Cooperative decentralized decision making for conflict resolution among autonomous agents

Autonomous agents plan their paths through known and unknown environments to reach their goals. When multiple autonomous agents share the same area, conflict situations may occur that need to be solved. We present a decentralized decision making algorithm to solve conflicts among autonomous agents. It is based on two main ideas: First, we introduce an innovative operationalization of cooperative behavior which allows to determine whether a behavior is cooperative by computing the total utility and comparing it to a reference utility. Second, we use motion primitives as a representation of available maneuvers obeying individual and environmental restrictions. The decentralized decision making algorithm is based on communication among the autonomous agents to find an optimal maneuver combination. Simulations show that our algorithm is applicable to different highway traffic scenarios of two automated vehicles. We use a mean-square acceleration as an individual cost function and show that our intelligent controller leads to cooperative solutions.