Deep Learning Approaches for Autonomous Driving a Comprehensive Survey

Investments into autonomous driving have created a revolutionary technology which is changing the way people traverse through space. The paper summarizes modern deep learning methods used in autonomous vehicles by exploring fundamental elements which include sensing objects and segmentation and path planning as well as sensor unification. We review multiple deep learning structures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and transformers which find practical use in modern driving operations. We examine the deployment difficulties of DL-based autonomous systems which include difficulties in generalization and safety concerns as well as interpretability issues. The conclusion introduces potential advancements and new research paths which aim to boost the reliability together with robustness of autonomous driving systems that use deep learning techniques.