Robust Neural Network for Sim-to-Real Gap in End-to-End Autonomous Driving

被引：1

作者：

Pareigis, Stephan ^{[1
]}

Maass, Fynn Luca ^{[1
]}

机构：

[1] HAW Hamburg, Dept Comp Sci, Berliner Tor 7, D-20099 Hamburg, Germany

来源：

PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS (ICINCO) | 2022年

关键词：

Sim-to-Real Gap; End-to-End Learning; Autonomous Driving; Artificial Neural Network; CARLA Simulator; Robust Control; PilotNet;

D O I：

10.5220/0011140800003271

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A neural network architecture for end-to-end autonomous driving is presented, which is robust against discrepancies in system dynamics during the training process and in application. The proposed network architecture presents a first step to alleviate the simulation to reality gap with respect to differences in system dynamics. A vehicle is trained to drive inside a given lane in the CARLA simulator. The data is used to train NVIDIA's PilotNet. When an offset is given to the steering angle of the vehicle while the trained network is being applied, PilotNet will not keep the vehicle inside the lane as expected. A new architecture is proposed called PilotNet., which is robust against steering angle offsets. Experiments in the simulator show that the vehicle will stay in the lane, although the steering properties of the vehicle differ.

引用

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页码：113 / 119

页数：7

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