Abstract
Autonomous Driving refers to self-driving vehicles without the need of intervention from the human driver. Safety enhancement, energy optimization, comfort, maintenance and cost are the key benefits of Autonomous Driving. Other benefits include - productivity, reduced congestion/traffic, prevention of car crashes, reducing carbon footprint and ease of parking in congested cities as driverless vehicles could drop passengers off and move on. Autonomous Driving can be achieved via processing visual images/videos at runtime and then converting them to vehicle control signals. This study will help in detecting objects on roads (such as moving vehicles, pedestrians, other static objects on road and road segmentation) using deep convolutional neural network (CNN), which in turn help in aiding driverless future. Semantic segmentation can help recognize objects and their location. Semantic segmentation refers to labelling images with pixel-by-pixel classification that in turn helps to perceive the surrounding environment. Human driver also perceives the driving environment in a similar manner. The purpose of this study is to do an overview and check the feasibility of semantic segmentation using deep learning algorithms in the field of Autonomous Driving specifically in road segmentation task. This study will also be comparative study on CNNs models in terms of accuracy, precision, mean IOU and processing time. The scope of this study will include examination and comparison of two popular algorithms - Fully Convolutional Network (FCNs) and Semantic Segmentation model (SegNet). In detail, this study will mainly focus on road segmentation task in Autonomous Driving with the help of CNN models and will conclude which model is best suited for road segmentation task under different weather conditions, in terms of precision, accuracy, mean IoU and processing time.
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References
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 3912, 2481–2495 (2017). CDC (2022) Facts about suicide, CDC.gov. https://www.cdc.gov/suicide/facts/index.html. Accessed 14 Oct 2022
Zhang, Z., Ohya, J.: Movement control with vehicle-to-vehicle communication by using end-to-end deep learning for autonomous driving. In: ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods, ICPRAM, pp. 377–385 (2021)
Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Trans. Image Process. 1310, 1327–1344 (2004). http://ieeexplore.ieee.org/document/1331445/
Pham, T., Lee, Y.-S., Mathulaprangsan, S., Wang, J.-C.: Source separation using dictionary learning and deep recurrent neural network with locality preserving constraint. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 151–156. IEEE (2017). http://ieeexplore.ieee.org/document/8019516/
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (2017). https://github.com/hszhao/PSPNet
Zablocki, É., Ben-Younes, H., Pérez, P., Cord, M.: Explainability of vision-based autonomous driving systems: review and challenges (2021). http://arxiv.org/abs/2101.05307
Caltagirone, L., Scheidegger, S., Svensson, L., Wahde, M.: Fast LIDAR-based road detection using fully convolutional neural networks. In: IEEE Intelligent Vehicles Symposium, Proceedings (2017)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 404, 834–848 (2018)
Zhang, Y., Gao, J., Zhou, H.: Breeds classification with deep convolutional neural network. PervasiveHealth: Pervasive Comput. Technol. Healthc. 145–151 (2020)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07–12 June 2015, pp. 1–9 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1–14 (2015)
Junaid, M., et al.: Multi-feature view-based shallow convolutional neural network for road segmentation. IEEE Access 8, 36612–36623 (2020). https://ieeexplore.ieee.org/document/8988193/
Wang, D., Wen, J., Wang, Y., Huang, X., Pei, F.: End-to-end self-driving using deep neural networks with multi-auxiliary tasks. Autom. Innov. 2(2), 127–136 (2019). https://doi.org/10.1007/s42154-019-00057-1
Zohourian, F., Antic, B., Siegemund, J., Meuter, M., Pauli, J.: Superpixel-based road segmentation for real-time systems using CNN. In: VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 5 September 2018, pp. 257–265 (2018)
Fernandes, D., et al.: Point-cloud based 3D object detection and classification methods for self-driving applications: a survey and taxonomy. Inf. Fusion 68, 161–191 (2021). https://linkinghub.elsevier.com/retrieve/pii/S1566253520304097. Accessed 23 Oct 2021
Fang, R., Cai, C.: Computer vision based obstacle detection and target tracking for autonomous vehicles. In: MATEC Web of Conferences, vol. 336, p. 07004 (2021). https://www.matec-conferences.org/10.1051/matecconf/202133607004
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, pp. 3213–3223 (2016)
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Jain, A. et al. (2023). Autonomous Driving Through Road Segmentation Based on Computer Vision Techniques. In: Wah, Y.B., Berry, M.W., Mohamed, A., Al-Jumeily, D. (eds) Data Science and Emerging Technologies. DaSET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-99-0741-0_9
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