We present Contingency Model Predictive Control (CMPC), a motion planning and control framework that optimizes performance objectives while simultaneously maintaining a contingency plan -- an alternate trajectory that avoids a potential hazard. By preserving the existence of a feasible avoidance trajectory, CMPC anticipates emergency and keeps the controlled system in a safe state that is selectively robust to the identified hazard. We accomplish this by adding an additional prediction horizon in parallel to the typical Model Predictive Control (MPC) horizon. This extra horizon is constrained to guarantee safety from the contingent threat and is coupled to the nominal horizon at its first command. Thus, the two horizons negotiate to compute commands that are both optimized for performance and robust to the contingent event. This article presents a linear formulation for CMPC, illustrates its key features on a toy problem, and then demonstrates its efficacy experimentally on a full-size automated road vehicle that encounters a realistic pop-out obstacle. Contingency MPC approaches potential emergencies with safe, intuitive, and interpretable behavior that balances conservatism with incentive for high performance operation.

翻译：我们提出应急模式预测控制(CMPC),这是一个优化行动模式预测和控制框架,它既能优化绩效目标,又能同时维持应急计划 -- -- 一种避免潜在危险的替代轨道。通过保持可行的避免轨道的存在,CMPC预计到紧急情况,并将受控制的系统保持在安全状态,有选择地对确定的危险保持稳健。我们通过在典型的模型预测控制(MPC)前景的同时增加一个额外的预测地平线来实现这一目标。这一额外地平线受限制,以保障特遣队不受威胁的安全,并与其第一命令的名义地平线相配合。因此,两个地平线进行谈判,以计算既适合绩效又对应急事件具有稳健的指令。这一条款为CMPC提供了一条线性配方,说明了它对于一个小问题的关键特征,然后在遇到现实的流行障碍的全尺寸自动道路车辆上实验地展示了其功效。应急平台对潜在的紧急情况采取了安全、直觉和可解释的行为,既兼顾保守与高性操作的动力。