How well do drivers adapt to remote operation? Learning from remote drivers with on-road experience

Remote driving is a promising strategy for helping Autonomous Vehicles (AVs) navigate many environments where edge cases may otherwise limit their abilities. For some companies, remote driving is an alternative to AVs altogether. Much remote driving research has taken place in simulated or controlled environments with novice operators, leaving the needs of operators with real-world experience under-explored. This research aims to understand if experienced operators are satisfied with current production remote driving systems, if they adapt to the difference in control, and how their job satisfaction compares to in-vehicle safety driving. This paper briefly overviews recent remote driving research and presents results from a questionnaire and a semi-structured interview with experienced teleoperators. The findings indicate that operators do adjust to the new domain, but latency and network reliability remain a challenge. Likewise, standardised training practices for operators are found to be lacking

Haptic data reduction through dynamic perceptual analysis and event-based communication

This research presents an adjustable and flexible framework for haptic data compression and communication that can be used in a robotic teleoperation session. The framework contains a customized event-driven transmission control protocol, several dynamically adaptive perceptual and prediction methods for haptic sample reduction, and last but not the least, an architecture for the data flow