Abstract
A crucial requirement for intelligent, driverless cars is to maneuver without moving out of its drivable region of the road. It is well known that steering angle calculation plays an important role in maintaining the vehicle in the center of the road or within the boundary lanes to meet safety critical requirements. This paper presents a review of autonomous steering techniques for self driving cars which is a relatively unexplored task in the fields of computer vision, robotics and machine learning. Our principle aim is to find out the state-of-the-art models in traditional computer vision approach and end-to-end Deep learning approach. Subsequently we have analyzed and compared the performance of each model based on the reported experimental results. Our research investigations lead us to conclude that ResNet50 Deep network combined with event cameras can be assumed to give better prediction of the due wheel angle in comparison to the use of traditional cameras. An overview of future research direction and applications is also given.
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Oussama, A., Mohamed, T. (2020). A Literature Review of Steering Angle Prediction Algorithms for Self-driving Cars. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1105. Springer, Cham. https://doi.org/10.1007/978-3-030-36674-2_4
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DOI: https://doi.org/10.1007/978-3-030-36674-2_4
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