ACTIVE COLLISION AVOIDANCE CONTROL BASED ON CONVOLUTIONAL NEURAL NETWORK FOR BLIND ZONE PERCEPTION OF AUTOMOTIVE SENSORS

Nenghui Jiang and Cheng Li

References

  1. [1] F.J. Nezhad, M. Taghizadeh-Yazdi, J.H. Dahooie, A.Z.Babgohan, and S.M. Sajadi, Designing a new mathematicalmodel for optimising a multi-product RFID-based closed-loopfood supply chain with a green entrepreneurial orientation,British Food Journal, 124(7), 2022, 2114–2148.
  2. [2] M. Arsalan and F. Akbar, An ultrasonic sensor basedautomatic braking system to mitigate driving exhaustion duringtraffic congestion, Proceedings of the Institution of MechanicalEngineers, Part D: Journal of Automobile Engineering,235(12), 2021, 3026–3035.
  3. [3] I. Han, Car-mounted (black box) camera–based predictionand avoidance of intersection collisions for advanced driver8assistance systems, Proceedings of the Institution of MechanicalEngineers Part D Journal of Automobile Engineering, 235(1),2020, 231–244.
  4. [4] C. Yuan, S. Weng, J. Shen, L. Chen, Y. He, and T. Wang,Research on active collision avoidance algorithm for intelligentvehicle based on improved artificial potential field model,International Journal of Advanced Robotic Systems, 17(3),2020, 1–15.
  5. [5] H. Shoeb and A.B. Mohammad, Neural predictive observerfor sensorless-controlled induction motor drive, MechatronicSystems and Control, 45(2), 2017, 2798.
  6. [6] V.T. Minh, R. Reza Moezzi, J. Cyrus, and H. Jaroslav,Feasible and optimal trajectories generation for autonomousdriving vehicles, Mechatronic Systems and Control, 51(1), 2023,11–24.
  7. [7] H. Wang, Y. Yu, Y. Cai, X. Chen, L. Chen, and Q. Liu, Acomparative study of state-of-the-art deep learning algorithmsfor vehicle detection, IEEE Intelligent Transportation SystemsMagazine, 11(2), 2019, 82–95.
  8. [8] Q. Zheng, B. Hu, T. Fan, C. Xu, and X. Li, Impact of RFIDtechnology on coordination of a three-tier fresh product supplychain, Asia-Pacific Journal of Operational Research, 39(1),2022, 214–235.
  9. [9] K. Zhang, W. Liu, J. Li, and L. Zhang, Stratified controlstrategy of vehicle longitudinal active collision avoidance,Journal of Physics: Conference Series, 1735(1), 2021,12003–12016.
  10. [10] Y. Yang and X. Song, Research on face intelligent perceptiontechnology integrating deep learning under different illumi-nation intensities, Journal of Computational and CognitiveEngineering, 1(1), 2022, 32–36.
  11. [11] C. Luo, I. Gil, and R. Fernandez-Garcia, Electro-textile UHF-RFID compression sensor for health-caring applications, IEEESensors Journal, 22(12), 2022, 12332–12338.
  12. [12] S. Chen, Y. Leng, and S. Labi, A deep learning algorithm forsimulating autonomous driving considering prior knowledge andtemporal information, Computer-Aided Civil and InfrastructureEngineering, 35(4), 2020, 305–321.
  13. [13] Q.Z. Wang and X.X. Wang, A fault detection diagnosis predictobserver based on resource allocation network, MechatronicSystems and Control, 50(2), 2022, 96–101.
  14. [14] W. Xue and L. Zheng, Active collision avoidance system designbased on model predictive control with varying sampling time,Automotive Innovation, 3(1), 2020, 62–72.
  15. [15] E.P. Neff, An RFID-based tracking system gives mice theirsay, Lab Animal, 50(10), 2021, 282.
  16. [16] V.S. Kaustubh, T. Tanuja, and V. Vibha, Review of vehicledetection systems in advanced driver assistant systems,Archives of Computational Methods in Engineering: State ofthe Art Reviews, 27(1), 2020, 26–28.
  17. [17] K.N. Bharath, P. Madhu, S.M. Rangappa, S. Basavarajappa, S.Suchart, A. Karfidov, and G. Sergey, Waste coconut leaf sheathas reinforcement composite material with phenol-formaldehydematrix, Polymer Composites, 43(4), 2022, 1985–1995.
  18. [18] W. Akram, K. Mahmood, X. Li, M. Sadiq, Z. Lv, andS. Chaudhry, An energy-efficient and secure identity basedRFID authentication scheme for vehicular cloud computing,Computer Networks, 217(9), 2022, 1–11.
  19. [19] Y. Zhang and X. Song, Research on visual vehicle detectionand tracking based on deep learning, IOP Conference Series:Materials Science and Engineering, 892(1), 2020, 38–46.
  20. [20] A. Shimi, M.R. Ebrahimi Dishabi, and M. Abdollahi Azgomi,An intelligent parking management system using RFIDtechnology based on user preferences, Soft Computing, 26(24),2022, 13869–13884.
  21. [21] M. Wang, Q. Li, Y. Gu, L. Fang, and X. Zhu, SCAF-Net: Scenecontext attention-based fusion network for vehicle detection inaerial imagery, IEEE Geoscience and Remote Sensing Letters,19(3), 2022, 1–5.
  22. [22] Z. Han, C. Wang, and Q. Fu, M 2 R-Net: Deep networkfor arbitrary oriented vehicle detection in MiniSAR images,Engineering Computations, 38(7), 2021, 2969–2995.
  23. [23] R. Zhang, K. Li, Y. Wu, D. Zhao, Z. Lv, F. Li, X.Chen, Zhijun Qiu, and Fan Yu, A multi-vehicle longitudinaltrajectory collision avoidance strategy using AEBS withvehicle-infrastructure communication, IEEE Transactions onVehicular Technology, 71(2), 2022, 1253–1266.

Important Links:

Go Back