Design, Implementation and Evaluation of an Immersive Teleoperation Interface for Human-Centered Autonomous Driving

Abstract

Highlights: What are the main findings? This paper presents a novel VR-based teleoperation interface designed to support remote human intervention in autonomous-vehicle operations. Operators receive synchronized visual, auditory, and haptic feedback to facilitate timely and informed interventions in simulated emergency scenarios, bridging the sensory and control gaps between remote operators and the operational environment of the vehicle. The participants reported a perceived sense of safety, control, and satisfaction while using the immersive teleoperation system. What is the implication of the main findings? Comprehensive multimodal feedback (such as visual, auditory, and haptic channels) is instrumental in supporting situational awareness without adding excessive cognitive load. The ability to interact directly in an immersive environment significantly contributes to enhancing trust in autonomous systems. As autonomous driving technologies advance, the need for human-in-the-loop systems becomes increasingly critical to ensure safety, adaptability, and public confidence. This paper presents the design and evaluation of a context-aware immersive teleoperation interface that integrates real-time simulation, virtual reality, and multimodal feedback to support remote interventions in emergency scenarios. Built on a modular ROS2 architecture, the system allows seamless transition between simulated and physical platforms, enabling safe and reproducible testing. The experimental results show a high task success rate and user satisfaction, highlighting the importance of intuitive controls, gesture recognition accuracy, and low-latency feedback. Our findings contribute to the understanding of human-robot interaction (HRI) in immersive teleoperation contexts and provide insights into the role of multisensory feedback and control modalities in building trust and situational awareness for remote operators. Ultimately, this approach is intended to support the broader acceptability of autonomous driving technologies by enhancing human supervision, control, and confidence.