Bezier curve based end-to-end trajectory synthesis for agile autonomous driving

摘要: Demonstrating high-speed autonomous racing can be considered as a grand challenge for vision based end-to-end deep learning models. DeepRacing AI is a novel end-to-end framework for trajectory synthesis for autonomous racing. We train and demonstrate the effectiveness of our approach using a high fidelity and photo-realistic Formula One gaming environment-used by real racing drivers. This is the first work that has used the highly realistic F1 game as a simulation environment for deep learning models. We present a novel method for single agent autonomous racing by training a deep neural network to predict a parameterized representation of a trajectory. Our Bezier curve based trajectory synthesis approach outperforms several other end-to-end DNN approaches for autonomous racing. In addition to evaluating our methodology in a closed-loop manner in the game; we also implement the DeepRacing algorithm on a 1/10 scale autonomous racing test-bed and show its ability to handle real-world data at high speeds.