AUTOPILOT：基于YOLOv7与MiDaS的自动驾驶车辆先进感知、定位与路径规划技术
AUTOPILOT An Advanced Perception, Localization and Path Planning Techniques for Autonomous Vehicles Using YOLOv7 and MiDaS
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中文总结 AI 辅助
本文提出结合YOLO目标检测与MiDaS深度感知的自动驾驶感知与定位系统，通过仿真实验验证其稳健性，为障碍物感知与路径规划提供新方法。
中文摘要 AI 辅助
自动驾驶汽车已成为一种可靠的技术，有望变革交通运输与出行方式。自动驾驶汽车的发展需要在感知、定位、决策制定和控制等多个领域取得重大进展。本研究论文基于一个项目实现，该项目结合了使用YOLO（You Only Look Once，一种实时目标检测算法）进行目标检测、使用MiDaS进行深度感知以实现障碍物的定位与感知、透视变换以及自动驾驶汽车路径规划中的决策制定。当前目标检测、深度感知、定位和路径规划的技术水平通过仿真和实验评估了组合系统的性能。结果表明，YOLO与MiDaS的组合为目标检测和深度感知提供了一种新的稳健系统。本研究论文为自动驾驶汽车技术的进步做出了贡献，并为环境中障碍物的感知和定位提供了新的创新方法。关键词：YOLO，MiDaS，感知，定位，决策制定。
英文摘要
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
发表机构
- Vidyavardhini’s College of Engineering and Technology（维迪亚瓦德希尼工程技术学院）
机构由 AI 辅助整理，请以论文原文为准。
