A Survey on Theories and Application for Self Driving Cars on Deep Learning Methods
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In today’s, Self-driving cars are one of the hot research topics in technology, which has a great
impact on social and economic development. Self-driving cars, powered by deep learning
algorithms, represent a groundbreaking fusion of artificial intelligence and transportation
technology. Deep learning is also one of the current major areas in artificial intelligence
research. It has been widely applied in natural language understanding, image processing, etc.
In recent years, more and more deep learning methods have been introduced to the solution
in the field of self-driving cars and have achieved excellent results. This survey provides
information related to self-driving cars and summarizes the application of deep learning
methods in the field of self-driving cars. Then the main problems in self-driving cars and their
solutions are analyzed based on deep learning methods, such as lane detection, scene
recognition and navigation etc. Also briefly described are some representative approaches to
self-driving cars using deep learning methods. Finally, future challenges in applications of deep
learning for self-driving cars are described.
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Bojarski, Mariusz, et al. "End to end learning for self-driving cars." arXiv preprint arXiv:1604.07316 (2016).
Chen, Congcong, et al. "Deepdriving: Learning affordance for direct perception in autonomous driving." Proceedings of the IEEE international conference on computer vision. 2017.
Li, Jie, et al. "End-to-end deep reinforcement learning for lane keeping assist in autonomous driving." IEEE Intelligent Vehicles Symposium (IV). 2017.
Zhang, Yuke, et al. "Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search." IEEE Robotics and Automation Letters 3.2 (2018): 787-794.
Pan, Xingchen, et al. "Virtual to real reinforcement learning for autonomous driving." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
Chen, Xiaozhou, et al. "End-to-end learning of driving models from large-scale video datasets." IEEE Transactions on Intelligent Transportation Systems 21.4 (2019): 1502-1514.
Ma, Chao, et al. "Deep reinforcement learning for autonomous driving: A survey." arXiv preprint arXiv:2008.03835 (2020).
Kato, Shohei, et al. "End-to-end multi-modal multi-task vehicle control for self-driving cars with visual perception." IEEE Robotics and Automation Letters 5.2 (2020): 3246-3253.
Wu, Zheng, et al. "Recent advances and future trends in reinforcement learning for autonomous vehicle control." IEEE Transactions on Intelligent Transportation Systems (2020).
www.researchgate.net (Figure : 1 and Figure : 2(a))
Wired.com (Figure : 2(b))
Copyright (c) 2024 Palak Verma
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