Nothing Special   »   [go: up one dir, main page]

Komol et al., 2021 - Google Patents

Deep transfer learning based intersection trajectory movement classification for big connected vehicle data

Komol et al., 2021

View PDF
Document ID
3518058577911115892
Author
Komol M
Elhenawy M
Masoud M
Glaser S
Rakotonirainy A
Wood M
Alderson D
Publication year
Publication venue
IEEE Access

External Links

Snippet

Trajectory movement labelling is an important pre-stage for predicting connected vehicle (CV) movement at intersections. Drivers' movement prediction and warning at intersections ensure advanced transportation safety and researchers use machine learning-based data …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/09Arrangements for giving variable traffic instructions
    • G08G1/0962Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125Traffic data processing
    • G08G1/0129Traffic data processing for creating historical data or processing based on historical data
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G09EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09BEDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00Teaching not covered by other main groups of this subclass
    • G09B19/16Control of vehicles or other craft
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G5/00Traffic control systems for aircraft, e.g. air-traffic control [ATC]

Similar Documents

Publication Publication Date Title
Manibardo et al. Deep learning for road traffic forecasting: Does it make a difference?
CN111179585B (en) Site testing method and device for automatic driving vehicle
CN104700646B (en) A kind of taxi exception track real-time detection method based on online gps data
Komol et al. Deep transfer learning based intersection trajectory movement classification for big connected vehicle data
US20180025291A1 (en) Data Processing System for Generating Data Structures
Kim et al. Applications of transit smart cards beyond a fare collection tool: a literature review
Zhang et al. Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning
Zhang et al. Spatiotemporal interaction pattern recognition and risk evolution analysis during lane changes
Komol et al. Deep RNN based prediction of driver’s intended movements at intersection using cooperative awareness messages
Fu et al. Constructing spatiotemporal driving volatility profiles for connected and automated vehicles in existing highway networks
Mahmud et al. Micro-level safety risk assessment model for a two-lane heterogeneous traffic environment in a developing country: A comparative crash probability modeling approach
Vlachogiannis et al. Intersense: An XGBoost model for traffic regulator identification at intersections through crowdsourced GPS data
Patil et al. Modeling vehicle collision instincts over road midblock using deep learning
Komol et al. A review on drivers’ red light running behavior predictions and technology based countermeasures
Fazekas et al. Road‐Type Detection Based on Traffic Sign and Lane Data
Shetty et al. Risk Assessment of Autonomous Vehicles across Diverse Driving Contexts
Bosurgi et al. Road functional classification using pattern recognition techniques
Masud Traffic time headway prediction and analysis: A deep learning approach
Fazekas et al. Detecting change in the urban road environment along a route based on traffic sign and crossroad data
Aziz et al. A data-driven framework to identify human-critical autonomous vehicle testing and deployment zones
Hofleitner et al. Automatic inference of map attributes from mobile data
Edara et al. MIMIC—Multidisciplinary Initiative on Methods to Integrate and Create Realistic Artificial Data
Komol C-ITS based prediction of driver red light running and turning behaviours
Zyner Naturalistic driver intention and path prediction using machine learning
Fang et al. A Novel Approach to Identify Intersection Information via Trajectory Big Data Analysis in Urban Environments