Research on the application of artificial intelligence to auto-driving cars

Research Article
Open access

Research on the application of artificial intelligence to auto-driving cars

Ziyang Lin 1*
  • 1 The Ohio State University, Columbus, Ohio, 43201, US    
  • *corresponding author Lin.3557@osu.edu
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230856
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Since the concept of artificial intelligence was introduced in the 1950s, artificial intelligence technologies have emerged more and more frequently. Besides, the application of artificial intelligence technologies in driverless cars has increased, especially in the field of image recognition. Many deep learning methods of image recognition have emerged in the field of autonomous driving. The primary purpose of this paper is to highlight the importance and functionality of using neural networks and image recognition in artificial intelligence for driverless functions. This paper also provides a comprehensive review of the core aspects of driverless technology, the role of image recognition and person recognition in environment perception, the impact of deep learning on driverless behavior decisions, and driverless vehicle kinetic energy control systems. Although the driverless car technology is now being updated, all three modes have great accuracy and results. However, each subsystem still faces many problems and challenges, which may be solved in future research. The driverless car technology would be promoted on a large scale after its safety and suitability are ensured.

Keywords:

Auto Drive, Deep Learning, Image Recognition, Artificial Intelligence, Neural Network.

Lin,Z. (2023). Research on the application of artificial intelligence to auto-driving cars. Applied and Computational Engineering,6,447-452.
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References

[1]. SAE International. SAE levels of Driving Automation™ refined for clarity and international audience (2021). Available at: https://www.sae.org/blog/sae-j3016-update.

[2]. Fujiyoshi, H., Hirakawa, T. and Yamashita, T. Deep learning-based image recognition for autonomous driving. IATSS Research. Elsevier. (2019). Available at: https://www.sciencedirect.com/science/article/pii/S0386111219301566.

[3]. IBM Cloud Education. What are convolutional neural networks? IBM. (2020). Available at: https://www.ibm.com/cloud/learn/convolutional-neural-networks.

[4]. Ji, Z., Xiao, W. Improving decision-making efficiency of image game based on deep Q-learning. Soft Comput 24, 8313–8322 (2020). Available at: https://doi.org/10.1007/s00500-020-04820-z.

[5]. Li, Y., Hu, X., Zhuang, Y., Gao, Z., Zhang P. and El-Sheimy, N. Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization. in IEEE Internet of Things Journal 7(7), 6279-6287 (2020). DOI: 10.1109/JIOT.2019.2957778.

[6]. Malik, N. Artificial Neural Networks and their applications. arXiv.org. (2005). Available at: https://arxiv.org/abs/cs/0505019.

[7]. IBM Cloud Education. What are neural networks? IBM. Available at: https://www.ibm.com/cloud/learn/neural-networks#:~:text=Neural%20networks%2C%20also%20known%20as%20artificial%20neural%20networks,way%20that%20biological%20neurons%20signal%20to%20one%20another.

[8]. Wood, T. Backpropagation. DeepAI. (2020). Available at: https://deepai.org/machine-learning-glossary-and-terms/backpropagation.

[9]. Hyder, G., Chowdhry, B. S., Memon K. and Ahmed, A. The Smart Automobile (SAM): An Application Based on Drowsiness Detection, Alcohol Detection, Vital Sign Monitoring and Lane based Auto Drive to avoid Accidents. 2020 Global Conference on Wireless and Optical Technologies (GCWOT). 1-10 (2020). DOI: 10.1109/GCWOT49901.2020.9391617.

Cite this article

Lin,Z. (2023). Research on the application of artificial intelligence to auto-driving cars. Applied and Computational Engineering,6,447-452.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. SAE International. SAE levels of Driving Automation™ refined for clarity and international audience (2021). Available at: https://www.sae.org/blog/sae-j3016-update.

[2]. Fujiyoshi, H., Hirakawa, T. and Yamashita, T. Deep learning-based image recognition for autonomous driving. IATSS Research. Elsevier. (2019). Available at: https://www.sciencedirect.com/science/article/pii/S0386111219301566.

[3]. IBM Cloud Education. What are convolutional neural networks? IBM. (2020). Available at: https://www.ibm.com/cloud/learn/convolutional-neural-networks.

[4]. Ji, Z., Xiao, W. Improving decision-making efficiency of image game based on deep Q-learning. Soft Comput 24, 8313–8322 (2020). Available at: https://doi.org/10.1007/s00500-020-04820-z.

[5]. Li, Y., Hu, X., Zhuang, Y., Gao, Z., Zhang P. and El-Sheimy, N. Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization. in IEEE Internet of Things Journal 7(7), 6279-6287 (2020). DOI: 10.1109/JIOT.2019.2957778.

[6]. Malik, N. Artificial Neural Networks and their applications. arXiv.org. (2005). Available at: https://arxiv.org/abs/cs/0505019.

[7]. IBM Cloud Education. What are neural networks? IBM. Available at: https://www.ibm.com/cloud/learn/neural-networks#:~:text=Neural%20networks%2C%20also%20known%20as%20artificial%20neural%20networks,way%20that%20biological%20neurons%20signal%20to%20one%20another.

[8]. Wood, T. Backpropagation. DeepAI. (2020). Available at: https://deepai.org/machine-learning-glossary-and-terms/backpropagation.

[9]. Hyder, G., Chowdhry, B. S., Memon K. and Ahmed, A. The Smart Automobile (SAM): An Application Based on Drowsiness Detection, Alcohol Detection, Vital Sign Monitoring and Lane based Auto Drive to avoid Accidents. 2020 Global Conference on Wireless and Optical Technologies (GCWOT). 1-10 (2020). DOI: 10.1109/GCWOT49901.2020.9391617.

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