Vehicle Driving Risk Prediction Based on Markov Chain Model
A driving risk status prediction algorithm based on Markov chain is presented. Driving risk states are classified using clustering techniques based on feature variables describing the instantaneous risk levels within time windows, where instantaneous risk levels are determined in time-to-collision a...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2018/4954621 |
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| _version_ | 1832564968968421376 |
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| author | Xiaoxia Xiong Long Chen Jun Liang |
| author_facet | Xiaoxia Xiong Long Chen Jun Liang |
| author_sort | Xiaoxia Xiong |
| collection | DOAJ |
| description | A driving risk status prediction algorithm based on Markov chain is presented. Driving risk states are classified using clustering techniques based on feature variables describing the instantaneous risk levels within time windows, where instantaneous risk levels are determined in time-to-collision and time-headway two-dimension plane. Multinomial Logistic models with recursive feature variable estimation method are developed to improve the traditional state transition probability estimation, which also takes into account the comprehensive effects of driving behavior, traffic, and road environment factors on the evolution of driving risk status. The “100-car” natural driving data from Virginia Tech is employed for the training and validation of the prediction model. The results show that, under the 5% false positive rate, the prediction algorithm could have high prediction accuracy rate for future medium-to-high driving risks and could meet the timeliness requirement of collision avoidance warning. The algorithm could contribute to timely warning or auxiliary correction to drivers in the approaching-danger state. |
| format | Article |
| id | doaj-art-7dc69dbb8488414a940b89e5dffa68c7 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-7dc69dbb8488414a940b89e5dffa68c72025-02-03T01:09:46ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2018-01-01201810.1155/2018/49546214954621Vehicle Driving Risk Prediction Based on Markov Chain ModelXiaoxia Xiong0Long Chen1Jun Liang2School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaA driving risk status prediction algorithm based on Markov chain is presented. Driving risk states are classified using clustering techniques based on feature variables describing the instantaneous risk levels within time windows, where instantaneous risk levels are determined in time-to-collision and time-headway two-dimension plane. Multinomial Logistic models with recursive feature variable estimation method are developed to improve the traditional state transition probability estimation, which also takes into account the comprehensive effects of driving behavior, traffic, and road environment factors on the evolution of driving risk status. The “100-car” natural driving data from Virginia Tech is employed for the training and validation of the prediction model. The results show that, under the 5% false positive rate, the prediction algorithm could have high prediction accuracy rate for future medium-to-high driving risks and could meet the timeliness requirement of collision avoidance warning. The algorithm could contribute to timely warning or auxiliary correction to drivers in the approaching-danger state.http://dx.doi.org/10.1155/2018/4954621 |
| spellingShingle | Xiaoxia Xiong Long Chen Jun Liang Vehicle Driving Risk Prediction Based on Markov Chain Model Discrete Dynamics in Nature and Society |
| title | Vehicle Driving Risk Prediction Based on Markov Chain Model |
| title_full | Vehicle Driving Risk Prediction Based on Markov Chain Model |
| title_fullStr | Vehicle Driving Risk Prediction Based on Markov Chain Model |
| title_full_unstemmed | Vehicle Driving Risk Prediction Based on Markov Chain Model |
| title_short | Vehicle Driving Risk Prediction Based on Markov Chain Model |
| title_sort | vehicle driving risk prediction based on markov chain model |
| url | http://dx.doi.org/10.1155/2018/4954621 |
| work_keys_str_mv | AT xiaoxiaxiong vehicledrivingriskpredictionbasedonmarkovchainmodel AT longchen vehicledrivingriskpredictionbasedonmarkovchainmodel AT junliang vehicledrivingriskpredictionbasedonmarkovchainmodel |