• Archana Sasi
• Chaya Swaroop S
• Koulik Saha
• Rohit S
• Bhuvan S M, Kristamsetty Akhil

Increasing safety and reducing road accidents are goals of Advanced Driver Assistance Systems (ADAS), hence saving lives which are of major concern. Road lane recognition and traffic sign detection are likely to be among the more significant challenges that future road vehicles will confront. According to research, the majority of accidents are caused by driver negligence, such as refusal to maintain road lanes, failing to obey traffic signs and regulations, and exceeding the speed limit. This research provides an accurate and efficient approach that can automatically recognize road lanes and traffic signs and respond accordingly. In our study, a real-time lane detection method based on Infrared (IR) technology and a sign detection method employing image processing and a machine learning algorithm are proposed. Since there would be no need for a driver or fuel because it runs on electricity, implementing these would be economical. Evaluation of the proposed framework is done by K-Means clustering algorithm using OpenCV. The proposed method was tested, and the outcomes demonstrated that it is reliable and efficient enough for real-time requirements.

Autonomous Vehicle, Machine Learning, OpenCV, Raspberry Pi, Road Lane, Sensors, Sign Detection;

"Real-Time Road Lane Recognition and Automation Traffic Sign Detection for Autonomous Vehicle", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 11, page no.d479-d486, November-2022, Available :http://www.jetir.org/papers/JETIR2211366.pdf

"Real-Time Road Lane Recognition and Automation Traffic Sign Detection for Autonomous Vehicle", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 11, page no. ppd479-d486, November-2022, Available at : http://www.jetir.org/papers/JETIR2211366.pdf