Journal ArticleDOI
Lane changing intention recognition based on speech recognition models
Reads0
Chats0
TLDR
A novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering techniques to recognize a driver’s lane changing intention and can achieve a recognition accuracy of 93.5% and 90.3% which is a significant improvement compared with the HMM-only algorithm.Abstract:
Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.read more
Citations
More filters
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
A Primer of Signal Detection Theory; Table of D' and β
TL;DR: The laws of categorical and comparative judgements of signal detection have been studied in the literature as mentioned in this paper for signal detection with equal variance with equal Variances, i.e., Gaussian Distributions of Signal and Noise with Unequal Variants.
Journal ArticleDOI
Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges
Yang Xing,Chen Lv,Huaji Wang,Hong Wang,Yunfeng Ai,Dongpu Cao,Efstathios Velenis,Fei-Yue Wang +7 more
TL;DR: An overview of the ego-vehicle driver intention inference (DII), which mainly focuses on the lane change intention on highways, is provided.
Journal ArticleDOI
A data-driven lane-changing model based on deep learning
TL;DR: This may be the first work that comprehensively models LC using deep learning approaches and the results indicate that the proposed data-driven model is able to accurately predict the LC process of a vehicle.
Journal ArticleDOI
A Novel Lane Change Decision-Making Model of Autonomous Vehicle Based on Support Vector Machine
TL;DR: An autonomous lane change decision-making model based on benefit, safety, and tolerance by analyzing the factors of the autonomous vehicle lane change is established and a support vector machine (SVM) algorithm with the Bayesian parameters optimization is adopted to solve this problem.
References
More filters
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
An introduction to hidden Markov models
TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
Proceedings ArticleDOI
Coupled hidden Markov models for complex action recognition
TL;DR: Algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions are presented.