Demo: Towards a Conversational LLM-Based Voice Assistant for Transportation Applications

Conversational assistants based on large language models (LLMs) have spread widely across many domains, and the automotive industry is keen to follow suit. However, current LLMs lack sufficient understanding of geospatial data; in addition, timely information, such as weather and traffic conditions, is inaccessible to LLMs. In this demo, we present an in-car assistant capable of verbally communicating with the driver, and by utilizing external APIs, it can answer questions related to routing, finding points of interest, and is aware of the local weather and traffic conditions. The assistant, including a customizable speech synthesizer, is accessible through a graphical user interface that facilitates experimentation by simulating the change in time, origin, destination, and location of the car.