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

Yi et al., 2016 - Google Patents

Data-driven situation awareness algorithm for vehicle lane change

Yi et al., 2016

View PDF
Document ID
8096788264000350343
Author
Yi D
Su J
Liu C
Chen W
Publication year
Publication venue
2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

External Links

Snippet

A good level of situation awareness is critical for vehicle lane change decision making. In this paper, a Data-Driven Situation Awareness (DDSA) algorithm is proposed for vehicle environment perception and projection using machine learning algorithms in conjunction …
Continue reading at repository.lboro.ac.uk (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
    • 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
    • G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
    • G06K9/629—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features
    • 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
    • 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
    • G06K9/6279—Classification techniques relating to the number of classes
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Gao et al. Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections
Li et al. Lane-change intention inference based on RNN for autonomous driving on highways
Yi et al. Trajectory clustering aided personalized driver intention prediction for intelligent vehicles
Gindele et al. A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments
Käfer et al. Recognition of situation classes at road intersections
CN116323364A (en) Waypoint prediction and motion forecast for vehicle motion planning
Li et al. Importance weighted Gaussian process regression for transferable driver behaviour learning in the lane change scenario
Bharilya et al. Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
Westny et al. MTP-GO: Graph-based probabilistic multi-agent trajectory prediction with neural ODEs
Zernetsch et al. Trajectory forecasts with uncertainties of vulnerable road users by means of neural networks
Zhang et al. A framework for turning behavior classification at intersections using 3D LIDAR
Ivanovic et al. Propagating state uncertainty through trajectory forecasting
Schlechtriemen et al. A probabilistic long term prediction approach for highway scenarios
Yi et al. Data-driven situation awareness algorithm for vehicle lane change
Srinivasan et al. Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?
Otto Fusion of data from heterogeneous sensors with distributed fields of view and situation evaluation for advanced driver assistance systems
Roy et al. Runtime monitoring of deep neural networks using top-down context models inspired by predictive processing and dual process theory
WO2023242223A1 (en) Motion prediction for mobile agents
Sackmann et al. Classification of Driver Intentions at Roundabouts.
Wang et al. Deep understanding of big geospatial data for self-driving: Data, technologies, and systems
Wheeler et al. A probabilistic framework for microscopic traffic propagation
Yan et al. A game-theoretical approach to driving decision making in highway scenarios
Katrakazas Developing an advanced collision risk model for autonomous vehicles
Li et al. Driver-specific risk recognition in interactive driving scenarios using graph representation
Campbell et al. Traffic light status detection using movement patterns of vehicles