Version 1
: Received: 17 July 2024 / Approved: 18 July 2024 / Online: 18 July 2024 (08:19:17 CEST)
How to cite:
Schmidt, A.; Bianchi, M.; Müller, S. Advancements in Deep Learning for Driving Policy and Perception in Autonomous Vehicles. Preprints2024, 2024071471. https://doi.org/10.20944/preprints202407.1471.v1
Schmidt, A.; Bianchi, M.; Müller, S. Advancements in Deep Learning for Driving Policy and Perception in Autonomous Vehicles. Preprints 2024, 2024071471. https://doi.org/10.20944/preprints202407.1471.v1
Schmidt, A.; Bianchi, M.; Müller, S. Advancements in Deep Learning for Driving Policy and Perception in Autonomous Vehicles. Preprints2024, 2024071471. https://doi.org/10.20944/preprints202407.1471.v1
APA Style
Schmidt, A., Bianchi, M., & Müller, S. (2024). Advancements in Deep Learning for Driving Policy and Perception in Autonomous Vehicles. Preprints. https://doi.org/10.20944/preprints202407.1471.v1
Chicago/Turabian Style
Schmidt, A., Mateo Bianchi and Sophie Müller. 2024 "Advancements in Deep Learning for Driving Policy and Perception in Autonomous Vehicles" Preprints. https://doi.org/10.20944/preprints202407.1471.v1
Abstract
This paper systematically discusses the application of reinforcement learning in automatic driving system. Reinforcement learning frameworks show significant advantages in optimizing decision making, predictive perception, path planning, and controller design, exceeding the limitations of traditional supervised learning methods. The paper highlights the critical role of components such as scene understanding, positioning, and map making in autonomous driving systems, which provide reliable environmental awareness through deep learning and sensor fusion technologies to support intelligent decision-making in complex urban environments. In addition, the paper discusses innovative approaches to safety reinforcement learning to reduce risk in autonomous driving and ensure that systems adhere strictly to safety constraints while maximizing expected rewards. These findings provide an important theoretical and practical basis for further improving algorithm robustness, managing multi-agent interactions, and integrating ethical considerations in the future.
Keywords
Reinforcement Learning; Autonomous Driving; Perception and Decision-making; Safety and Optimization.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
