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Deep learning in driverless: Research results, issues, challenges
- 1 Tianjin University
* Author to whom correspondence should be addressed.
Abstract
As computing and communication technologies continue to evolve, leading technology companies are investing significant resources in self-driving car techniques. With the use of deep learning in driverless driving, driverless cars are enabling them to sense, make decisions and control in complex environments, leading to truly autonomous driving. Deep learning is used for environment perception, which enables driverless cars to understand their surroundings by recognizing information such as road surfaces, pedestrians and vehicles, and also used for decision formulation, by predicting the behavior of other vehicles, pedestrians and identifying the environment around the road conditions to control the driverless car to make the optimal driving decision. But in order to achieve fully autonomous driving, there are still some technical aspects. Therefore, we deep learning in the existing driverless technology is reviewed, mainly discussing the application in the direction of driverless implementation and safety inspection. Through reading a large amount of literature, we discuss the problems that hinder the development of the driverless field, and at the same time, how to solve the problems in the future to promote the breakthrough of the driverless field. The application of deep learning in driverless not only enhances the intelligence level of driverless cars, but also prepares for the commercial application of driverless cars in the future. It enables driverless cars to better identify and adapt to complex traffic environments. Finally, realizing truly autonomous driving.
Keywords
Deep learning, driverless, autonomous driving
[1]. Kaur, K., Rampersad, G., 2018. Trust in driverless cars: investigating key factors influencing the adoption of driverless cars. J. Eng. Technol. Manag. 48, 87–96.
[2]. Y. Li, H. Cheng, Z. Zeng, H. Liu and M. Sester, "Autonomous Vehicles Drive into Shared Spaces: eHMI Design Concept Focusing on Vulnerable Road Users," 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA, 2021, pp. 1729-1736, doi: 10.1109/ITSC48978.2021.9564515.
[3]. R. Hussain and S. Zeadally, "Autonomous Cars: Research Results, Issues, and Future Challenges," in IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1275-1313, Secondquarter 2019, doi: 10.1109/COMST.2018.2869360.
[4]. R. Qian, Y. Yue, F. Coenen and B. Zhang, "Traffic sign recognition with convolutional neural network based on max pooling positions," 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, China, 2016, pp. 578-582, doi: 10.1109/FSKD.2016.7603237.
[5]. Abdel-Salam, R., Mostafa, R. & Abdel-Gawad, A.H. RIECNN: real-time image enhanced CNN for traffic sign recognition. Neural Comput & Applic 34, 6085–6096 (2022). https://ifbic1b13095ec5284139s6b9fnp59v0fc6ccbfiac.eds.tju.edu.cn/10.1007/s00521-021-06762-5
[6]. Du, Shuyang et al. “Self-Driving Car Steering Angle Prediction Based on Image Recognition.” ArXiv abs/1912.05440 (2019): n. pag.
[7]. G. Karmakar, A. Chowdhury, R. Das, J. Kamruzzaman and S. Islam, "Assessing Trust Level of a Driverless Car Using Deep Learning," in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4457-4466, July 2021, doi: 10.1109/TITS.2021.3059261.
[8]. Y. Ma, Z. Li and M. A. Sotelo, "Testing and Evaluating Driverless Vehicles' Intelligence: The Tsinghua Lion Case Study," in IEEE Intelligent Transportation Systems Magazine, vol. 12, no. 4, pp. 10-22, winter 2020, doi: 10.1109/MITS.2020.3014432.
Cite this article
Fu,J. (2024). Deep learning in driverless: Research results, issues, challenges. Applied and Computational Engineering,92,1-5.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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