AUTOPILOT An Advanced Perception, Localization and Path Planning Techniques for Autonomous Vehicles Using YOLOv7 and MiDaS

Abstract:Self driving vehicles have emerged as a reliable technology that has the capability to transform transportation and mobility. The development of self driving cars requires significant advances in a number of areas, including perception, localization, decision making, and control. This research paper is based on the project implementation of the combination of object detection using YOLO (You Only Look Once), depth sensing using MiDaS for the localization and perception of obstacles, perspective transform, and decision making for path planning in self driving cars. The contemporary state of the technology for object detection, depth sensing, localization, and path planning evaluates the performance of the combined system through simulations and experiments. The results show that the combination of YOLO and MiDaS provides a new robust system for object detection and depth sensing. This research paper contributes to the advancement of self driving car technology and provides new and innovative approaches to the perception and localization of obstacles in the environment. Keywords: YOLO, MiDaS, perception, localization, decision making

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2610.06232 [cs.RO]

(or arXiv:2610.06232v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.06232 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

H. Devmurari, G. Kuckian, and P. Vishwakarma, Proc. IEEE ICACTA 2023

https://doi.org/10.1109/ICACTA58201.2023.10393218 Focus to learn more DOI(s) linking to related resources

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