Abstract
With the growing development of autonomous navigation technologies, transportation systems have become safer and hassle-free. However, this decreasing necessity for human intervention has been both a boon and a bane for novel technologies. Utilising autonomous driving systems in highly rugged terrains has become very sophisticated and challenging. Upgradation of persisting navigation technologies in deformed terrains can improve accessibility, economy, security, traffic, and travel time. Therefore, the current preliminary research illustrates an acoustic wave and visual navigation-based rover system that can better navigate by monitoring the terra-formations using the acoustic and visual sensors to receive the reflected signal and image, respectively. The implementation tries to map the sensed world to a digital twin that the rover navigates through. The realisation of the model and the essential metrics are illustrated. The extension of the proposed technology to a grander scale in automobiles must be examined in several other environments and more focused on.
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Chellaswamy, C., Famitha, H., Anusuya, T., Amirthavarshini, S.B.: IoT based humps and pothole detection on roads and information sharing. In: 2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), pp. 084–090. IEEE (2018)
Rahman, A., Mustafa, R., Hossain, M.S.: Real-time pothole detection and localization using convolutional neural network. In: Arefin, M.S., Kaiser, M.S., Bandyopadhyay, A., Ahad, M.A.R., Ray, K. (eds.) Proceedings of the International Conference on Big Data, IoT, and Machine Learning. LNDECT, vol. 95, pp. 579–592. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-6636-0_44
Chellaswamy, C., Saravanan, M., Kanchana, E., Shalini, J., Others: Deep learning based pothole detection and reporting system. In: 2020 7th International Conference on Smart Structures and Systems (ICSSS), pp. 1–6. IEEE (2020)
Rateke, T., Justen, K.A., Von Wangenheim, A.: Road surface classification with images captured from low-cost camera-road traversing knowledge (rtk) dataset. Revista de Informática Teórica e Aplicada 26, 50–64 (2019)
Springer, M., Ament, C.: A mobile and modular low-cost sensor system for road surface recognition using a bicycle. In: 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 360–366. IEEE (2020)
Menegazzo, J., von Wangenheim, A.: Multi-contextual and multi-aspect analysis for road surface type classification through inertial sensors and deep learning. In: 2020 X Brazilian Symposium on Computing Systems Engineering (SBESC), pp. 1–8. IEEE (2020)
Blanche, J., Mitchell, D., Flynn, D.: Run-time analysis of road surface conditions using non-contact microwave sensing. In: 2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), pp. 1–6. IEEE (2020)
Mandal, V., Mussah, A.R., Adu-Gyamfi, Y.: Deep learning frameworks for pavement distress classification: a comparative analysis. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 5577–5583. IEEE (2020)
Tasmin, T., Wang, J., Dia, H., Richards, D., Tushar, Q.: A probabilistic approach to evaluate the relationship between visual and quantified pavement distress data using logistic regression. In: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), pp. 1–4. IEEE (2020)
Reddy, E.J., Reddy, P.N., Maithreyi, G., Balaji, M.B.C., Dash, S.K., Kumari, K.A.: Development and analysis of pothole detection and alert based on NodeMCU. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp. 1–5. IEEE (2020)
Madli, R., Hebbar, S., Pattar, P., Golla, V.: Automatic detection and notification of potholes and humps on roads to aid drivers. IEEE Sens. J. 15, 4313–4318 (2015)
Gorintla, S., Kumar, B.A., Chanadana, B.S., Sai, N.R., Kumar, G.S.C.: Deep-learning-based intelligent PotholeEye+ detection pavement distress detection system. In: 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 1864–1869. IEEE (2022)
Nguyen, T., Lechner, B., Wong, Y.D.: Response-based methods to measure road surface irregularity: a state-of-the-art review. Eur. Transp. Res. Rev. 11(1), 1–18 (2019). https://doi.org/10.1186/s12544-019-0380-6
Rateke, T., von Wangenheim, A.: Road surface detection and differentiation considering surface damages. Auton. Robot. 45(2), 299–312 (2021). https://doi.org/10.1007/s10514-020-09964-3
Lõuk, R., Tepljakov, A., Riid, A.: A two-stream context-aware ConvNet for pavement distress detection. In: 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), pp. 270–273. IEEE (2020)
Ogawa, N., Maeda, K., Ogawa, T., Haseyama, M.: Degradation level estimation of road structures via attention branch network with text data. In: 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 1–2. IEEE (2021)
Liang, X., Yu, X., Chen, C., Jin, Y., Huang, J.: Automatic classification of pavement distress using 3D ground-penetrating radar and deep convolutional neural network. IEEE Trans. Intell. Transp. Syst. 23(11), 22269–22277 (2022). https://doi.org/10.1109/TITS.2022.3197712
Sun, X., Xie, Y., Jiang, L., Cao, Y., Liu, B.: DMA-Net: DeepLab with multi-scale attention for pavement crack segmentation. IEEE Trans. Intell. Transp. Syst. 23(10), 18392–18403 (2022)
Liu, Z., Gu, X., Yang, H., Wang, L., Chen, Y., Wang, D.: Novel YOLOv3 model with structure and hyperparameter optimization for detection of pavement concealed cracks in GPR images. IEEE Trans. Intell. Transp. Syst. 23(11), 22258–22268 (2022)
Christodoulou, S.E., Hadjidemetriou, G.M., Kyriakou, C.: Pavement defects detection and classification using smartphone-based vibration and video signals. In: Workshop of the European Group for Intelligent Computing in Engineering, pp. 125–138 (2018)
Li, W., Burrow, M., Metje, N., Ghataora, G.: Automatic road survey by using vehicle mounted laser for road asset management. IEEE Access. 8, 94643–94653 (2020)
Zajic, G., Popovic, K., Gavrovska, A., Reljin, I., Reljin, B.: Video-based assistance for autonomous driving. In: 2020 Zooming Innovation in Consumer Technologies Conference (ZINC), pp. 151–154. IEEE (2020)
Golovnin, O.K., Rybnikov, D.V.: Video processing method for high-definition maps generation. In: 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), pp. 1–5. IEEE (2020)
Jingxiang, Z., Jinxi, Z., Dandan, C.: Preliminary study on pavement performance characteristics based on intelligent method. In: 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 1484–1490. IEEE (2020)
Lembach, J., Stricker, R., Gross, H.-M.: Efficient implementation of regional mutual information for the registration of road images. In: 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6. IEEE (2020)
Auriacombe, O., Vassilev, V., Pinel, N.: Dual-polarised radiometer for road surface characterisation. J. Infrared Millimeter Terahertz Waves 43(1–2), 108–124 (2022). https://doi.org/10.1007/s10762-022-00847-5
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Pothapragada, P., Neelamraju, P.M. (2023). Acoustic and Visual Sensor Fusion-Based Rover System for Safe Navigation in Deformed Terrain. In: Sharma, H., Saha, A.K., Prasad, M. (eds) Proceedings of International Conference on Intelligent Vision and Computing (ICIVC 2022). ICIVC 2022. Proceedings in Adaptation, Learning and Optimization, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-031-31164-2_29
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