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

arXiv: https://arxiv.org/abs/2306.08865 · HTML: https://mlsanity.com/paper/2306.08865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation](https://mlsanity.com/paper/2105.09371/) (2021)
- [End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception](https://mlsanity.com/paper/1801.06734/) (2018)
- [Hierarchical Variational Memory for Few-shot Learning Across Domains](https://mlsanity.com/paper/2112.08181/) (2021)
- [Fast and Robust Bio-inspired Teach and Repeat Navigation](https://mlsanity.com/paper/2010.11326/) (2020)
- [Few-Shot Learning for Road Object Detection](https://mlsanity.com/paper/2101.12543/) (2021)
- [A Few Shot Adaptation of Visual Navigation Skills to New Observations using Meta-Learning](https://mlsanity.com/paper/2011.03609/) (2020)
- [MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving](https://mlsanity.com/paper/1709.05581/) (2017)
- [Hierarchical Multi-task Deep Neural Network Architecture for End-to-End Driving](https://mlsanity.com/paper/1902.03466/) (2019)

## Built on by (most cited first, 1 of 1)

- [Hierarchical end-to-end autonomous navigation through few-shot waypoint detection](https://mlsanity.com/paper/2409.14633/) (2024)

## Abstract (arXiv)

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.