Deep Learning Approaches for Autonomous Driving a Comprehensive Survey

Authors

  • Vasanthamma Professor, Department of CS-AIML, Proudhadevaraya Institute of Technology Hosapete, India
  • Manoj Dubey Associate Professor, Department of ASH-Mathematics, PIT, Parul University, India
  • Kanaparthi Kantharaju Assistant Professor, Department of Artificial Intelligence & Data Science, Koneru Lakshmaiah Education Foundation, Green Fields, India
  • Naga Venkateshwara Rao Kollipara Assistant Professor, Department of ECE, St.Martins Engineering College, India
  • M. Sumalatha Assistant Professor, Department of Computer Science and Engineering, Swarnandhra College of Engineering and Technology, India

DOI:

https://doi.org/10.63278/mme.v31i1.1253

Keywords:

Autonomous driving, deep learning, convolutional neural networks, sensor fusion, path planning, object detection, reinforcement learning.

Abstract

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.

References

S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, "A Survey of Deep Learning Techniques for Autonomous Driving," arXiv preprint arXiv:1910.07738, 2019. Available: https://arxiv.org/abs/1910.07738

Y. Huang and Y. Chen, "Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies," arXiv preprint arXiv:2006.06091, 2020. Available: https://arxiv.org/abs/2006.06091

Y. Huang and Y. Chen, "Survey of State-of-Art Autonomous Driving Technologies with Deep Learning," in Proceedings of the 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C), Macau, China, 2020, pp. 221-228. Available: https://ieeexplore.ieee.org/document/9282663

A. Dosovitskiy et al., "CARLA: An Open Urban Driving Simulator," in Proceedings of the 1st Annual Conference on Robot Learning (CoRL), Mountain View, CA, USA, 2017, pp. 1-16. Available: https://arxiv.org/abs/1711.03938

P. S. Chib and P. Singh, "Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey," arXiv preprint arXiv:2307.04370, 2023. Available: https://arxiv.org/abs/2307.04370

X. Zhu et al., "A Survey on Deep Learning Approaches for Data Integration in Autonomous Driving Systems," arXiv preprint arXiv:2306.11740, 2023. Available: https://arxiv.org/abs/2306.11740

A. Singh, "End-to-End Autonomous Driving using Deep Learning: A Systematic Review," arXiv preprint arXiv:2311.18636, 2023. Available: https://arxiv.org/abs/2311.18636

L. He et al., "Neural Radiance Field in Autonomous Driving: A Survey," arXiv preprint arXiv:2404.13816, 2024. Available: https://arxiv.org/abs/2404.13816

M. Bojarski et al., "End to End Learning for Self-Driving Cars," arXiv preprint arXiv:1604.07316, 2016. Available: https://arxiv.org/abs/1604.07316

S. Chen et al., "Deep Reinforcement Learning for Autonomous Driving: A Survey," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 7225-7246, Nov. 2022. Available: https://ieeexplore.ieee.org/document/9351818

Y. Huang and Y. Chen, "Survey of State-of-Art Autonomous Driving Technologies with Deep Learning," in Proceedings of the 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C), Macau, China, 2020, pp. 221-228. Available: https://qrs20.techconf.org/QRSC2020_FULL/pdfs/QRS-C2020-4QOuHkY3M10ZUl1MoEzYvg/891500a221/891500a221.pdf

S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, "A Survey of Deep Learning Techniques for Autonomous Driving," arXiv preprint arXiv:1910.07738, 2019.Available: https://arxiv.org/pdf/1910.07738.pdf

Y. Huang and Y. Chen, "Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies," arXiv preprint arXiv:2006.06091, 2020. Available: https://arxiv.org/pdf/2006.06091.pdf

Y. Huang and Y. Chen, "Survey of State-of-Art Autonomous Driving Technologies with Deep Learning," in Proceedings of the 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C), Macau, China, 2020, pp. 221-228. Available: https://qrs20.techconf.org/QRSC2020_FULL/pdfs/QRS-C2020-4QOuHkY3M10ZUl1MoEzYvg/891500a221/891500a221.pdf

A. Dosovitskiy et al., "CARLA: An Open Urban Driving Simulator," in Proceedings of the 1st Annual Conference on Robot Learning (CoRL), Mountain View, CA, USA, 2017, pp. 1-16. Available: https://arxiv.org/pdf/1711.03938.pdf

P. S. Chib and P. Singh, "Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey," arXiv preprint arXiv:2307.04370, 2023. Available: https://arxiv.org/pdf/2307.04370.pdf

X. Zhu et al., "A Survey on Deep Learning Approaches for Data Integration in Autonomous Driving Systems," arXiv preprint arXiv:2306.11740, 2023.Available: https://arxiv.org/pdf/2306.11740.pdf

A. Singh, "End-to-End Autonomous Driving using Deep Learning: A Systematic Review," arXiv preprint arXiv:2311.18636, 2023. Available: https://arxiv.org/pdf/2311.18636.pdf

Downloads

How to Cite

Vasanthamma, Manoj Dubey, Kanaparthi Kantharaju, Naga Venkateshwara Rao Kollipara, and M. Sumalatha. 2025. “Deep Learning Approaches for Autonomous Driving a Comprehensive Survey”. Metallurgical and Materials Engineering 31 (1):346-54. https://doi.org/10.63278/mme.v31i1.1253.

Issue

Vol. 31 No. 1 (2025)

Section

Research

License

Copyright (c) 2025 Vasanthamma, Manoj Dubey, Kanaparthi Kantharaju, Naga Venkateshwara Rao Kollipara, M. Sumalatha

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their published articles online (e.g., in institutional repositories or on their website, social networks like ResearchGate or Academia), as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

 

Creative Commons License
Except where otherwise noted, the content on this site is licensed under a Creative Commons Attribution 4.0 International License.