Conversational Intent Understanding for Passengers in Autonomous Vehicles
This work addresses the need for contextual dialogue systems in autonomous vehicles to handle passenger interactions, representing an incremental improvement using existing methods on new data.
The paper tackled the problem of understanding passenger intents and extracting slots for dialogue systems in autonomous vehicles, achieving an F1-score of 0.91 on intent recognition and 0.96 on slot extraction.
Understanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehicle-passenger interactions in autonomous vehicles (AV). When the passengers give instructions to AMIE (Automated-vehicle Multimodal In-cabin Experience), the agent should parse such commands properly and trigger the appropriate functionality of the AV system. In our AMIE scenarios, we describe usages and support various natural commands for interacting with the vehicle. We collected a multimodal in-cabin data-set with multi-turn dialogues between the passengers and AMIE using a Wizard-of-Oz scheme. We explored various recent Recurrent Neural Networks (RNN) based techniques and built our own hierarchical models to recognize passenger intents along with relevant slots associated with the action to be performed in AV scenarios. Our experimental results achieved F1-score of 0.91 on utterance-level intent recognition and 0.96 on slot extraction models.