International Journal For Multidisciplinary Research

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Integrated Engine for Autonomous Driving Using YOLO and SLAM

Author(s) Mr. Varun Naveen Kakapuri, Mr. Vaibhav Bhaskar Raut, Mr. Rohit Ravindra Shilimkar, Prof. Pratima Patil
Country India
Abstract The rapid advancement in autonomous driving technology necessitates the development of
highly efficient systems that ensure accurate object detection, precise localization, and reliable
navigation. This project presents an integrated engine combining YOLO (You Only Look Once)
for real-time object detection and SLAM (Simultaneous Localization and Mapping) for accurate
localization and mapping, enhancing the decision-making and obstacle avoidance capabilities of
autonomous vehicles. YOLO’s deep learning framework allows for the fast detection of various
objects in the vehicle’s environment, while SLAM builds a dynamic, real-time map that ensures the
vehicle maintains awareness of its surroundings. The integration of these technologies provides a
robust solution for navigating complex and dynamic environments, overcoming challenges such as
dynamic obstacle avoidance, lane tracking, and real-time path planning. This project aims to con-
tribute to the development of safer and more efficient autonomous vehicles, focusing on real-time
performance and the seamless interaction between detection and localization systems.
Keywords YOLO (You Only Look Once), SLAM (Simultaneous Localization and Mapping), Object Detection, Lane Detection, Obstacle Avoidance, Deep Learning.
Field Engineering
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-04
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.46881
Short DOI https://doi.org/g9m7j3
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