A Helping Human Hand: Relevant Scenarios for the Remote Operation of Highly Automated Vehicles in Public Transport

Abstract

Remote operation bears the potential to roll out highly automated vehicles (AVs, SAE Level 4) more safely and quickly. Moreover, legal regulations on highly automated driving, e.g., the current law on highly automated driving (SAE Level 4) in Germany, permit a remote supervisor to monitor and intervene in driving operations remotely in lieu of a safety operator on board AVs. In order to derive requirements for safe and effective remote driving and remote assistance of AVs and to create suitable human-centered design solutions for human-machine interfaces (HMIs) that serve this purpose, a set of 74 core scenarios that are likely to occur in public transport AVs under remote operation was compiled. The scenarios were collected in several projects on the remote operation of AVs across a variety of contexts including interviews with and observations of control center staff, video analyses from naturalistic road events, and interviews with safety operators of AVs. A hierarchical system that is based on interactions of central actors was used to structure the scenarios. The set explicates relevant cases in remote operation, which may help improve workplaces for remote operation both by combatting human factors issues such as distraction and fatigue, and by boosting usability, user experience, trust, and acceptance. As the catalogue of scenarios is not exhaustive, scenarios may be added as knowledge of the remote operation of AVs progresses. Further research is needed to validate and adapt the scenarios to specific conceptualizations of remote operations.

Investigating Levels of Remote Operation in High-Level On-Road Autonomous Vehicles using Operator Sequence Diagrams

“…The open format of submission to the DMV means that there is little standardisation to the reporting so it is di cult to produce in depth analysis, however the results can still be used to suggest potential reasons for remote operator involvement and these can be cross-referenced with those suggested in research literature. Kettwich et al (2022) compiled a taxonomy of scenarios for remote operation sourced from several AV contexts including observations of control centre staff working in public transport and follow-up interviews, video analysis of road events, and interviews with existing safety operators of AV's (Kettwich, et al, 2022). This taxonomy is split into 'use case clusters' each with several 'sub-use' cases.…”

“…This taxonomy of scenarios is not considered by the authors to be exhaustive, and it is noted in the discussion of the paper that the levels of depth and detail are varying, affecting the balance of use cases across clusters. Further, relating to the work presented as part of the current study, the taxonomy only differentiates between remote driving and remote assistance as options for the RO, and in some cases does not consider involvement of the RO at all, meaning not all scenarios presented in the taxonomy may be appropriate (Kettwich, et al, 2022). Nevertheless, the Kettwich et al taxonomy was useful to cross reference all of the scenarios identi ed in this work (through other academic literature and the DMV reports).…”

“…With these predictions in market growth, accompanying changes are being made to AV regulations and laws around the world. For example, an update to the German Road Tra c Act was recently announced, allowing SAE Level 4 vehicles on roads (Kettwich, et al, 2022) and in France fully autonomous vehicles can now be allowed on roads for testing (Connected Automated Driving.eu, 2021). As part of the Queens Speech in early May 2022, attention was brought to the planned changes to the Highway Code in the UK to help the integration of AV's onto UK roads including clarifying at what point a driver must be ready to resume control (Centre for Connected and Automated Vehicles, 2022).…”

Assessing Training Methods for Advanced Driver Assistance Systems and Autonomous Vehicle Functions: Impact on User Mental Models and Performance

“…Over 90% of these accidents have been identified by the National Road Safety Partnership Program (NRSPP) in Australia as being caused by human factors, highlighting a substantial behavioural dimension to the prevailing road safety issues [4]. The primary contributors to road accidents include speeding [5], distracted driving, intoxicated driving [6], and driver fatigue [6,7]. To address these challenges, Advanced Driver Assistance Systems (ADAS) such as Adaptive Cruise Control (ACC) have been developed.…”