[1] Sun Wei, Zhang Weigong, Li Xu, Chen Gang, et al. Driving fatigue fusion detection based on T-S fuzzy neural networkevolved by subtractive clustering and particle swarm optimization [J]. Journal of Southeast University (English Edition), 2009, 25 (3): 356-361. [doi:10.3969/j.issn.1003-7985.2009.03.015]
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Driving fatigue fusion detection based on T-S fuzzy neural networkevolved by subtractive clustering and particle swarm optimization(
)
减法聚类和粒子群优化TS模糊神经网络的驾驶疲劳融合检测
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 25
- Issue:
- 2009 3
- Page:
- 356-361
- Research Field:
- Traffic and Transportation Engineering
- Publishing date:
- 2009-09-30
Info
- Title:
- Driving fatigue fusion detection based on T-S fuzzy neural networkevolved by subtractive clustering and particle swarm optimization
- 减法聚类和粒子群优化TS模糊神经网络的驾驶疲劳融合检测
- Author(s):
- Sun Wei; Zhang Weigong; Li Xu; Chen Gang
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
- Keywords:
- driving fatigue; fusion detection; particle swarm optimization(PSO); subtractive clustering(SC)
- PACS:
- U495
- DOI:
- 10.3969/j.issn.1003-7985.2009.03.015
- Abstract:
- In order to improve the accuracy and reliability of the driving fatigue detection based on a single feature, a new detection algorithm based on multiple features is proposed.Two direct driver’s facial features reflecting fatigue and one indirect vehicle behavior feature indicating fatigue are considered.Meanwhile, T-S fuzzy neural network(TSFNN)is adopted to recognize the driving fatigue of drivers.For the structure identification of the TSFNN, subtractive clustering(SC)is used to confirm the fuzzy rules and their correlative parameters.Moreover, the particle swarm optimization(PSO)algorithm is improved to train the TSFNN.Simulation results and experiments on vehicles show that the proposed algorithm can effectively improve the convergence speed and the recognition accuracy of the TSFNN, as well as enhance the correct rate of driving fatigue detection.
- 为提高基于单一特征检测算法的准确率和可靠性, 提出基于多个特征的驾驶疲劳融合检测算法.从直接反映驾驶员疲劳的2个面部特征和间接反映疲劳的1个车辆行为特征2个方面对驾驶疲劳进行综合检测.该算法运用TS模糊神经网络来识别驾驶疲劳, 采用减法聚类对网络进行结构辨识, 确定模糊规则的条数及相关参数的初始值, 并改进了粒子群优化算法对网络进行训练.仿真和实车实验表明, 该算法不仅能有效改善TS模糊神经网络的收敛速度和识别精度, 而且能提高驾驶疲劳的检测正确率.
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Memo
- Memo:
-
Biographies: Sun Wei(1980—), male, graduate;Zhang Weigong(corresponding author), male, doctor, professor, zhangwg@seu.edu.cn.
Foundation items: The National Key Technologies R& D Program during the 11th Five-Year Plan Period(No.2009BAG13A04), the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061), the Transportation Science Research Project of Jiangsu Province(No.08X09).
Citation: Sun Wei, Zhang Weigong, Li Xu, et al.Driving fatigue fusion detection based on T-S fuzzy neural network evolved by subtractive clustering and particle swarm optimization[J].Journal of Southeast University(English Edition), 2009, 25(3):356-361.