论文标题
SL1-simplex：在动态和不可预见的环境中自动驾驶车辆的安全速度调节
SL1-Simplex: Safe Velocity Regulation of Self-Driving Vehicles in Dynamic and Unforeseen Environments
论文作者
论文摘要
本文提出了通过模型切换和模型学习的单纯架构的新型扩展，以实现动态和不可预见的环境中自动驾驶车辆的安全速度调节。为了保证自动驾驶汽车的可靠性，$ \ Mathcal {l} _ {1} $自适应控制器可以补偿单纯形架构作为验证的安全控制器来容忍同意软件和物理故障的验证的安全控制器。同时，通过整合牵引力控制系统和防锁制动系统，将安全开关控制器合并到单纯速度中，以进行安全速度调节。具体而言，车辆的角度和纵向速度渐近地跟踪了随着驾驶环境而变化的引用，而车轮滑动则仅限于安全信封以防止滑动和滑动。由于车辆动态对驾驶环境的高度依赖性，因此提议的单纯形利用有限的时间模型学习来及时学习和更新$ \ Mathcal {l} _ {1} $自适应控制器的车辆模型，当时在安全膜或不确定的测量阈值中出现在不确定的环境中时发生在不确定的环境中。最后，通过自动平台验证了建议的单纯形架构对安全速度调节的有效性。
This paper proposes a novel extension of the Simplex architecture with model switching and model learning to achieve safe velocity regulation of self-driving vehicles in dynamic and unforeseen environments. To guarantee the reliability of autonomous vehicles, an $\mathcal{L}_{1}$ adaptive controller that compensates for uncertainties and disturbances is employed by the Simplex architecture as a verified safe controller to tolerate concurrent software and physical failures. Meanwhile, safe switching controller is incorporated into the Simplex for safe velocity regulation through the integration of the traction control system and anti-lock braking system. Specifically, the vehicle's angular and longitudinal velocities asymptotically track the provided references that vary with driving environments, while the wheel slips are restricted to safety envelopes to prevent slipping and sliding. Due to the high dependence of vehicle dynamics on the driving environments, the proposed Simplex leverages the finite-time model learning to timely learn and update the vehicle model for $\mathcal{L}_{1}$ adaptive controller, when any deviation from the safety envelope or the uncertainty measurement threshold occurs in the unforeseen driving environments. Finally, the effectiveness of the proposed Simplex architecture for safe velocity regulation is validated by the AutoRally platform.
