Abstract
Steering angle prediction is critical in the control of Autonomous Vehicles (AVs) and has attracted the attention of researchers, manufacturers, and insurance companies in the automotive industry. Different Deep Learning (DL) architectures have been applied to predict the steering angle of AVs in various scenarios. A survey on steering angle prediction based on deep learning algorithms can help expert researchers identify those areas that require development. Also, novice researchers can use the survey as a starting point. In this article, we present a broad study on the recent advances made in DL architectures that covers the steering angle prediction of AVs. A new comprehensive taxonomy of the application of DL in steering angle prediction of AVs is created. The survey presents a concise research summary synthesis, and analysis. It is found that most researchers depend on Convolutional Neural Network (CNN) over other DL architectures in predicting the steering angle of autonomous driving vehicles. Also identified are open research problems. The prominent challenge facing DL-based steering angle prediction of AVs is lack of sufficient real-world datasets, which means that researchers largely depend on data generated from simulated environments. Lastly, alternative viewpoints to solve the identified open research challenges are proposed, pointing towards promising future research directions.
Highlights
Human errors account for more than 90% of car accidents
CONVOLUTIONAL NEURAL NETWORK IN AUTONOMOUS VEHICLE STEERING ANGLE PREDICTION we describe the applications of Convolutional Neural Network (CNN) in the steering control of Autonomous Vehicles (AVs) to demonstrate the importance of CNN in AVs
OPTIMIZER we present the solvers used for optimizing Deep Learning (DL) architectures during the training of different projects for steering angle prediction of AVs
Summary
Human errors account for more than 90% of car accidents. In comparison, mechanical failures are responsible for only 2% [1]. This study will benefit novice researchers and developers who are interested in this research area and can use our literature survey as initial reading material It will help expert readers who can use the study to propose novel approaches for steering angle prediction by adopting DL architecture. Different DL architectures that are applied in steering angle prediction of AVs are discussed so that the readers can become acquainted with DL architectures operations and understand how these DL algorithms operate to achieve their goal. These DL architectures will be discussed as follows: A. CNNs has performed brilliantly on other applications such as classification of images, detection of objects, steering angle prediction (Rausch et al [2], Pan et al [18] & Do et al [20]) speech recognition [2], object recognition [20], natural language processing (Wani et al [30] & Do et al [20]), and text processing [30]
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