Connected and autonomous vehicle control for on-ramp merging scenario considering fuzzy and uncertain factors

In the process of constructing a merging model for an on-ramp merging scenario in mixed traffic, the fuzzy and uncertain factors should be taken into account in order to avoid collisions and improve efficiency. This study proposes an optimisation scheme based on a soft set for a connected and autonomous vehicle (CAV) control strategy in the on-ramp merging scenario, in particular considering that fuzzy and uncertain factors of the environment and the human-driven vehicle affect the acceleration input of the CAVs. The soft set is applied in this study to quantify the impact of some fuzzy and uncertain factors and relationships, such as the distance between the vehicles and the distance from the end of the merging point. The relationships between these factors and the CAV’s desired acceleration are matched by the membership functions and the rules. Numerical simulations are conducted for different traffic demand levels and CAV penetrations to verify the effectiveness of the proposed method. The results indicate that the improved method increases the flow rate by 3.4–7.1% and the average speed by 17.59–22.63% compared to the baseline, and the collision rate is reduced to zero.