Abstract
The emergence of autonomous cars in today’s world makes it imperative to develop superlative steering algorithms. Deep convolutional neural networks are widely adopted in vision problems for their adept nature to classify images. End-to-end models have acted as an excellent substitute for handcrafted feature extraction. This chapter’s proposed system, which comprises of steering angle prediction, road detection, road centering, and object detection, is a facilitated version of an autonomous steering system over just considering a single-blind end-to-end architecture. The benefits of proposing such an algorithm for the makeover of existing cars include reduced costs, increased safety, and increased mobility.
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Muthazhagan, B., Sundaramoorthy, S. (2021). Enhanced End-to-End System for Autonomous Driving Using Deep Convolutional Networks. In: Ahmed, K.R., Hassanien, A.E. (eds) Deep Learning and Big Data for Intelligent Transportation. Studies in Computational Intelligence, vol 945. Springer, Cham. https://doi.org/10.1007/978-3-030-65661-4_4
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