Ensuring operational control over automated vehicles is not trivial and failing to do so endangers the lives of road users. An integrated approach is necessary to ensure that all agents play their part, including drivers, occupants, vehicle designers and governments. A comprehensive approach to this problem is possible by considering Meaningful Human Control (MHC). In this chapter, an Integrated System Proximity framework and Operational Process Design approach are introduced to assist the development of Connected Automated Vehicles (CAVs) under the consideration of MHC. The framework includes an extension to a system approach, which also considers ways that MHC can be improved through either Implicit Proximal Updating or Explicit Distal Updating. The implementation is demonstrated using recent cases from practice. Finally, stakeholders are called upon to ensure that MHC processes are explicitly included in policy, regulations, and design processes so that CAVs advance in a responsible, safe and humanly agreeable fashion.

Modelling causal responsibility in multi-agent spatial interactions is crucial for safety and efficiency of interactions of humans with autonomous agents. However, current formal metrics and models of responsibility either lack grounding in ethical and philosophical concepts of responsibility, or cannot be applied to spatial interactions. In this work we propose a metric of causal responsibility which is tailored to multi-agent spatial interactions, for instance interactions in traffic. In such interactions, a given agent can, by reducing another agent's feasible action space, influence the latter. Therefore, we propose feasible action space reduction (FeAR) as a metric of causal responsibility among agents. Specifically, we look at ex-post causal responsibility for simultaneous actions. We propose the use of Moves de Rigueur (MdR) - a consistent set of prescribed actions for agents - to model the effect of norms on responsibility allocation. We apply the metric in a grid world simulation for spatial interactions and show how the actions, contexts, and norms affect the causal responsibility ascribed to agents. Finally, we demonstrate the application of this metric in complex multi-agent interactions. We argue that the FeAR metric is a step towards an interdisciplinary framework for quantifying responsibility that is needed to ensure safety and meaningful human control in human-AI systems.