accurately detect intrusion information appearing in the in-vehicle controller area network, and prevent security incidents of the Internet of vehicles caused by intrusion into the in-vehicle controller area network.

公开(公告)号：US20250005914A1

公开(公告)日：2025-01-02

申请号：US18711367

申请日：2023-04-21

Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Yong QI ,  Xin ZENG

IPC: G06V10/80 ,  G06V10/77 ,  G06V10/82 ,  G06V20/54 ,  G06V20/56

Abstract: The present disclosure provides a multi-task panoptic driving perception method and system based on improved You Only Look Once version 5 (YOLOv5). The method in the present disclosure includes: performing image preprocessing on an image in a dataset to obtain an input image; extracting a feature of the input image by using a backbone network of improved YOLOv5, to obtain a feature map, where the backbone network is obtained by replacing a C3 module in a backbone network of YOLOv5 with an inverted residual bottleneck module; inputting the feature map into a neck network to obtain a feature map, and fusing the obtained feature map and the feature map obtained by the backbone network; inputting the fused feature map into a detection head to perform traffic target detection; and inputting the feature map of the neck network into a branch network to perform lane line detection and drivable area segmentation.

公开(公告)号：US20230154207A1

公开(公告)日：2023-05-18

申请号：US17043681

申请日：2020-08-18

Applicant: Nanjing University of Science and Technology

Inventor： Yong QI ,  Yuan ZHUANG

IPC: G06V20/59 ,  G06V20/40 ,  G06V10/82 ,  G06V10/77 ,  G06V10/776 ,  G08B21/18

CPC classification number: G06V20/597 ,  G06V10/82 ,  G06V10/776 ,  G06V10/7715 ,  G06V20/46 ,  G06V20/49 ,  G08B21/18

Abstract: A driver fatigue detection method based on combining a pseudo-three-dimensional (P3D) convolutional neural network (CNN) and an attention mechanism includes: 1) extracting a frame sequence from a video of a driver and processing the frame sequence; 2) performing spatiotemporal feature learning through a P3D convolution module; 3) constructing a P3D-Attention module, and applying attention on channels and a feature map through the attention mechanism; and 4) replacing a 3D global average pooling layer with a 2D global average pooling layer to obtain more expressive features, and performing a classification through a Softmax classification layer. By analyzing the yawning behavior, blinking and head characteristic movements, the yawning behavior is well distinguished from the talking behavior, and it is possible to effectively distinguish between the three states of alert state, low vigilant state and drowsy state, thus improving the predictive performance of fatigue driving behaviors.