Component behavior prediction and monitoring through built-in test

2004 ◽  
Author(s):  
F. Barbier ◽  
N. Belloir
Keyword(s):  
Behavior Prediction

Personality and behavior prediction and consistency across cultures: A multimethod study of Blacks and Whites in South Africa.

2018 ◽  
Vol 114 (3) ◽  
pp. 465-481 ◽  
Author(s):  
Velichko H. Fetvadjiev ◽  
Deon Meiring ◽  
Fons J. R. van de Vijver ◽  
J. Alewyn Nel ◽  
Lusanda Sekaja ◽  
...  
Keyword(s):  
South Africa ◽  
Behavior Prediction ◽  
Blacks And Whites ◽  
And Behavior

scholarly journals Interaction-Aware Cut-in Behavior Prediction and Risk Assessment for Autonomous Driving

2020 ◽  
Vol 53 (5) ◽  
pp. 656-663
Author(s):  
Jinwei Zhang ◽  
Guofa Li ◽  
Zejian Deng ◽  
Huilong Yu ◽  
Jan P. Huissoon ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Autonomous Driving ◽  
Behavior Prediction

scholarly journals Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain

2021 ◽  
Vol 13 (10) ◽  
pp. 5690
Author(s):  
Chengyuan Mao ◽  
Lewen Bao ◽  
Shengde Yang ◽  
Wenjiao Xu ◽  
Qin Wang
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Warning System ◽  
Demographic Characteristics ◽  
Chain Model ◽  
Behavior Prediction ◽  
Road Geometry ◽  
Using Data ◽  
Pedestrian Crossing ◽  
Prediction Approach

Pedestrian violations pose a danger to themselves and other road users. Most previous studies predict pedestrian violation behaviors based only on pedestrians’ demographic characteristics. In practice, in addition to demographic characteristics, other factors may also impact pedestrian violation behaviors. Therefore, this study aims to predict pedestrian crossing violations based on pedestrian attributes, traffic conditions, road geometry, and environmental conditions. Data on the pedestrian crossing, both in compliance and in violation, were collected from 10 signalized intersections in the city of Jinhua, China. We propose an illegal pedestrian crossing behavior prediction approach that consists of a logistic regression model and a Markov Chain model. The former calculates the likelihood that the first pedestrian who decides to cross the intersection illegally within each signal cycle, while the latter computes the probability that the subsequent pedestrians who decides to follow the violation. The proposed approach was validated using data gathered from an additional signalized intersection in Jinhua city. The results show that the proposed approach has a robust ability in pedestrian violation behavior prediction. The findings can provide theoretical references for pedestrian signal timing, crossing facility optimization, and warning system design.

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