Abstract
The automobile industry has been finding ways to make driving safer and more convenient. We have reached a point where we need to automate the process of driving and make it more affordable. The present implementations are very robotic and cannot run among human-driven vehicles, and this made us realize the need of having a more naturalistic driving style for the autonomous vehicles. This research paper focuses on making a level 3 autonomous vehicle with the help of computer vision and deep learning. The main objective is to achieve the most ideal results in lane detection and object detection which can be worked upon to improve results. The reason that this project is of such great value is the fact that 80%of road accidents happen due to human error and negligence and we believe that providing a driver aid of some sorts could be really helpful in such scenarios. With the help of this paper we will be able to obtain high values with great accuracy with the help of which this can be implemented in real-time vehicles by just making a few alterations and by simple calibrations. We believe this will be a step toward a complete autonomous future.
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References
M. Bertozzi, A. Broggi, Gold: Aparallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans. Image Process. 7(1), 62–81 (1998)
Y. Hou, Z. Ma, C. Liu, C.C. Loy, Learning lightweight lane detection CNNs by self attention distillation, in International Conference on Computer Vision (ICCV), At COEX Convention Center, Seoul, Korea (2019)
L.-C. Chen, Y. Yang, J. Wang, W. Xu, A.L. Yuille, Attentionto scale: scale-aware semantic image segmentation, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 3640–3649 (2016)
R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in Computer Vision and Pattern Recognition (2014)
N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in Computer Vision and Pattern Recognition (2005)
E. Rublee, V. Rabaud, K. Konolige, G. Bradski, ORB: an efficient alternative to SIFT or SURF, in International Conference on Computer Vision, Barcelona (2011)
Convolutional Neural Networks for Visual Recognition, [Online]. Available: https://cs231n.github.io/convolutional-networks/
Redmond, YOLO: Real-Time Object Detection, [Online]. Available: https://pjreddie.com/darknet/yolo/
C.-C. Tseng, H.-Y. Cheng, B.-S. Jeng, A lane detection algorithm using geometry information and modified Hough transform. 2005IPPR
R.N. Mallick, Implementation of Canny edge detection algorithm. PhD diss. (2012)
“BilateralFilter”, [onlineavailable]: wikipedia.org
Introducing Raspberry Pi Model B+. Raspberry Pi Foundation. Retrieved 14 July2014
Diagram of Raspberry Pi with CSI camera connector”. Elinux.org. 2 March 2012. Retrieved 22 June 2012
Introduction to Arduino (PDF). priceton.edu. Archived from the original (PDF) on 3 April 2018. Retrieved 4 Feb 2018
A. Dosovitski, G. Ros, F. Codevilla, A. Lopez, V. Koltun, CARLA: an open urban driving simulator, in 1st Conference on Robot Learning (CoRL) (2017)
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Subhashini, J., Khanna, G., Arya, R., Kompalli, L.G.K., Gupta, R. (2022). Naturalistic Driving Simulation Using Automation. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol 1340. Springer, Singapore. https://doi.org/10.1007/978-981-16-1249-7_40
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DOI: https://doi.org/10.1007/978-981-16-1249-7_40
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