Moosavi et al., 2021 - Google Patents
Driving style representation in convolutional recurrent neural network model of driver identificationMoosavi et al., 2021
View PDF- Document ID
- 9591550368925463074
- Author
- Moosavi S
- Mahajan P
- Parthasarathy S
- Saunders-Chukwu C
- Ramnath R
- Publication year
- Publication venue
- arXiv preprint arXiv:2102.05843
External Links
Snippet
Identifying driving styles is the task of analyzing the behavior of drivers in order to capture variations that will serve to discriminate different drivers from each other. This task has become a prerequisite for a variety of applications, including usage-based insurance, driver …
- 230000000306 recurrent 0 title abstract description 27
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
- G06K9/6267—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
- G06K9/62—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
- 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
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Moosavi et al. | Driving style representation in convolutional recurrent neural network model of driver identification | |
Azadani et al. | Driving behavior analysis guidelines for intelligent transportation systems | |
Dong et al. | Characterizing driving styles with deep learning | |
Xing et al. | Personalized vehicle trajectory prediction based on joint time-series modeling for connected vehicles | |
Cura et al. | Driver profiling using long short term memory (LSTM) and convolutional neural network (CNN) methods | |
Malik et al. | Driving pattern profiling and classification using deep learning | |
Khodairy et al. | Driving behavior classification based on oversampled signals of smartphone embedded sensors using an optimized stacked-LSTM neural networks | |
Chen et al. | Understanding individualization driving states via latent Dirichlet allocation model | |
Tselentis et al. | Driver profile and driving pattern recognition for road safety assessment: Main challenges and future directions | |
Siami et al. | A mobile telematics pattern recognition framework for driving behavior extraction | |
CN114299607B (en) | A human-vehicle collision risk analysis method based on autonomous driving vehicles | |
Li et al. | Driver identification in intelligent vehicle systems using machine learning algorithms | |
CN110949398A (en) | Method for detecting abnormal driving behavior of first-vehicle drivers in vehicle formation driving | |
Jeong et al. | Real-time driver identification using vehicular big data and deep learning | |
Hema et al. | Hyperparameter optimization of LSTM based Driver’s aggressive behavior prediction model | |
Xue et al. | A context-aware framework for risky driving behavior evaluation based on trajectory data | |
Azadani et al. | Driver identification using vehicular sensing data: A deep learning approach | |
Hu et al. | Driver identification using 1D convolutional neural networks with vehicular CAN signals | |
Taherifard et al. | Attention-based event characterization for scarce vehicular sensing data | |
Zhang et al. | Stacking‐based ensemble learning method for the recognition of the preceding vehicle lane‐changing manoeuvre: A naturalistic driving study on the highway | |
Ahmed et al. | Convolutional neural network for driving maneuver identification based on inertial measurement unit (IMU) and global positioning system (GPS) | |
Bernardi et al. | Driver identification: a time series classification approach | |
Lee et al. | A privacy-preserving learning method for analyzing hev driver’s driving behaviors | |
Gross et al. | Route and stopping intent prediction at intersections from car fleet data | |
Islam et al. | Elevating driver behavior understanding with rknd: A novel probabilistic feature engineering approach |