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Researcher profile

Lui Sha

Lui Sha contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.SYSystems and ControlMachine LearningMultiagent Systems

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Published work

5 published item(s)

preprint2026arXiv

Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems

Autonomous systems increasingly rely on machine-learning (ML) components for safety-critical tasks such as perception and control in autonomous vehicles (AVs). While ML enables essential capabilities, it inevitably exhibits long-tail faults that make it unsuitable for safety-critical tasks. Runtime assurance (RTA) mitigates this issue by pairing ML components with verifiable safety monitors, e.g., Control Simplex and Perception Simplex architectures. However, the limited performance of safety monitors remains a major bottleneck. The Synergistic Simplex (SS) architecture improves system performance by enabling bidirectional integration between ML components and safety monitors while preserving formal safety guarantees. The key innovation here is allowing safety monitors to use ML outputs, which is typically prohibited in RTA systems. We formally derive conditions under which this integration preserves safety and demonstrate the performance benefits. We present the design, analysis, and evaluation of SS for AV obstacle detection.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SL1-Simplex: Safe Velocity Regulation of Self-Driving Vehicles in Dynamic and Unforeseen Environments

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.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Finite-Time Model Inference From A Single Noisy Trajectory

This paper proposes a novel model inference procedure to identify system matrix from a single noisy trajectory over a finite-time interval. The proposed inference procedure comprises an observation data processor, a redundant data processor and an ordinary least-square estimator, wherein the data processors mitigate the influence of observation noise on inference error. We first systematically investigate the comparisons with naive least-square-regression based model inference and uncover that 1) the same observation data has identical influence on the feasibility of the proposed and the naive model inferences, 2) the naive model inference uses all of the redundant data, while the proposed model inference optimally uses the basis and the redundant data. We then study the sample complexity of the proposed model inference in the presence of observation noise, which leads to the dependence of the processed bias in the observed system trajectory on time and coordinates. Particularly, we derive the sample-complexity upper bound (on the number of observations sufficient to infer a model with prescribed levels of accuracy and confidence) and the sample-complexity lower bound (high-probability lower bound on model error). Finally, the proposed model inference is numerically validated and analyzed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Safety Constrained Control Framework for UAVs in GPS Denied Environment

Unmanned aerial vehicles (UAVs) suffer from sensor drifts in GPS denied environments, which can lead to potentially dangerous situations. To avoid intolerable sensor drifts in the presence of GPS spoofing attacks, we propose a safety constrained control framework that adapts the UAV at a path re-planning level to support resilient state estimation against GPS spoofing attacks. The attack detector is used to detect GPS spoofing attacks based on the resilient state estimation and provides a switching criterion between the robust control mode and emergency control mode. To quantify the safety margin, we introduce the escape time which is defined as a safe time under which the state estimation error remains within a tolerable error with designated confidence. An attacker location tracker (ALT) is developed to track the location of the attacker and estimate the output power of the spoofing device by the unscented Kalman filter (UKF) with sliding window outputs. Using the estimates from ALT, an escape controller (ESC) is designed based on the constrained model predictive controller (MPC) such that the UAV escapes from the effective range of the spoofing device within the escape time.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Safety Constrained Multi-UAV Time Coordination: A Bi-level Control Framework in GPS Denied Environment

Unmanned aerial vehicles (UAVs) suffer from sensor drifts in GPS denied environments, which can cause safety issues. To avoid intolerable sensor drifts while completing the time-critical coordination task for multi-UAV systems, we propose a safety constrained bi-level control framework. The first level is the time-critical coordination level that achieves a consensus of coordination states and provides a virtual target which is a function of the coordination state. The second level is the safety-critical control level that is designed to follow the virtual target while adapting the attacked UAV(s) at a path re-planning level to support resilient state estimation. In particular, the time-critical coordination level framework generates the desired speed and position profile of the virtual target based on the multi-UAV cooperative mission by the proposed consensus protocol algorithm. The safety-critical control level is able to make each UAV follow its assigned path while detecting the attacks, estimating the state resiliently, and driving the UAV(s) outside the effective range of the spoofing device within the escape time. The numerical simulations of a three-UAV system demonstrate the effectiveness of the proposed safety constrained bi-level control framework.

0 citations0 reviewsNo code linkedOpen access