End to End Vehicle Lateral Control Using a Single Fisheye Camera

By Marin Toromanoff, É. Wirbel, F. Wilhelm, Camilo Vejarano, Xavier Perrotton, Fabien Moutarde. 2018

42 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Robotics & Control

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Citations in the corpus

WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, background): “demonstrated end-to-end learning [2] for steering and recently it was applied to fisheye cameras [54].”
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment (2019, methodology): “In [33, 11] the recorded data is modified to perform label augmentation: data similar to failure cases is generated a posteriori, but without the need of additional sensors.”