Research on the Application of Deep Learning In Autonomous Vehicles
This paper examines the application of deep learning in autonomous driving vehicles. The rapid development of autonomous driving technology has provided vast possibilities for utilizing deep learning algorithms. Firstly, this paper introduces the technical components and fundamental knowledge of autonomous driving vehicles. It then delves into the basic principles of deep learning in autonomous driving and its specific applications in various aspects, including visual perception, perception and decision-making, and control and execution. Furthermore, challenges faced by deep learning in autonomous driving are discussed, and future development directions are anticipated. Through this study, a better understanding of the current application status and future trends of deep learning technology in the field of autonomous driving can be obtained.
References
- Zang D, Wei Z, Bao M, et al. Deep learning–based traffic sign recognition for unmanned autonomous vehicles[J]. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2018, 232(5): 497-505.
- Schneider L, Hafner M, Franke U. The Stixel world–A comprehensive representation of traffic scenes for autonomous driving[J]. at - Automatisierungstechnik, 2018, 66(9): 745-751.
- Lateef F, Ruichek Y. Survey on semantic segmentation using deep learning techniques[J]. Neurocomputing, 2019, 338-348.
- Pavithra G, Dhanya N. Curve Path Prediction and Vehicle Detection in Lane Roads Using Deep Learning for Autonomous Vehicles[J]. International Journal of Recent Technology and Engineering (IJRTE), 2019, 7(5s3): 167-170.
- Yudin A D, Skrynnik A, Krishtopik A, et al. Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection[J]. Optical Memory and Neural Networks, 2019, 28(4): 283-295.
- Jiangdong L, Teng L, Xiaolin T, et al. Decision-Making Strategy on Highway for Autonomous Vehicles Using Deep Reinforcement Learning[J]. IEEE ACCESS, 2020, 8177804-177814.
- Sun Y, Cheng J, Zhang G, et al.Mapless Motion Planning System for an Autonomous Underwater Vehicle Using Policy Gradient-based Deep Reinforcement Learning[J]. Journal of Intelligent Robotic Systems, 2019, 96(3-4): 591-601.
- Ferraz P A P, Oliveira D G A B, Ferreira F M F, et al.Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision[J]. IET Intelligent Transport Systems, 2020, 14(10): 1319-1327.