単眼魚眼カメラによる車両横方向制御のエンドツーエンド学習
End to End Vehicle Lateral Control Using a Single Fisheye Camera
短距離魚眼カメラ1台のみで車線維持のためのステアリング制御をエンドツーエンドで学習し、実走行とシミュレータで評価した研究。
著者: Marin Toromanoff, Emilie Wirbel, Frédéric Wilhelm, Camilo Vejarano, Xavier Perrotton, Fabien Moutarde
分類: cs.RO, cs.LG, stat.ML
原文アブストラクト
Convolutional neural networks are commonly used to control the steering angle for autonomous cars. Most of the time, multiple long range cameras are used to generate lateral failure cases. In this paper we present a novel model to generate this data and label augmentation using only one short range fisheye camera. We present our simulator and how it can be used as a consistent metric for lateral end-to-end control evaluation. Experiments are conducted on a custom dataset corresponding to more than 10000 km and 200 hours of open road driving. Finally we evaluate this model on real world driving scenarios, open road and a custom test track with challenging obstacle avoidance and sharp turns. In our simulator based on real-world videos, the final model was capable of more than 99% autonomy on urban road
関連論文
- 計画を超えて：微分可能な制御ループ内蔵のエンドツーエンド自動運転自動運転/エンドツーエンド制御
