An allocating slot method for autonomous valet parking vehicles with improved ant colony algorithm

Abstract

A great deal of energy is wasted and lots of traffic jams occur since drivers repeated seeking the available lots to parking vehicle as lacking of information exchange between drivers and the vehicle park spot. In order to addressing the drawbacks of information islands, an improved ant colony algorithm (IACA) is proposed to allocate parking slots for autonomous valet parking vehicles. Compressed global pheromone and defined thresholds are the major improvements that avoids the reduction of global optimization ability and population diversity as falling into local optimal solutions due to the positive feedback effect in the late stage. In order to verify the effectiveness of the presented algorithm, two common test scenarios are designed and three comparison algorithms are developed, including first coming first serve (FCFS), normal ant colony algorithm and immune algorithm. The results show that all the four algorithms could complete the parking slots allocations. However, the cost of proposed algorithm is 13.2% lower than that of FCFS and the convergence speed is 94% higher than that of the normal ant colony algorithm, which means the proposed algorithm has strong generality and better global search ability.