SL1-Simplex：自动驾驶车辆在动态和不可预见环境中的安全速度调节
SL1-Simplex: Safe Velocity Regulation of Self-Driving Vehicles in Dynamic and Unforeseen Environments
摘要：
本文提出了一种具有模型切换和模型学习的Simplex架构的新扩展，以实现自动驾驶车辆在动态和不可预见的环境中的安全速度调节。为了保证自动驾驶汽车的可靠性，一个$\mathcal{L}_{1}$自适应控制器，补偿不确定性和干扰是由单纯形架构作为一个验证的安全控制器，容忍并发的软件和物理故障。同时，通过牵引控制系统与防抱死制动系统的集成，将安全切换控制器集成到Simplex中，实现安全调速。具体地，车辆的角速度和纵向速度渐近地跟踪随驾驶环境而变化的所提供的参考，而车轮滑移被限制在安全包络线内以防止打滑和滑动。由于车辆动力学对驾驶环境的高度依赖性，所提出的Simplex利用有限时间模型学习来及时学习和更新车辆模型的$\mathcal{L}_{1}$自适应控制器，当任何偏离安全包络线或不确定性测量阈值发生在不可预见的驾驶环境。最后，在AutoRally平台上验证了所提出的Simplex结构在安全速度调节中的有效性。
英文摘要：
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.
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