D. Deva Hemaa,b,* and K. Ashok Kumara
| 1. National Highway Traffic Safety Administration, Fatality analysis reporting system (fars), https://www. fars.nhtsa.dot.gov/Main/index.aspx. (accessed on November 2021)
2. Lajunen, T. and Parker, D.Are aggressive people aggressive drivers? A study of the relationship between self-reported general aggressiveness, driver anger and aggressive driving.
3. Devaraj D., Chandrasekaran D., Pandian B., Binitha B., Dipikhasre M., andAnil R.Road accident analysis in kerala and location based severity level classification using decision tree algorithm.
4. Deva Hema, D., Nandhini, S., VisnuDharsini, S., and ShivaNandhini, J. Intelligent speed control in motor bikes for accident prevention using internet of things.
5. Jo J., Lee S.J., Park K.R., Kim I.J., andKim J.Detecting driver drowsiness using feature-level fusion and user-specific classification.
6. Kumtepe O., Akar G.B., andYuncu E.Driver aggressiveness detection via multisensory data fusion.
7. Jian-Qiang, G. and Yi-ying, W. Research on online identification algorithm of dangerous driving behavior. In
8. Veeramuthu A., Meenakshi S., andAshok Kumar, K. A neural network based deep learning approach for efficient segmentation of brain tumor medical image data.
9. Wang, Y. and Ho, I.W.H. Joint deep neural network modelling and statistical analysis on characterizing driving behaviors. In
10. Shahverdy M., Fathy M., Berangi R., andSabokrou M.Driver behavior detection and classification using deep convolutional neural networks.
11. Wang K., Xue Q., Xing Y., andLi C.Improve aggressive driver recognition using collision surrogate measurement and imbalanced class boosting.
12. Zahid M., Chen Y., Khan S., Jamal A., Ijaz M., andAhmed T.Predicting risky and aggressive driving behavior among taxi drivers: do spatio-temporal attributes matter?.
13. Lee, J. and Jang, K.A framework for evaluating aggressive driving behaviors based on in-vehicle driving records.
14. Kovaceva J.,Isaksson-Hellman, I., and Murgovski, N. Identification of aggressive driving from naturalistic data in car-following situations.
15. Streiffer C., Raghavendra R., Benson T., andSrivatsa M.Darnet: a deep learning solution for distracted driving detection. In
16. Xie, J. and Zhu, M.Maneuver-based driving behavior classification based on random forest.
17. Aoude G.S., Desaraju V.R., Stephens L.H., andHow J.P.Driver behavior classification at intersections and validation on large naturalistic data set.
18. Qiao Z., Zhao J., Zhu J., Tyree Z., Mudalige P., Schneider J., andDolan J.M.Human driver behavior prediction based on urbanflow. In
19. Moukafih Y., Hafidi H., andGhogho M.Aggressive driving detection using deep learning-based time series classification. In
20. Hema, D.D. and Kumar, K.A.Hyperparameter optimization of LSTM based Driver’s aggressive behavior prediction model. In
21. Hinz T.,Navarro-Guerrero, N., Magg, S., and Wermter, S. Speeding up the hyperparameter optimization of deep convolutional neural networks.
22. Ciancio C., Ambrogio G., Gagliardi F., andMusmanno R.Heuristic techniques to optimize neural network architecture in manufacturing applications.
23. Kim, H.J. and Shin, K.S.A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets.
24. Chung, H. and Shin, K.S.Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction.
25. United States Department of Transportation, NGSIM: Next Generation Simulation, https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm, (accessed on November2021).
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