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A Motion Planning System for the Autonomous Vechicle with Nvidia Jetson Nano

Student: Murzabekov Almaz

Supervisor: Vasily Kornilov

Faculty: Graduate School of Business

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2021

Autonomous Vehicles (AV) as self-driving cars, delivery robots, air drones are increasingly penetrating our lives. AV moves independently thanks to special software programs and hardware devices. Self-driving cars promise to bring big changes in the life of mankind, as the very concept of transportation will change dramatically. Such vehicles’ key advantage will be the multiple (possibly even complete) disappearance of road accidents with fatalities. Autonomous vehicles will significantly reduce the cost of transporting goods, increase the efficiency of using highways. Autonomous systems can perceive the environment and move safely almost without or little human help. Modern AV systems sense the road scene, avoid any accident and collision and achieve the fastest response for any changes in the dynamic variables. This work aims to provide a detailed analysis of the state of the art for the research and development of AV. The theoretical studies that have been successfully applied to commercial products also will be described. While building a fully autonomous vehicle (AV) is an over-complex problem, and the building process contains a huge number of topics, the main focus of this work will be on the topic of the motion planning system. The motion planning system is an essential part of each AV. A detailed description of hardware and software stack for building a fully AV, the implementation of physical robotics with variety of hardware devices will be placed in the scope of this work, as well as developing the software part of motion planning system. Keywords: Autonomous Vehicles, Self-Driving Cars, Delivery Robots, Artificial Intelligence, AI, Big Data, Motion Planning System, Computer Vision, Machine Learning, SLAM, NVidia Jetson Nano, Arduino, Hardware, Software.

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