1 code implementation • 2 Mar 2020 • Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang, Junqiang Xi
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.
no code implementations • 11 Jan 2018 • Wenshuo Wang, Junqiang Xi, Ding Zhao
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).
no code implementations • 16 Aug 2017 • Wenshuo Wang, Junqiang Xi, Ding Zhao
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.
no code implementations • 4 Feb 2017 • Wenshuo Wang, Ding Zhao, Junqiang Xi, Wei Han
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.
no code implementations • 3 Jun 2016 • Wenshuo Wang, Junqiang Xi, Xiaohan Li
Driving styles have a great influence on vehicle fuel economy, active safety, and drivability.
no code implementations • 22 May 2016 • Wenshuo Wang, Junqiang Xi
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.