V2X, GNSS, radar, and camera-based intelligent system for adaptive control of heavy mining vehicles during foggy weather

References

• Abboud, K., H. A. Omar, and W. Zhuang. 2016. Interworking of DSRC and cellular network technologies for V2X communications: A survey. IEEE Transactions on Vehicular Technology 65 (12):9457–70. doi: 10.1109/TVT.2016.2591558.
   Web of Science ®Google Scholar
• Abuhaiba, I. S. I. 2003. Skew correction of textural documents. Journal of King Saud University - Computer and Information Sciences 15:73–93. doi: 10.1016/S1319-1578(03)80003-4.
   Google Scholar
• Adidela, S., S. Singh, T. Sahu, and A. Mishra. 2021. Single image and video Dehazing: A dark channel prior (DCP)-based approach. In Proceedings of IEEE 18th India council international conference (INDICON), pp. 1–6. doi: 10.1109/INDICON52576.2021.9691546.
   Google Scholar
• Chatterjee, D., and S. K. Chaulya. 2019. Vision improvement system using image processing technique for adverse weather condition of opencast mines. International Journal of Mining, Reclamation, and Environment 33 (7):505–16. doi: 10.1080/17480930.2018.1496886.
   Web of Science ®Google Scholar
• Colosimo, B. M., Q. Huang, T. Dasgupta, and F. Tsung. 2018. Opportunities and challenges of quality engineering for additive manufacturing. Journal of Quality Technology 50 (3):233–52. doi: 10.1080/00224065.2018.1487726.
   Web of Science ®Google Scholar
• Dannheim, C., C. Icking, M. Mader, and P. Sallis. 2014. Weather detection in vehicles by means of camera and lidar systems. In Proceedings of Sixth International Conference on Computational Intelligence, Communication Systems and Networks, pp. 186–191. doi: 10.1109/CICSyN.2014.47.
   Google Scholar
• Diewald, S., A. Moller, L. Roalter, and K. Matthias. 2012. Driveassist - A V2X-based driver assistance system for android. In Proceedings of Mensch & Computer Workshop, 2012, 373–80. Accessed December 30, 2020. https://www.researchgate.net/publication/235642532_DriveAssist_-_A_V2X-Based_Driver_ Assistance_System_for_Android.
   Google Scholar
• Galvez, R. L., A. A. Bandala, E. P. Dadios, R. R. P. Vicerra, and J. M. Z. Maningo. 2018. Object detection using convolutional neural networks. In Proceedings of TENCON-IEEE Region 10 Conference, pp. 2023–2027. doi: 10.1109/TENCON.2018.8650517.
   Google Scholar
• Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for multi-class classification: An overview. Machine Learning. 10.48550/arXiv.2008.05756
   Google Scholar
• Guo, F., J. Tang, and X. Xiao. 2014. Foggy scene rendering based on transmission map estimation. International Journal of Computer Games Technology 2014:1–13. doi: 10.1155/2014/308629.
   Google Scholar
• Hautiere, N., J. P. Tarel, and D. Aubert. 2010. Mitigation of visibility loss for advanced camera-based driver assistance. IEEE Transactions on Intelligent Transportation Systems 11 (2):474–84. doi: 10.1109/TITS.2010.2046165.
   Web of Science ®Google Scholar
• He, K., J. Sun, and X. Tang. 2011. Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (12):2341–53. doi: 10.1109/TPAMI.2010.168.
   PubMed Web of Science ®Google Scholar
• Hitam, M. S., E. A. Awalludin, W. N. J. H. W. Y. Yussof, and Z. Bachok. 2013. Mixture contrast limited adaptive histogram equalisation for underwater image enhancement. In Proceedings of International Conference on Computer Applications Technology, pp. 1–5. doi: 10.1109/ICCAT.2013.6522017.
   Google Scholar
• Howard, A. G., M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. Computer Vision and Pattern Recognition. doi: 10.48550/arXiv.1704.04861.
   Google Scholar
• Indian Standard (IS 16479). 2020. Performance requirements and test procedures of braking systems for wheeled or high-speed rubber tracked earth moving machines and construction equipment vehicles (first revision), Bureau of Indian Standard, New Delhi, India.
   Google Scholar
• Javaid, A., M. A., Siddique, A. A., Reshi, F., Rustam, E., Lee, V., Rupapara, Mui-zzud-din. (2022). Coal mining accident causes classification using voting-based hybrid classifier. Journal of Ambient Intelligence and Humanized Computing, 14 (10): 13211. doi: 10.1007/s12652-022-03779-z.
   Google Scholar
• Jiménez, F., J. E. Naranjo, J. J. Anaya, F. García, A. Ponz, and J. M. Armingol. 2016. Advanced driver assistance system for road environments to improve safety and efficiency. Transportation Research Procedia 14:2245–54. doi: 10.1016/j.trpro.2016.05.240.
   Google Scholar
• Laureano, G. T., M. S. V. de Paiva, A. D. S. Soares, and C. J. Coelho. 2015. A topological approach for detection of chessboard patterns for camera calibration. In: Deligiannidis, L. & Arabnia, H. R. (Eds.), Emerging trends in image processing, computer vision and pattern recognition. Amsterdam: Elsevier, 517–31. doi: 10.1016/b978-0-12-802045-6.00034-x.
   Google Scholar
• Lee, S. P., A. K. Chao, F. Tsung, D. S. H. Wong, S. T. Tseng, and S. S. Jang. 2011. Monitoring batch processes with multiple on–off steps in semiconductor manufacturing. Journal of Quality Technology 43 (2):142–57. doi: 10.1080/00224065.2011.11917852.
   Web of Science ®Google Scholar
• Lee, S., S. Yun, and J. H. Nam. 2016. A review on dark channel prior based image dehazing algorithms. EURASIP Journal of Image and Video Proccessing 4: 1–23. doi: 10.1186/s13640-016-0104-y.
   Google Scholar
• Liu, Q., P. Liang, J. Xia, T. Wang, M. Song, X. Xu, J. Zhang, Y. Fan, and L. Liu. 2021. A highly accurate positioning solution for C-V2X systems. Sensors (Basel, Switzerland)21 (4):1175. doi: 10.3390/s21041175.
   PubMed Web of Science ®Google Scholar
• Liu, W., D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg. 2016. SSD: Single shot multibox detector. In Proceedings of European Conference on Computer Vision, pp. 21–37. doi: 10.48550/arXiv.1512.02325.
   Google Scholar
• MacIntyre, B., and W. B. Cowan. 1992. A practical approach to calculating luminance contrast on a CRT. ACM Transactions on Graphics 11 (4):336–47. doi: 10.1145/146443.146467.
   Web of Science ®Google Scholar
• Najjar, Y. A. Y. A., and D. C. Soong. 2012. Comparison of image quality assessment: PSNR, HVS, SSIM, UIQI. International Journal of Scientific & Engineering Research 3 (8):1–5. Accessed December 30, 2020. https://www.ijser.org/researchpaper/Comparison-of-Image-Quality-Assessment-PSNR-HVS-SSIM-UIQI.pdf.
   Google Scholar
• Negru, M., and S. Nedevschi. 2013. Image based fog detection and visibility estimation for driving assistance systems. In Proceedings of IEEE 9th International Conference on Intelligent Computer Communication and Processing, pp. 163–168. doi: 10.1109/ICCP.2013.6646102.
   Google Scholar
• Nilsson, J., and T. A. Moller. 2020. Understanding SSIM. Accessed December 30, 2020. https://www.researchgate.net/publication/342435717_Understanding_SSIM.
   Google Scholar
• Ramya, C., and S. S. Rani. 2012. A novel method for the contrast enhancement of fog degraded video sequences. International Journal of Computer Applications 54 (13):1–5. doi: 10.5120/8623-2489.
   Google Scholar
• Richards, A., and T. Hoelter. 2020. Can thermal imaging see through fog and rain? Teledyne FLIR. Accessed December 30, 2020. https://www.flir.in/discover/rd-science/can-thermal-imaging-see-through-fog-and-rain/.
   Google Scholar
• Sheikh, H. R., and A. C. Bovik. 2006. Image information and visual quality. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society 15 (2):430–44. doi: 10.1109/TIP.2005.859378.
   PubMed Web of Science ®Google Scholar
• Spinneker, R., C. Koch, S. B. Park, and J. J. Yoon. 2014. Fast fog detection for camera based advanced driver assistance systems. In Proceedings of 17th International IEEE Conference on Intelligent Transportation Systems, pp. 1369–1374. doi: 10.1109/ITSC.2014.6957878.
   Google Scholar
• Stevens, N. T., R. Browne, S. H. Steiner, and R. J. MacKay. 2010. Augmented measurement system assessment. Journal of Quality Technology 42 (4):388–99. doi: 10.1080/00224065.2010.11917835.
   Web of Science ®Google Scholar
• Sun, E., A. Nieto, Z. Li, and V. Kecojevic. 2010. An integrated information technology assisted driving system to improve mine trucks-related safety. Safety Science 48 (10):1490–7. doi: 10.1016/j.ssci.2010.07.012.
   Web of Science ®Google Scholar
• Sun, E., and X. Zhang. 2011. 3D assisted driving system for haul trucks in surface mining. In Proceedings of 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), pp. 363–366. doi: 10.1109/TMEE.2011.6199218.
   Google Scholar
• Sun, W., R. A. Kontar, J. Jin, and T. S. Chang. 2023. A continual learning framework for adaptive defect classification and inspection. Journal of Quality Technology 55 (5):598–614. doi: 10.1080/00224065.2023.2224974.
   Web of Science ®Google Scholar
• Vivek, N., S. V. Srikanth, P. Saurabh, T. P. Vamsi, and K. Raju. 2014. On field performance analysis of IEEE 802.11 P and WAVE protocol stack for V2V & V2I communication. In Proceedings of IEEE International Conference on Information Communication Embedded Systems, pp. 1–6. doi: 10.1109/ICICES.2014.7033960.
   Google Scholar
• Wang, J., Y. Shao, Y. Ge, and R. Yu. 2019. A survey of vehicle to everything (V2X) testing. Sensors (Basel, Switzerland) 19 (2):334–6. doi: 10.3390/s19020334.
   PubMed Web of Science ®Google Scholar
• Wang, Q., K. Paynabar, and M. Pacella. 2022. Online automatic anomaly detection for photovoltaic systems using thermography imaging and low rank matrix decomposition. Journal of Quality Technology 54 (5):503–16. doi: 10.1080/00224065.2021.1948372.
   Web of Science ®Google Scholar
• Wang, Z., & A. C. Bovik. (2002). A universal image quality index. IEEE Signal Processing Letters, 9 (3):81–84. doi: 10.1109/97.995823.
   Web of Science ®Google Scholar
• Wang, Z., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society 13 (4):600–12. doi: 10.1109/TIP.2003.819861.
   PubMed Web of Science ®Google Scholar
• Wayahdi, M. R., D. Syahputra, and S. H. N. Ginting. 2020. Evaluation of the K-nearest neighbor model with K-Fold cross validation on image classification. Infokum 9 (1):1–6. http://infor.seaninstitute.org/index.php/infokum/article/view/72.
   Google Scholar
• Xu, H., J. Guo, Q. Liu, and L. Ye. 2012. Fast image dehazing using improved dark channel prior. In Proceedings of IEEE International Conference on Information Science and Technology, pp. 663–667. doi: 10.1109/ICIST.2012.6221729.
   Google Scholar
• Younis, A., L. Shixin, S. Jn, and Z. Hai. 2020. Real-time object detection using pre-trained deep learning models MobileNet-SSD. In Proceedings of 6th International Conference on Computing and Data Engineering, Sanya, China, pp. 44–48. doi: 10.1145/3379247.3379264.
   Google Scholar
• Zeletin, R. P., I. Radusch, and M. A. Rigani. 2010. Vehicular-2-X communication: State-of-the-art and research in mobile vehicular ad hoc networks. Heidelberg: Springer.
   Google Scholar