Prediction Method of Driver’s Propensity Adapted to Driver’s Dynamic Feature Extraction of Affection
Reads0
Chats0
TLDR
Results show that this method is better than the traditional psychology test, and it provides a basis for further studying dynamic characteristics of driver's affection.Abstract:
Driver’s propensity is a dynamic measurement of driver’s characteristics, such as affection and preference. In the vehicle driver-assistance system, especially its collision warning subsystem, it is also an important parameter of computing driver's intention. The prediction of driver’s propensity from relative static and macroscopic perspective is an essential precondition for further researching and extracting dynamic characteristics. Physiology and psychology tests are designed to measure driver’s character and calculate physiological rhythm. Changing data of driver’s psychology and emotion during driving are obtained by real vehicle test. Then driver’s propensity values of different types are calculated by weighting method according to the contribution rate of standard features. Results show that this method is better than the traditional psychology test, and it provides a basis for further studying dynamic characteristics of driver’s affection.read more
Citations
More filters
Journal ArticleDOI
Dynamic Recognition of Driver’s Propensity Based on GPS Mobile Sensing Data and Privacy Protection
TL;DR: Dynamic recognition model of driver’s propensity based on support vector machine is established taking the vehicle safety controlled technology and respecting and protecting the driver's privacy as precondition, and results show that the established recognition model is reasonable and feasible.
Journal ArticleDOI
A Real-Time Recognition System of Driving Propensity Based on AutoNavi Navigation Data
Xiaoyuan Wang,Long Chen,Huili Shi,Junyan Han,Geyang Wang,Quanzheng Wang,Fusheng Zhong,Hao Li +7 more
TL;DR: A novel and convenient method for building personalized intelligent driver assistance systems in practical applications based on AutoNavi navigation data based on driving propensity characteristics variables is proposed.
Journal ArticleDOI
An In-Vehicle Application Providing System Based on Driver’s Biodata
Kwang-Ho Seok,Yoon Sang Kim +1 more
TL;DR: It is found that the number of the data used had a significant influence on improving the driver’s satisfaction level, playing an important role in reflecting driver's status.
Book ChapterDOI
Machine Learning Recognition Mechanism Based on WI-FI Signal Optimization in the Detection of Driver’s Emotional Fluctuations
TL;DR: In this paper , the authors proposed an artificial intelligence machine learning mechanism under Industry 4.0 technology to capture and detect the instantaneous facial emotions of drivers during driving and achieved an accuracy of 85% in the real scene.
References
More filters
Journal ArticleDOI
An Adaptive Longitudinal Driving Assistance System Based on Driver Characteristics
TL;DR: A prototype of a longitudinal driving-assistance system, which is adaptive to driver behavior, is developed, and results show that the self-learning algorithm is effective and that the system can, to some extent, adapt to individual characteristics.
Journal ArticleDOI
Frequency, determinants, and consequences of different drivers’ emotions: An on-the-road study using self-reports, (observed) behaviour, and physiology
TL;DR: In this paper, the frequency, determinants and consequences of three relevant emotions in traffic were investigated based on appraisal theory, and it was predicted that the combination of three appraisal components (goal congruence, blame and threat) affects the occurrence of anger, anxiety and happiness.
Journal ArticleDOI
Influence of street characteristics, driver category and car performance on urban driving patterns
TL;DR: In this paper, a data set of over 14,000 driving patterns registered in actual traffic is used to obtain a better understanding of the variables that affect driving patterns, by determining the extent they are influenced by street characteristics and/or driver-car categories.
Journal ArticleDOI
Correlation between driving errors and vigilance level: influence of the driver's age.
TL;DR: In older drivers, in comparison with young and middle-aged drivers, the degradation of driving performance was correlated to the evolution of lower frequency waking EEG (i.e., theta), and the deterioration of the vigilance level attested by EEG correlated with the increase in gravity of all studied driving errors in older drivers.
Journal ArticleDOI
Negative or positive? The effect of emotion and mood on risky driving
Tian-Yi Hu,Xiaofei Xie,Jiang Li +2 more
TL;DR: In this paper, the authors explored how two states of affect, emotion and mood, would influence driver's risky driving behavior through risk perception and risk attitude, and found that negative affect played an opposite and more powerful role compared to positive affect.