Autonomous driving presents many challenges due to the large number of scenarios the autonomous vehicle (AV) may encounter. End-to-end deep learning models are comparatively simplistic models that can handle a broad set of scenarios. However, end-to-end models require large amounts of diverse data to perform well. This paper presents a novel deep neural network that performs image-to-steering path navigation that helps with the data problem by adding one-shot learning to the system. Presented with a previously unseen path, the vehicle can drive the path autonomously after being shown the path once and without model retraining. In fact, the full path is not needed and images of the road junctions is sufficient. In-vehicle testing and offline testing are used to verify the performance of the proposed navigation and to compare different candidate architectures.
@article{arxiv.2306.08865,
title = {One-Shot Learning of Visual Path Navigation for Autonomous Vehicles},
author = {Zhongying CuiZhu and Francois Charette and Amin Ghafourian and Debo Shi and Matthew Cui and Anjali Krishnamachar and Iman Soltani},
journal= {arXiv preprint arXiv:2306.08865},
year = {2023}
}
Comments
Machine Learning for Autonomous Driving Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS 20222), New Orleans, USA