# Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections
## 1 Introduction  
Significant part of the scientific research focuses on the challenges of control design of vehicles at non-signalized intersections. The basis of research is to determine the control design challenges related to the coordination of autonomous vehicles at intersection scenarios. In the US, there were more than 32 thousands fatal crashes on the roads in 2017 involving approximately 53 thousands vehicles and about 30 percentages of the human-driven vehicles participated in intersection accidents, see [1]. To decrease the number of fatal crashes related to human errors, it is attempted to analyze the use of autonomous vehicles in traffic systems. Traditionally the exact ordering at an intersection with traffic lights and signs is given by traffic rules followed by the driver of the vehicles. By the development of the automation in traffic systems, the coordination of autonomous vehicles at non-signalized intersection scenarios have become significant challenge of the control design. A recent survey considering non-signalized intersection strategies is made by [2]. Multi-agent systems are developed for the control of non-signalized intersections and approached in several papers, see [3, 4]. The proposed optimization algorithms are used to secure collision-free passage of vehicles through the intersection. The optimization problem for the control of autonomous vehicles crossing an intersection is reformulated as a convex program and solved by [5], while an optimal scheduling is proposed for the ordering problem as a Mixed-Integer Linear Program by [6]. For the determination of the crossing order of vehicles through the intersection Model Predictive Control (MPC) is used frequently to get an optimal solution by both centralized or decentralized controllers. An algorithm taking the expected entering time of vehicles into consideration is proposed to secure safe crossing of the intersection, see [7]. A decentralized MPC is used for the determination of time, while the negotiation between vehicles and the detection of the safety critical situations are guaranteed by vehicle-to-infrastructure (V2I) communication units. Two different decentralized MPC methods are presented to consider efficient fuel consumption, the requirement of comfort and to handle unexpected vehicle maneuvers during the passage through the intersection, see [8]. A MPC method is used for the assignment of priorities of automated vehicles to guarantee the optimal scheduling at the intersection by solving its control problem as a Mixed Integer Programming problem [9]. A bi-level, model predictive controller with coordination is presented by [10] to ensure the collision avoidance between automated vehicles at intersections. The optimization problem is solved by the use of a distributed Sequential Quadratic Programming method. In the control strategy optimal traveling speed is designed for each vehicle to secure safety constraints. An intersection crossing strategy by using a centralized controller is proposed by [11] formulating the control problem as a convex quadratic program, while a decentralized solution solving the local optimization problems of the sequence of the vehicles is developed by [12]. A centralized Optimal Control Problem (OPC) has been solved in a distributed fashion in [13] using a decomposition technique to create local OCPs with coupled constraints. The contribution of the paper is a MPC-based multicriteria control method for autonomous vehicles considering both time duration of the intersection crossing and the total energy consumption of all vehicles. For safety and comfort reasons, constraints as speed limits, predefined minimum and maximum accelerations are built in the control strategy. As the purpose of the research is to determine the ordering of vehicles, the requirements of minimum traveling time and efficient energy consumption are incorporated in the control design, see also [10; 14]. There are several benefits of the presented method compared to other strategies in the literature.