A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)

The shared autonomy framework has become an option with great potential in the field of autonomous vehicles. Human and machine control decisions typically demonstrate strengths in different scenarios. As a result, the robustness of systems can be enhanced by the collaboration between humans and autonomy. A shared autonomy architecture that takes into account both human and environmental factors was proposed in this work. The authority distribution between the human operator and the autonomy algorithm was determined by the Shared Autonomy Arbiter (SAB). Designed with a two-tier structure, the SAB incorporated a policy-level decision module, as well as a numerical-level arbitration tuning module. A fuzzy inference system (FIS) was incorporated to enhance the noise tolerance of the policy selection module. Furthermore, the human factor was taken into account by applying a projection to the users’ control input. The human operator’s control decision was projected by the Adaptive Personalized Control System (APeCS) to accommodate the skill levels and habits of various users. By incorporating a broad set of factors, this framework is suitable for diverse applications that require robustness in complex environments. Two case studies were included in this work to demonstrate its effectiveness. The first presented a concept design illustrating the application of the proposed architecture on autonomous vehicles operating in varied environments. The second showed that the proposed architecture can serve as a robust testbed by taking advantage of the authority modulating mechanism. By connecting a system under assessment and an established autonomy algorithm to the SAB, the new system can be tested robustly and safely through the flexible authority distribution.