An Authority Allocation Strategy for Shared Control in Human-Machine Cooperative Driving via Lane-Based Probabilistic Collision Risk Assessment

An effective shared control strategy plays a crucial role in assisting drivers during hazardous situations in human-machine cooperative driving. This study introduces an author-ity allocation strategy for shared control based on collision risk assessment in a long-term maneuver. Initially, maneuvers are categorized by lanes, which can properly represent lane-based driving characteristics in real-world driving conditions. Subsequently, multiple lane models are built to combine with interactive multiple models to compute target lane probabilities. The target lane probabilities indicate the likelihood of a vehi-cle moving toward or remaining in each lane, determined by its lateral position in curvilinear coordinates. Finally, collision risk is evaluated through the integration of model probability distribution of lanes and artificial potential field value between a pair of predicted trajectories. Intuitively, the driver's authority is directly correlated with the collision risk, so authorities between the driver and the machine are determined by the collision risk through a piecewise proportional function. The effectiveness of the proposed algorithm is verified using the Carla simulator in a cut-in scenario.