Uncertainties in the environment and behavior model inaccuracies compromise the state estimation of a dynamic obstacle and its trajectory predictions, introducing biases in estimation and shifts in predictive distributions. Addressing these challenges is crucial to safely control an autonomous system. In this paper, we propose a novel algorithm SIED-MPC, which synergistically integrates Simultaneous State and Input Estimation (SSIE) and Distributionally Robust Model Predictive Control (DR-MPC) using model confidence evaluation. The SSIE process produces unbiased state estimates and optimal input gap estimates to assess the confidence of the behavior model, defining the ambiguity radius for DR-MPC to handle predictive distribution shifts. This systematic confidence evaluation leads to producing safe inputs with an adequate level of conservatism. Our algorithm demonstrated a reduced collision rate in autonomous driving simulations through improved state estimation, with a 54% shorter average computation time.

翻译：环境中的不确定性以及行为模型的不准确性会损害动态障碍物的状态估计及其轨迹预测，导致估计偏差和预测分布偏移。解决这些挑战对于安全控制自主系统至关重要。本文提出一种新颖算法SIED-MPC，该算法通过模型置信度评估，协同整合了同步状态与输入估计（SSIE）和分布鲁棒模型预测控制（DR-MPC）。SSIE过程生成无偏状态估计和最优输入间隙估计，用以评估行为模型的置信度，从而为DR-MPC定义处理预测分布偏移的模糊半径。这种系统化的置信度评估能够以适当的保守水平生成安全输入。我们的算法通过改进状态估计，在自动驾驶仿真中实现了碰撞率的降低，且平均计算时间缩短了54%。