On a visually complex simulated track, a slim 3D CNN beat LSTM and GRU recurrent models on average lap time, but the model choice was made on the same test track.
UruBots Autonomous Cars Team One Description Paper for FIRA 2024
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abstract
This document presents the design of an autonomous car developed by the UruBots team for the 2024 FIRA Autonomous Cars Race Challenge. The project involves creating an RC-car sized electric vehicle capable of navigating race tracks with in an autonomous manner. It integrates mechanical and electronic systems alongside artificial intelligence based algorithms for the navigation and real-time decision-making. The core of our project include the utilization of an AI-based algorithm to learn information from a camera and act in the robot to perform the navigation. We show that by creating a dataset with more than five thousand samples and a five-layered CNN we managed to achieve promissing performance we our proposed hardware setup. Overall, this paper aims to demonstrate the autonomous capabilities of our car, highlighting its readiness for the 2024 FIRA challenge, helping to contribute to the field of autonomous vehicle research.
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Mini Autonomous Car Driving based on 3D Convolutional Neural Networks
On a visually complex simulated track, a slim 3D CNN beat LSTM and GRU recurrent models on average lap time, but the model choice was made on the same test track.