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Research Article

Abnormal driving behavior detection based on an improved ant colony algorithm

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Article: 2216060 | Received 26 Mar 2023, Accepted 16 May 2023, Published online: 04 Jun 2023

References

  • Abusitta, A., G. H. de Carvalho, O. A. Wahab, T. Halabi, B. C. Fung, and S. Al Mamoori. 2023. Deep learning-enabled anomaly detection for IoT systems. Internet of Things 21:100656. doi:10.1016/j.iot.2022.100656.
  • Chen, L.-W., and H.-M. Chen. 2020. Driver behavior monitoring and warning with dangerous driving detection based on the internet of vehicles. IEEE Transactions on Intelligent Transportation Systems 22 (11):7232–1806. doi:10.1109/TITS.2020.3004655.
  • Chen, L.-W., and H.-M. Chen. 2021. Driver behavior monitoring and warning with dangerous driving detection based on the internet of vehicles. IEEE Transactions on Intelligent Transportation Systems 22 (11):7232–41. doi:10.1109/tits.2020.3004655.
  • Habibifar, N., and H. Salmanzadeh. 2022. Relationship between driving styles and biological behavior of drivers in negative emotional state. Transportation Research, Part F, Traffic Psychology and Behaviour 85:245–58. doi:10.1016/j.trf.2022.01.010.
  • Hu, J., X. Zhang, and S. Maybank. 2020. Abnormal driving detection with normalized driving behavior data: A deep learning approach. IEEE Transactions on Vehicular Technology 69 (7):6943–51. doi:10.1109/TVT.2020.2993247.
  • Kumar, P. P., K. Kant, and A. Pal (2022). C-FAR: A Compositional Framework for Anomaly Resolution in Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109/TITS.2022.3196548.
  • Li, G., W. Lai, X. Sui, X. Li, X. Qu, T. Zhang, and Y. Li. 2020. Influence of traffic congestion on driver behavior in post-congestion driving. Accident Analysis & Prevention 141:105508. doi:10.1016/j.aap.2020.105508.
  • Liu, L., Z. Wang, and S. Qiu. 2020. Driving behavior tracking and recognition based on multisensors data fusion. IEEE Sensors Journal 20 (18):10811–23. doi:10.1109/JSEN.2020.2995401.
  • Ma, Y., Z. Xie, S. Chen, F. Qiao, and Z. Li. 2023. Real-time detection of abnormal driving behavior based on long short-term memory network and regression residuals. Transportation Research Part C: Emerging Technologies 146:103983. doi:10.1016/j.trc.2022.103983.
  • Meneghetti, L., M. Terzi, S. Del Favero, G. A. Susto, and C. Cobelli. 2018. Data-driven anomaly recognition for unsupervised model-free fault detection in artificial pancreas. IEEE Transactions on Control Systems Technology 28 (1):33–47. doi:10.1109/TCST.2018.2885963.
  • Nguyen, T.-H., D.-N. Lu, D.-N. Nguyen, and H.-N. Nguyen. 2020. Dynamic basic activity sequence matching method in abnormal driving pattern detection using smartphone sensors. Electronics 9 (2):217. doi:10.3390/electronics9020217.
  • Odiathevar, M., W. K. Seah, and M. Frean. 2022. A Bayesian approach to distributed anomaly detection in edge AI networks. IEEE Transactions on Parallel and Distributed Systems 33 (12):3306–20. doi:10.1109/TPDS.2022.3151853.
  • Peng, Y., Q. Xu, S. Lin, X. Wang, G. Xiang, S. Huang, and H. Zhang, C. Fan. 2022. The application of electroencephalogram in driving safety: Current status and future prospects. Frontiers in Psychology 13:13. doi:10.3389/fpsyg.2022.919695.
  • Ren, M. L., X. D. Huang, X. X. Zhu, and L. J. Shao. 2020. Optimized PSO algorithm based on the simplicial algorithm of fixed point theory. Applied Intelligence 50 (7):2009–24. doi:10.1007/s10489-020-01630-6.
  • Ryan, C., F. Murphy, and M. Mullins. 2020. End-to-end autonomous driving risk analysis: A behavioural anomaly detection approach. IEEE Transactions on Intelligent Transportation Systems 22 (3):1650–62. doi:10.1109/TITS.2020.2975043.
  • Ryan, C., F. Murphy, and M. Mullins. 2021. End-to-end autonomous driving risk analysis: A behavioural anomaly detection approach. IEEE Transactions on Intelligent Transportation Systems 22 (3):1650–62. doi:10.1109/tits.2020.2975043.
  • Sethuraman, R., S. Sellappan, J. Shunmugiah, N. Subbiah, V. Govindarajan, and S. Neelagandan. 2023. An optimized AdaBoost Multi-class support vector machine for driver behavior monitoring in the advanced driver assistance systems. Expert Systems with Applications 212:118618. doi:10.1016/j.eswa.2022.118618.
  • Singh, H., and A. Kathuria. 2021. Analyzing driver behavior under naturalistic driving conditions: A review. Accident Analysis & Prevention 150:105908.
  • Vlakveld, W., M. Doumen, and S. van der Kint. 2021. Driving and gaze behavior while texting when the smartphone is placed in a mount: A simulator study. Transportation Research, Part F, Traffic Psychology and Behaviour 76:26–37. doi:10.1016/j.trf.2020.10.014.
  • Xiang, H., J. Zhu, G. Liang, and Y. Shen. 2021. Prediction of dangerous driving behavior based on vehicle motion state and passenger feeling using cloud model and Elman neural network. Frontiers in Neurorobotics 15:641007.
  • Xu, W., J. Wang, T. Fu, H. Gong, and A. Sobhani. 2022. Aggressive driving behavior prediction considering driver’s intention based on multivariate-temporal feature data. Accident Analysis & Prevention 164:106477. doi:10.1016/j.aap.2021.106477.
  • Yang, Y., Y. Chen, C. Wu, S. M. Easa, W. Lin, and X. Zheng. 2020. Effect of highway directional signs on driver mental workload and behavior using eye movement and brain wave. Accident Analysis & Prevention 146:105705. doi:10.1016/j.aap.2020.105705.
  • Yao, Y., X. Wang, M. Xu, Z. Pu, Y. Wang, E. Atkins, and D. J. Crandall. 2022. DoTA: Unsupervised detection of traffic anomaly in driving videos. IEEE Transactions on Pattern Analysis & Machine Intelligence 45 (1):444–59. doi:10.1109/TPAMI.2022.3150763.
  • Yi, N., J. Xu, L. Yan, and L. Huang. 2020. Task optimization and scheduling of distributed cyber–physical system based on improved ant colony algorithm. Future Generation Computer Systems 109:134–48. doi:10.1016/j.future.2020.03.051.
  • Yu, W., and Q. Huang. 2022. A deep encoder-decoder network for anomaly detection in driving trajectory behavior under spatio-temporal context. International Journal of Applied Earth Observation and Geoinformation 115:103115. doi:10.1016/j.jag.2022.103115.