# One-Shot Learning of Visual Path Navigation for Autonomous Vehicles

**Authors:** Zhongying CuiZhu, Francois Charette, Amin Ghafourian, Debo Shi, Matthew Cui, Anjali Krishnamachar, Iman Soltani  
**arXiv:** [2306.08865](https://arxiv.org/abs/2306.08865)  
**Date:** 2023-06-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2306.08865v1/

## Key points

- The paper addresses challenges faced by autonomous driving systems in handling diverse scenarios
- Proposes a deep neural network that incorporates one-shot learning to overcome data problem
- Enables autonomous vehicles to navigate previously unseen paths after being shown the path just once
- Full path not required for navigation; images of road junctions are sufficient
- Reduces amount of data needed for training and improves efficiency of autonomous driving systems
- In-vehicle testing and offline testing conducted to validate approach and compare architectures
- Results demonstrate effectiveness of incorporating one-shot learning into image-to-steering path navigation
- Presents a promising solution to address challenges associated with autonomous driving
- Leverages one-shot learning and deep neural networks
- Contributes to advancing development of more efficient and adaptable autonomous driving systems with limited training data

## Summary

The paper titled "One-Shot Learning of Visual Path Navigation for Autonomous Vehicles" addresses the challenges faced by autonomous driving systems in handling a wide range of scenarios. To overcome the data problem posed by end-to-end deep learning models, the authors propose a novel deep neural network that incorporates one-shot learning into the system. This approach enables an autonomous vehicle to navigate previously unseen paths after being shown the path just once without needing model retraining. The full path is not required for navigation; instead, images of road junctions are sufficient which significantly reduces the amount of data needed for training and improves the efficiency of autonomous driving systems. To validate their approach, in-vehicle testing and offline testing were conducted to verify performance and compare different candidate architectures. The results demonstrate the effectiveness of incorporating one-shot learning into image-to-steering path navigation for autonomous vehicles. This paper presents a promising solution to address challenges associated with autonomous driving by leveraging one-shot learning and deep neural networks which contributes to advancing the development of more efficient and adaptable autonomous driving systems capable of handling diverse scenarios with limited training data.