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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.<\/jats:p>","DOI":"10.3233\/web-230011","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T11:16:54Z","timestamp":1691493414000},"page":"185-207","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving"],"prefix":"10.1177","volume":"22","author":[{"given":"Swati","family":"Jaiswal","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B. 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