Park4U Mate: Context-Aware Digital Assistant for Personalized Autonomous Parking

TL;DR

This paper introduces Park4U Mate, a context-aware voice-based digital assistant for autonomous parking that adapts to interior and exterior vehicle contexts, demonstrating effective decision-making and user-friendly interaction.

Contribution

The paper presents a novel voice-based assistant integrated with a smart parking strategy that considers vehicle context, enhancing autonomous parking and human-machine interaction.

Findings

The optimization algorithm efficiently determines optimal parking strategies.

Users find voice interaction clear and effective for autonomous parking.

Multi-modal interaction is preferred over voice-only methods.

Abstract

People park their vehicle depending on interior and exterior contexts. They do it naturally, even unconsciously. For instance, with a baby seat on the rear, the driver might leave more space on one side to be able to get the baby out easily; or when grocery shopping, s/he may position the vehicle to remain the trunk accessible. Autonomous vehicles are becoming technically effective at driving from A to B and parking in a proper spot, with a default way. However, in order to satisfy users' expectations and to become trustworthy, they will also need to park or make a temporary stop, appropriate to the given situation. In addition, users want to understand better the capabilities of their driving assistance features, such as automated parking systems. A voice-based interface can help with this and even ease the adoption of these features. Therefore, we developed a voice-based in-car assistant (Park4U Mate), that is aware of interior and exterior contexts (thanks to a variety of sensors), and that is able to park autonomously in a smart way (with a constraints minimization strategy). The solution was demonstrated to thirty-five users in test-drives and their feedback was collected on the system's decision-making capability as well as on the human-machine-interaction. The results show that: (1) the proposed optimization algorithm is efficient at deciding the best parking strategy; hence, autonomous vehicles can adopt it; (2) a voice-based digital assistant for autonomous parking is perceived as a clear and effective interaction method. However, the interaction speed remained the most important criterion for users. In addition, they clearly wish not to be limited on only voice-interaction, to use the automated parking function and rather appreciate a multi-modal interaction.