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End to End 학습 기반 자율 주행 프레임워크 개발 및 실차 기반 실험
정찬영(Chan-Young Jung),성현기(Hyun-Ki Seong),심현철(David Hyun-Chul Shim) 제어로봇시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.5
In recent years, autonomous vehicles have been developed by various approaches for traffic safety and driver convenience. End-to-end learning-based autonomous driving has gained enormous attention in conjunction with deep learning technologies in which perception, planning, and control of the conventional autonomous driving algorithm are trained by a single deep neural network. In this paper, we present the end-to-end learning-based autonomous driving framework. The framework consisted of three parts: data acquisition in real-world and simulated environments, network design and optimization, and performance evaluation. Our framework was integrated on a full-scale autonomous vehicle platform and evaluated with various performance metrics.