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Found 6 papers, 1 papers with code
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships.
Learning and Inferring a Driver's Braking Action in Car-Following Scenarios
A learning-based inference method, using onboard data from CAN-Bus, radar and cameras as explanatory variables, is introduced to infer drivers' braking decisions by combining a Gaussian mixture model (GMM) with a hidden Markov model (HMM).
Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches
In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns.
A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model
Second, based on this model, we develop an online model-based prediction algorithm to predict the forthcoming vehicle trajectory and judge whether the driver will demonstrate an LDB or a DCB.
Statistical Pattern Recognition for Driving Styles Based on Bayesian Probability and Kernel Density Estimation
Driving styles have a great influence on vehicle fuel economy, active safety, and drivability.
A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machines
To shorten the recognition time and improve the recognition of driving styles, a k-means clustering-based support vector machine ( kMC-SVM) method is developed and used for classifying drivers into two types: aggressive and moderate.
