Driver Intention Recognition Method Using Continuous Hidden Markov Model

Hou, Haijing; Jin, Lisheng; Niu, Qingning; Sun, Yonghai; Lu, Meng

doi:10.1080/18756891.2011.9727797

Cited by 51 publications

(18 citation statements)

References 10 publications

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“…Formally, a Markov chain [30][31][32][33][34][35][36] is a discrete (discrete-time) random process with the Markov property. Often, the term "Markov chain" is used to mean a Markov process which has a discrete (finite or countable) state-space.…”

Section: Solving By Using Markov Chain Methodsmentioning

confidence: 99%

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Qualitative Description and Quantitative Optimization of Tactical Reconnaissance Agents System Organization

Li¹,

Chen

Zhang

2012

IJCIS

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In this paper, the problem of qualitative description and quantitative optimization for tactical reconnaissance agents system organization is considered with objective of higher teamwork efficiency and more reasonable task balancing strategies. By analyzing tactical reconnaissance system and its environment, task-(role)-entity agent mapping mechanism and agents in system organization, the system framework is qualitatively described. By transforming the system into an interaction task request-service mechanism queuing system, a Markov chain of system state transition is obtained, since its state transition process in interaction is Markov process and accords with real tactical reconnaissance behaviors. By solving the state transition equations, the inherent relationship of tactical reconnaissance agents is found and the optimized system configuration is obtained. The established simulation demonstration system proves that the proposed approach and model are feasible and effective.

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Section: Solving By Using Markov Chain Methodsmentioning

confidence: 99%

“…The steps are often thought of as time, but they can equally well refer to physical distance or any other discrete measurement; formally, the steps are just the integers or natural numbers, and the random process is a mapping of these to states [32][33][34][35][36][37][38][39].…”

Section: Solving By Using Markov Chain Methodsmentioning

confidence: 99%

Qualitative Description and Quantitative Optimization of Tactical Reconnaissance Agents System Organization

Li¹,

Chen

Zhang

2012

IJCIS

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“…Additionally, the eye and gaze data are also used for early detection of a driver's intentions, which is an interesting feature of ADAS. Most schemes developed for prediction of a driver's maneuvering behavior are principally based on the hidden Markov model (HMM) and its variants [209][210][211][212]. These schemes are applied to the data obtained from the driver's gaze sequence [9] and head position [213].…”

Section: Data Processing Algorithmsmentioning

confidence: 99%

Gaze and Eye Tracking: Techniques and Applications in ADAS

Khan

Lee

2019

Sensors

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Tracking drivers’ eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS). It is especially a matter of serious discussion among the road safety researchers’ community, as visual distraction is considered among the major causes of road accidents. In this paper, techniques for eye and gaze tracking are first comprehensively reviewed while discussing their major categories. The advantages and limitations of each category are explained with respect to their requirements and practical uses. In another section of the paper, the applications of eyes and gaze tracking systems in ADAS are discussed. The process of acquisition of driver’s eyes and gaze data and the algorithms used to process this data are explained. It is explained how the data related to a driver’s eyes and gaze can be used in ADAS to reduce the losses associated with road accidents occurring due to visual distraction of the driver. A discussion on the required features of current and future eye and gaze trackers is also presented.

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“…In the activation of ADAS, however, there can be conflicting situations where the intervention of the ADAS can interfere with the driver’s intention of operation. Correct prediction of driver’s intention is an essential part to determine whether the ADAS should engage to override the driver’s control inputs [1]. …”

Section: Introductionmentioning

confidence: 99%

Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

Kim

Bong

Park

et al. 2017

Sensors

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Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

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Driver Intention Recognition Method Using Continuous Hidden Markov Model

Cited by 51 publications

References 10 publications

Qualitative Description and Quantitative Optimization of Tactical Reconnaissance Agents System Organization

Qualitative Description and Quantitative Optimization of Tactical Reconnaissance Agents System Organization

Gaze and Eye Tracking: Techniques and Applications in ADAS

Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

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