[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zhang et al., 2023 - Google Patents

Spatiotemporal interaction pattern recognition and risk evolution analysis during lane changes

Zhang et al., 2023

View PDF
Document ID
6191879303231928794
Author
Zhang Y
Zou Y
Zhang Y
Wu L
et al.
Publication year
Publication venue
IEEE Transactions on Intelligent Transportation Systems

External Links

Snippet

In complex lane change (LC) scenarios, semantic interpretation and safety analysis of dynamic interaction pattern are necessary for autonomous vehicles to make appropriate decisions. This study proposes a learning framework that combines primitive-based …
Continue reading at research.chalmers.se (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management

Similar Documents

Publication Publication Date Title
Zhang et al. Spatiotemporal interaction pattern recognition and risk evolution analysis during lane changes
Shangguan et al. An integrated methodology for real-time driving risk status prediction using naturalistic driving data
Huang et al. A probabilistic risk assessment framework considering lane-changing behavior interaction
Yi et al. Trajectory clustering aided personalized driver intention prediction for intelligent vehicles
Li et al. Attention-based lane change and crash risk prediction model in highways
Cong et al. DACR-AMTP: adaptive multi-modal vehicle trajectory prediction for dynamic drivable areas based on collision risk
Gaikwad Vehicle velocity prediction using artificial neural networks and effect of real-world signals on prediction window
Li et al. Unsupervised driving style analysis based on driving maneuver intensity
Zec et al. Statistical sensor modelling for autonomous driving using autoregressive input-output HMMs
Ma et al. Driving style estimation by fusing multiple driving behaviors: a case study of freeway in China
Jia et al. Lane‐Changing Behavior Prediction Based on Game Theory and Deep Learning
Firl Probabilistic maneuver recognition in traffic scenarios
Das Artificial intelligence in highway safety
Wang et al. Driving angle prediction of lane changes based on extremely randomized decision trees considering the harmonic potential field method
Li et al. Driver-specific risk recognition in interactive driving scenarios using graph representation
Yuan et al. Vehicle trajectory prediction based on posterior distributions fitting and TCN-transformer
Wang et al. A state dependent mandatory lane-changing model for urban arterials with hidden Markov model method
Katrakazas Developing an advanced collision risk model for autonomous vehicles
Jones et al. Probabilistic modeling of vehicle acceleration and state propagation with long short-term memory neural networks
Chai et al. Driving style recognition based on naturalistic driving: Volatilities, decision-making, and safety performances
Guo et al. Dynamic traffic graph based risk assessment of multivehicle lane change interaction scenarios
Zhang et al. Prediction of jaywalker-vehicle conflicts based on encoder-decoder framework utilizing multi-source data
Chen et al. Platoon separation strategy optimization method based on deep cognition of a driver’s behavior at signalized intersections
Ahsan et al. An ensemble time-embedded transformer model for traffic conflict prediction at RRFB pedestrian crossings
Zhang et al. V2V Spatiotemporal Interactive Pattern Recognition and Risk Analysis in Lane Changes