Autonomous Decision Making for High-Speed Vehicle in Interception Scenario via Individual Similarity Pigeon-Inspired Optimization

An autonomous interception framework is presented for intelligent high-speed vehicles (HSV) with matrix game theory, incorporating an enhanced pigeon-inspired optimization. Considering attackers with equal levels of intelligence, the matrix game is applied to describe the real-time one-to-one interception process. Subsequently, a maneuver library based on a simplified overload model is introduced to predict the motion tendency of a 6 -degree-of-freedom (6-DOF) model. In this regard, individual similarity pigeon-inspired optimization (ISPIO) is designed for optimal maneuvers. A series of experiments demonstrate the performance of the proposed framework in face-to-face interception and initial disadvantage scenarios, and analyze the influences of different optimization algorithms for both scenarios.