密集高速公路交通中基于分布式模型预测控制的交互感知轨迹预测与规划
Interaction-Aware Trajectory Prediction and Planning in Dense Highway Traffic using Distributed Model Predictive Control
AI总结：
针对密集交通中自动驾驶车辆的最优轨迹规划问题，提出一种结合分布式模型预测控制、耦合多智能体环境下轨迹预测与规划的新型框架，经仿真验证，其局部最优解优于解耦的预测规划方法。
摘要：
本文研究了在车辆间交互紧密的密集交通中运行的自动驾驶车辆（AV）的最优轨迹规划问题。为解决该问题，我们提出了一种新型框架，采用分布式模型预测控制，在多智能体环境中将轨迹预测与规划相结合。我们通过仿真对该框架进行了验证，仿真中使用了基于非线性模型预测控制的轨迹规划器。我们分析了框架在不同预测误差下的性能与收敛性。结果表明，与解耦的预测和规划方法相比，所得到的局部最优解得到了提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
