Enhancing Navigation Algorithms with Semantic Embeddings
This is an idea proposed in 2024 as a Cambridge Computer Science Part III or MPhil project, and is currently being worked on by Gabriel Mahler.
Pathfinding algorithms used in modern navigation systems utilize a plethora of different geospatial data, such as from OpenStreetMap. Nevertheless, they often operate under one-size-fits-all assumptions and the simple objective of minimizing the anticipated travel time.
By leveraging vectorized geospatial descriptions, this project aims to build a framework for finding walking routes that seek to achieve much more customizable objectives. Given a set of specific requirements and preferences ("avoid dark streets at night"), we aim to leverage the semantic representation of a given area to select relevant geospatial data.
Once points of interest are selected, we then generate a specific walking route that seeks to fulfill the initial requirements by trying to maximize their vectorized similarity to a semantic representation of the route. The potential of the framework, and its contrasting versatility to existing path-finding algorithms, can be evaluated through experiments that reflect real-world scenarios such as accessibility, goals ("are we going shopping or just for a walk in nature?").
Related reading
ideasRelated News
- Remote Sensing of Nature / Jan 2023