Multi-dimensional shared control and its application to human-machine shared driving

Shared control is a collaborative paradigm that integrates the strengths of human and intelligent machine. With the advancement of technology, there is an increasing demand for more sophisticated control of dynamic systems, leading to increasingly need for multi-dimensional control. Current research remains predominantly confined to single-dimensional frameworks, proving inadequate for addressing requirements and potentially leading to performance degradation or even task failure. Simply extending single-dimensional cannot resolve this issue, as it requires collaboration among multiple control variables. To address these challenges, this paper proposes a multi-dimensional authority allocation strategy based on dynamic authority boundaries. First, a method for constructing dynamic authority boundaries is proposed for factors that significantly affect shared control performance. Second, a multi-dimensional authority optimization problem is designed on the basis of dynamic authority boundaries to determine the optimal authority allocation. Finally, the specific application of the proposed dynamic authority boundaries and multi-dimensional authority allocation strategy is demonstrated in a human-machine shared driving scenario, and experiments conducted on a human-in-the-loop test platform validate the effectiveness of the framework. Moreover, the proposed framework is not only applicable to shared driving but can also be extended to other domains such as shared control mobile robots and shared control aerial vehicles.