# Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances

## TL;DR

This paper develops and evaluates hierarchical neural models for understanding passenger commands in autonomous vehicles, achieving high accuracy in intent detection and slot filling from multi-modal dialogue data.

## Contribution

It introduces hierarchical joint models for intent detection and slot filling in AV passenger interactions, outperforming existing baselines on a new multi-modal dataset.

## Key findings

- F1 score of 0.91 for intent detection
- F1 score of 0.96 for slot filling
- Effective handling of noisy ASR outputs

## Abstract

Understanding passenger intents and extracting relevant slots are important building blocks towards developing contextual dialogue systems for natural interactions in autonomous vehicles (AV). In this work, we explored AMIE (Automated-vehicle Multi-modal In-cabin Experience), the in-cabin agent responsible for handling certain passenger-vehicle interactions. When the passengers give instructions to AMIE, the agent should parse such commands properly and trigger the appropriate functionality of the AV system. In our current explorations, we focused on AMIE scenarios describing usages around setting or changing the destination and route, updating driving behavior or speed, finishing the trip and other use-cases to support various natural commands. We collected a multi-modal in-cabin dataset with multi-turn dialogues between the passengers and AMIE using a Wizard-of-Oz scheme via a realistic scavenger hunt game activity. After exploring various recent Recurrent Neural Networks (RNN) based techniques, we introduced our own hierarchical joint models to recognize passenger intents along with relevant slots associated with the action to be performed in AV scenarios. Our experimental results outperformed certain competitive baselines and achieved overall F1 scores of 0.91 for utterance-level intent detection and 0.96 for slot filling tasks. In addition, we conducted initial speech-to-text explorations by comparing intent/slot models trained and tested on human transcriptions versus noisy Automatic Speech Recognition (ASR) outputs. Finally, we compared the results with single passenger rides versus the rides with multiple passengers.