"What's Happening"- A Human-centered Multimodal Interpreter Explaining the Actions of Autonomous Vehicles

TL;DR

This paper presents a human-centered multimodal interpreter system for autonomous vehicles that uses visual, textual, and auditory feedback to enhance passenger trust and understanding, demonstrating significant trust improvements in user studies.

Contribution

It introduces a novel multimodal interpreter system that integrates human preferences and provides real-time explanations to improve trust in autonomous vehicles.

Findings

Trust increased by over 8% with the system.

Passenger trust in ordinary environments increased by up to 30%.

System effectively enhances transparency and perceived reliability.

Abstract

Public distrust of self-driving cars is growing. Studies emphasize the need for interpreting the behavior of these vehicles to passengers to promote trust in autonomous systems. Interpreters can enhance trust by improving transparency and reducing perceived risk. However, current solutions often lack a human-centric approach to integrating multimodal interpretations. This paper introduces a novel Human-centered Multimodal Interpreter (HMI) system that leverages human preferences to provide visual, textual, and auditory feedback. The system combines a visual interface with Bird's Eye View (BEV), map, and text display, along with voice interaction using a fine-tuned large language model (LLM). Our user study, involving diverse participants, demonstrated that the HMI system significantly boosts passenger trust in AVs, increasing average trust levels by over 8%, with trust in ordinary environments rising by up to 30%. These results underscore the potential of the HMI system to improve the acceptance and reliability of autonomous vehicles by providing clear, real-time, and context-sensitive explanations of vehicle actions.