With the advancement of affordable self-driving vehicles using complicated nonlinear optimization but limited computation resources, computation time becomes a matter of concern. Other factors such as actuator dynamics and actuator command processing cost also unavoidably cause delays. In high-speed scenarios, these delays are critical to the safety of a vehicle. Recent works consider these delays individually, but none unifies them all in the context of autonomous driving. Moreover, recent works inappropriately consider computation time as a constant or a large upper bound, which makes the control either less responsive or over-conservative. To deal with all these delays, we present a unified framework by 1) modeling actuation dynamics, 2) using robust tube model predictive control, 3) using a novel adaptive Kalman filter without assuminga known process model and noise covariance, which makes the controller safe while minimizing conservativeness. On onehand, our approach can serve as a standalone controller; on theother hand, our approach provides a safety guard for a high-level controller, which assumes no delay. This can be used for compensating the sim-to-real gap when deploying a black-box learning-enabled controller trained in a simplistic environment without considering delays for practical vehicle systems.

翻译：随着基于复杂非线性优化但计算资源有限的平价自动驾驶车辆的进步，计算时间成为关注焦点。执行器动力学及执行器指令处理成本等其他因素也不可避免地导致延迟。在高速场景中，这些延迟对车辆安全至关重要。近期研究虽分别考虑这些延迟，但未能在自动驾驶背景下统一处理。此外，近期研究不适当地将计算时间视为常数或较大的上限，导致控制响应迟钝或过度保守。为应对所有延迟，我们提出统一框架：1) 建模执行动力学，2) 采用鲁棒管模型预测控制，3) 使用无需已知过程模型和噪声协方差的新型自适应卡尔曼滤波器，在最小化保守性的同时确保控制器安全。一方面，该方法可独立作为控制器使用；另一方面，它为假设无延迟的高层控制器提供安全防护。当在未考虑延迟的简化环境中部署黑盒学习型控制器以应用于实际车辆系统时，该方法可补偿仿真到现实的差距。