In this paper we treat optimal trajectory planning for an autonomous vehicle (AV) operating in dense traffic, where vehicles closely interact with each other. To tackle this problem, we present a novel framework that couples trajectory prediction and planning in multi-agent environments, using distributed model predictive control. A demonstration of our framework is presented in simulation, employing a trajectory planner using non-linear model predictive control. We analyze performance and convergence of our framework, subject to different prediction errors. The results indicate that the obtained locally optimal solutions are improved, compared with decoupled prediction and planning.

翻译：本文针对在密集交通环境中运行的自动驾驶车辆（AV）的最优轨迹规划问题展开研究，其中车辆之间存在密切的交互作用。为解决这一问题，我们提出了一种基于分布式模型预测控制的新型框架，将多智能体环境中的轨迹预测与规划过程相耦合。通过采用非线性模型预测控制的轨迹规划器，我们在仿真中展示了该框架的性能。我们分析了不同预测误差下框架的收敛性与性能表现。结果表明，与解耦的预测-规划方法相比，本文方法所获得的局部最优解有所改善。