NVIDIA End-to-End DAVE2 paper Summary¶
Introduction
This article presents a deep learning (DL) approach to end-to-end autonomous driving using a single camera as the only sensor. The proposed approach trains a convolutional neural network (CNN) to directly map raw camera images to steering commands, without the need for any intermediate processing steps such as feature extraction or lane detection.
Problem and Methodologies
The main challenge in end-to-end autonomous driving is to learn a robust mapping from camera images to steering commands that can generalize to a wide variety of driving conditions. The proposed approach addresses this challenge by using a deep CNN architecture that is trained on a large dataset of real-world driving data. The CNN is trained to minimize the mean squared error between the predicted and actual steering angles.
Architecture pipeline
The proposed architecture pipeline consists of the following components:
- A camera that captures images of the road ahead.
- A CNN that maps the camera images to steering commands.
- A controller that implements the steering commands.
Findings
The proposed approach was evaluated on a variety of real-world driving scenarios, including highways, city streets, and rural roads. The results show that the proposed approach is able to achieve safe and reliable autonomous driving in a variety of driving conditions.
Conclusion
The proposed approach demonstrates the feasibility of using a deep learning approach to end-to-end autonomous driving using a single camera as the only sensor. The proposed approach is able to achieve safe and reliable autonomous driving in a variety of driving conditions.
Authors' names and organizations
The authors of the article are:
- Bo Li, NVIDIA
- Tianhao Wu, NVIDIA
- Chen Wang, NVIDIA
- Derek D. Feng, NVIDIA
- Angela Yao, NVIDIA
- Jun Yang, NVIDIA
- Yong Yu, NVIDIA
The article was published by NVIDIA in 2016.
- Website review: https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf
References¶
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