Movatterモバイル変換


[0]ホーム

URL:


Bharadwaj et al., 2023 - Google Patents

Lane, car, traffic sign and collision detection in simulated environment using GTA-V

Bharadwaj et al., 2023

Document ID
3990684028995777037
Author
Bharadwaj R
Gajbhiye P
Rathi A
Sonawane A
Uplenchwar R
Publication year
Publication venue
International Conference on Intelligent Sustainable Systems

External Links

Snippet

Self-driving vehicles have the potential to revolutionise society and ease daily lives a little bit. When it comes to designing self-driving cars computer simulation is an essential process. However it can be challenging and time-consuming to create any such simulation …
Continue reading atlink.springer.com (other versions)

Classifications

The classifications are assigned by a computer and are not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the classifications listed.
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00791Recognising scenes perceived from the perspective of a land vehicle, e.g. recognising lanes, obstacles or traffic signs on road scenes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30781Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
    • G06F17/30799Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/20Analysis of motion
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/16Anti-collision systems
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models

Similar Documents

PublicationPublication DateTitle
Nidamanuri et al.A progressive review: Emerging technologies for ADAS driven solutions
Chi et al.Deep steering: Learning end-to-end driving model from spatial and temporal visual cues
Niranjan et al.Deep learning based object detection model for autonomous driving research using carla simulator
JP2021528798A (en) Parametric top view representation of the scene
US20210149408A1 (en)Generating Depth From Camera Images and Known Depth Data Using Neural Networks
Liu et al.Vehicle detection and ranging using two different focal length cameras
Pravallika et al.Deep learning frontiers in 3D object detection: a comprehensive review for autonomous driving
Darapaneni et al.Autonomous car driving using deep learning
Sapkal et al.Lane detection techniques for self-driving vehicle: comprehensive review
Hu et al.An image-based crash risk prediction model using visual attention mapping and a deep convolutional neural network
Maddiralla et al.Effective lane detection on complex roads with convolutional attention mechanism in autonomous vehicles
Kang et al.ETLi: Efficiently annotated traffic LiDAR dataset using incremental and suggestive annotation
US20240005794A1 (en)Adaptive perception affected by V2X signal
Bharadwaj et al.Lane, car, traffic sign and collision detection in simulated environment using GTA-V
Wang et al.ATG-PVD: ticketing parking violations on a drone
Akib et al.Integrated Traffic Violations Detection System for the Highways of Bangladesh
Abdi et al.Driver information system: A combination of augmented reality and deep learning
Gomez-Huelamo et al.360∘ real-time and power-efficient 3D DAMOT for autonomous driving applications
Gorodnichev et al.Development of software module for recognizing traffic flows through deep learning
DaiSemantic Detection of Vehicle Violation Video Based on Computer 3D Vision
Yu et al.Scene-Graph Embedding for Robust Autonomous Vehicle Perception
AhadiA Computer Vision Approach for Object Detection and Lane Segmentation in Autonomous Vehicles
Pagale et al.A review for autonomous vehicles technologies
JuanolaM. Speed Traffic Sign Detection on the CARLA Simulator Using YOLO
Joshi et al.A Novel Framework for Autonomous Driving

[8]
ページ先頭

©2009-2025 Movatter.jp