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Deep learning using computer vision in self driving cars for lane and traffic sign detection

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Abstract

Recently, the amount of research in the field of self-driving cars has grown significantly with autonomous vehicles having clocked in more than 10 million miles, providing a substantial amount of data for use in training and testing. The most complex part of training is the use of computer vision for feature extraction and object detection in real-time. Much relevant research has been done on improving the algorithms in the area of image segmentation. The proposed idea presents the use of Convoluted Neural Networks using Spatial Transformer Networks and lane detection in real time to increase the efficiency of autonomous vehicles. The depth of the neural network will help in training vehicles and during the testing phase, the vehicles will learn to make decisions based on the training data. In case of sudden changes to the environment, the vehicle will be able to make decisions quickly to prevent damage or danger to lives. Along with lane detection, a self-driving car must also be able to detect traffic signs. The proposed approach uses the Adam Optimizer which runs on top of the LeNet-5 architecture. The LeNet-5 architecture is analyzed and compared with the Feed Forward Neural Network approach. The accuracy of the LeNet-5 architecture was found to be 97% while the accuracy of the Feed Forward Neural Network was 94%.

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Acknowledgements

The authors would like to thank the researchers at the German Ruht-Universitaet Bochum in Germany for making available the very useful and comprehensive German Traffic Sign Dataset (http://benchmark.ini.rub.de/) that was used in this research (Stallkamp et al. 2011). This research was completed as a master's graduate research project at the Department of Electrical Engineering and Computer Science at Texas A&M University - Kingsville.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX, USA

    Nitin Kanagaraj, David Hicks & Ayush Goyal

  2. Universidad Autonoma de Tamaulipas, Ciudad Victoria, Mexico

    Sanju Tiwari

  3. National Institute of Technology Uttarakhand, Srinagar Garhwal, India

    Ghanapriya Singh

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  1. Nitin Kanagaraj
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  2. David Hicks
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  3. Ayush Goyal
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  4. Sanju Tiwari
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  5. Ghanapriya Singh
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Correspondence to Sanju Tiwari.

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Kanagaraj, N., Hicks, D., Goyal, A. et al. Deep learning using computer vision in self driving cars for lane and traffic sign detection. Int J Syst Assur Eng Manag 12, 1011–1025 (2021). https://doi.org/10.1007/s13198-021-01127-6

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  • DOI: https://doi.org/10.1007/s13198-021-01127-6

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