Model Predictive Control lacks the ability to escape local minima in nonconvex problems. Furthermore, in fast-changing, uncertain environments, the conventional warmstart, using the optimal trajectory from the last timestep, often falls short of providing an adequately close initial guess for the current optimal trajectory. This can potentially result in convergence failures and safety issues. Therefore, this paper proposes a framework for learning-aided warmstarts of Model Predictive Control algorithms. Our method leverages a neural network based multimodal predictor to generate multiple trajectory proposals for the autonomous vehicle, which are further refined by a sampling-based technique. This combined approach enables us to identify multiple distinct local minima and provide an improved initial guess. We validate our approach with Monte Carlo simulations of traffic scenarios.

翻译：模型预测控制在处理非凸问题时难以逃离局部最优解。此外，在快速变化的不确定环境中，采用上一时刻最优轨迹的传统热启动方法，往往无法为当前最优轨迹提供足够接近的初始猜测，这可能导致收敛失败与安全问题。为此，本文提出一种面向模型预测控制算法的学习辅助热启动框架。该方法利用基于神经网络的模态预测器为自动驾驶车辆生成多条轨迹候选方案，并通过基于采样的技术进行进一步优化。这种组合策略使我们能够识别多个不同的局部最优解，并提供更优的初始猜测。我们通过蒙特卡洛交通场景仿真验证了该方法的有效性。