Model-based Approach for Building Trust in Autonomous Drones through Digital Twins

The 21st century is the age of automation. The automotive industry is converging towards deployment of complete automation by 2030. But are humans ready for it, or will they be hesitant to adopt it due to the lack of trust? To safeguard future autonomous mobility, robust run-time trust assurance and assessment is necessary. One strategy that is so far under-explored is rooted in involving the intelligence inside the autonomous agents, which could be directed towards detection of trust-breaking behaviour in other agents so that problematic vehicles are reported before they can engage in harmful behaviour.To support the progress in this direction, we propose a peer-to-peer model-based run-time trust assessment method, employing the model in terms of a Digital Twin for an autonomous vehicle (drone in our case) to ensure the trusted execution of intelligent agents. In this research, we examine the role of the Digital Twin in the trust-building scenario, and propose the characteristics of the intended Digital Twin model. To illustrate the approach, we present a case study of an autonomous-drone food delivery system and use formal approaches such as Petri Nets and Finite State Machines (FSM) to evaluate the scenario and demonstrate how trust could be built among autonomous drones or other vehicles.