• Sonal S Bawankule
• Riya Chandekar
• Rutika Sawarkar
• Tisha Welekar
• Shubhanshu Wahane

This study proposes an enhanced model of autonomous vehicle navigation that combines real-time lanes, obstacles, and traffic sign recognition and tracking modules with the Global Positioning System (GPS). With reliable vision and precise navigation, the suggested system seeks to improve the safety and accuracy of autonomous cars. The major navigation tool for the autonomous car is the GPS module, which provides route planning and worldwide positioning. In addition, the model includes vision-based lane-detecting sensors, using sensor fusion techniques, the obstacle identification and tracking component combines information from cameras, radar, and LiDAR to identify and track nearby obstacles in real-time. By improving the car's comprehension of traffic laws and regulations, this feature helps make autonomous driving safer and more law-abiding. By merging data from vision sensors, GPS, and other environmental perception modules, the integrated system creates a comprehensive image of the vehicle's surroundings. This data is processed by a centralized decision-making system, which then prepares and implements the best navigation strategies to ensure a safe and enjoyable driving experience. The proposed approach's efficiency is proved in a variety of driving scenarios using extensive simulations and real-world testing.

accurate navigation, route planning, vision-based lane detection sensors, centralized decision making, cameras, and radar.

"AUTOMATIC SMART GPS CAR USING MACHINE LEARNING AND DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.b580-b587, April-2024, Available :http://www.jetir.org/papers/JETIR2404165.pdf

"AUTOMATIC SMART GPS CAR USING MACHINE LEARNING AND DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppb580-b587, April-2024, Available at : http://www.jetir.org/papers/JETIR2404165.pdf