基于车道检测和交通标志检测的深度学习路径跟踪控制

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Swati Jaiswal, B. C. Mohan
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引用次数: 0

摘要

自动驾驶汽车是交通运输技术的重大进步,它提供了安全、可持续、可靠的交通运输。车道检测、机动预测和交通标志识别是自动驾驶汽车的基础。因此,本研究的重点是开发一个动态的实时决策系统,通过深度学习技术的进步,在自动驾驶汽车中获得有效的驾驶体验。本研究利用深度卷积神经网络(deep CNN)、SegNet等深度学习分类器进行交通信号检测、道路分割、车道检测。该研究的主要亮点依赖于提出的Finch Hunt优化,该优化涉及深度学习分类器的超参数调优。该实时决策系统准确率为97.44%,灵敏度为97.56%,特异度为97.83%。此外,本文提出的分割模型的聚类准确率最高,为90.37%,车道检测模型的平均绝对误差、均方误差和均方根误差最低,分别为17.76%、11.32%和5.66%。本文提出的道路分割模型在聚类精度上优于所有同类模型。最后,与现有模型相比,该模型提供了更好的车道检测输出,且误差最小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving
Automated vehicles are a significant advancement in transportation technique, which provides safe, sustainable, and reliable transport. Lane detection, maneuver forecasting, and traffic sign recognition are the fundamentals of automated vehicles. Hence, this research focuses on developing a dynamic real-time decision-making system to obtain an effective driving experience in autonomous vehicles with the advancement of deep learning techniques. The deep learning classifier such as deep convolutional neural network (Deep CNN), SegNet and are utilized in this research for traffic signal detection, road segmentation, and lane detection. The main highlight of the research relies on the proposed Finch Hunt optimization, which involves the hyperparameter tuning of a deep learning classifier. The proposed real-time decision-making system achieves 97.44% accuracy, 97.56% of sensitivity, and 97.83% of specificity. Further, the proposed segmentation model achieves the highest clustering accuracy with 90.37% and the proposed lane detection model attains the lowest mean absolute error, mean square error, and root mean error of 17.76%, 11.32%, and 5.66% respectively. The proposed road segmentation model exceeds all the competent models in terms of clustering accuracy. Finally, the proposed model provides a better output for lane detection with minimum error, when compared with the existing model.
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
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