Lane change decision-making is one of the current opening challenges of automated and connected vehicles. Due to highly dynamic and complex traffic situations, multi-objective decision-making considering vehicle safety and riding efficiency is much more challenging. Therefore, this paper proposes a novel multi-objective predictive optimization-based lane change decision making method, which consists of dynamic matrix modeling and resolving of multi-objective predictive optimization problem. First, the matrix model of traffic flow is established based on the big data information from connected vehicles.
Then, dynamic models representing lane change safety and riding efficiency are designed. The predictive optimization problem with constraints can be solved so that the optimal lane change decision is provided. Experimental results illustrate that the proposed method performs better and can improve vehicle safety and riding efficiency of automated and connected vehicles.