IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant.

Area of Science:

• Human-Computer Interaction
• Artificial Intelligence
• Natural Language Processing

Background:

• Task-oriented dialogue systems, crucial for in-vehicle assistants, typically rely on datasets like KVRET for training and evaluation.
• Assessing dialogue agent performance necessitates a human user simulator when real participants are unavailable.
• Existing methods lack a comprehensive simulator for evaluating the human-user side of in-vehicle dialogue systems.

Purpose of the Study:

• To introduce a novel end-to-end simulator that accurately replicates human driver behavior and interaction with in-vehicle assistants.
• To enable effective assessment of dialogue agent systems by providing a realistic human user simulation.
• To facilitate controlled and diverse conversational testing by allowing adjustable driver profiles.

Main Methods:

• Development of an end-to-end driver simulator capable of understanding and responding to in-vehicle assistant utterances.
• Implementation of a system that allows interaction with an in-vehicle assistant, simulating a real driver.
• Utilizing configurable driver profiles to control the diversity and nature of simulated conversations.

Main Results:

• The proposed driver simulator demonstrated superior performance across all evaluated tasks compared to existing models.
• The simulator successfully mimics human-like interaction, enabling realistic testing of dialogue systems.
• Experimental results confirm the effectiveness and efficiency of the end-to-end simulator.

Conclusions:

• The developed driver simulator provides a robust solution for evaluating in-vehicle dialogue systems without real users.
• This methodology enhances the assessment of AI-driven assistants by offering a controllable and realistic simulation environment.
• The simulator's ability to manage conversational diversity through driver profiles offers significant advantages for system development.