Intelligent Intersection Coordination and Trajectory Optimization for Autonomous Vehicles

Abstract

Since multiple roads merge at intersections, proper coordination for vehicles is of great importance for modern intelligent transportation systems (ITS). In this paper, we try to smartly integrate the infrastructure and vehicle-based planners, to achieve feasible and efficient solutions. In detail, the vehicle reference trajectories can be firstly achieved by the high-level infrastructure-based coordination, which can be formulated as standard quadratic programming (QP) and mixed integer programming (MIP) problems. Due to the possible occurrence of obstacles such as pedestrians, the vehicles are also required to perform low-level ego trajectory optimization based on local observations, which are essentially dynamic programming (DP) and QP problems. Numerical results show that the proposed framework can effectively solve many opening problems in vehicle coordination, such as obstacle avoidance and deadlocks among vehicles.