Route Natvigation Recommendation System with Large Language Model

Which route is the best to drive from Monash University (Clayton campus) to Melbourne CBD? 

For many of us, answering this question would likely mean opening a route natvigation app and asking the provider to give us the fastest route. For some of us, this question might not need to be answered as you may already be experienced to drive from Monash Uni to CBD, or simply find that the route computed by the app is insufficent to handle your specific requirements, preferences, or constraints. 

Route natvigation is an important tool in driving, and particular useful to drivers who are new or have no experience to the target location. While many drivers would find adequate to use the shortest path (route) by asking the app to solve the simpliest form of natgivation problem - the shortest path problem, many users with complex requirements, preferences, or constraints would find the shortest path solution insufficient to meet their demands. For example:

• My rental vehicle in Europe is pretty large, is the computed path sufficient for the vehicle to pass through?
• My vehicle has low clearance, would the path easy for the vehicle to drive on (with as little patholes/unpaved roads as possible)?
• I am on a sightseeing trip, can we also ensure the route is not boring (with beautiful scenary)? 

Unfortunately, these questions are challenging to be answered by the simple shortest path algorithm. Users often want visual images justifying the choices, explanations on why the path (or a particular road) is selected, and easy to use graphical interfaces to natvigate on all the options. With the current active development of Large Language Model and Chatbot AI, reasoning on routing options and visual images has become feasible.

Are you interested to build a Smart Route Natvigation Recommendation System with us using the latest AI technology?