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Lui Sha

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Systems and Control eess.SY Cryptography and Security cs.CY Machine Learning Multiagent Systems Operating Systems Software Engineering

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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.

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.

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.

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.

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.

preprint2016arXiv

A Pathophysiological Model-Driven Communication for Dynamic Distributed Medical Best Practice Guidance Systems

There is a great divide between rural and urban areas, particularly in medical emergency care. Although medical best practice guidelines exist in hospital handbooks, they are often lengthy and difficult to apply clinically. The challenges are exaggerated for doctors in rural areas and emergency medical technicians (EMT) during patient transport. In this paper, we propose the concept of distributed executable medical best practice guidance systems to assist adherence to best practice from the time that a patient first presents at a rural hospital, through diagnosis and ambulance transfer to arrival and treatment at a regional tertiary hospital center. We codify complex medical knowledge in the form of simplified distributed executable disease automata, from the thin automata at rural hospitals to the rich automata in the regional center hospitals. However, a main challenge is how to efficiently and safely synchronize distributed best practice models as the communication among medical facilities, devices, and professionals generates a large number of messages. This complex problem of patient diagnosis and transport from rural to center facility is also fraught with many uncertainties and changes resulting in a high degree of dynamism. To address this situation, we propose a pathophysiological model-driven message exchange communication architecture that ensures the real-time and dynamic requirements of synchronization among distributed emergency best-practice models are met in a reliable and safe manner. Taking the signs, symptoms, and progress of stroke patients transported across a geographically distributed healthcare network as the motivating use case, we implement our communication system and apply it to our developed best practice automata using laboratory simulations. Our proof-of-concept experiments shows there is potential for the use of our system in a wide variety of domains.

preprint2016arXiv

Model-Driven Design of Clinical Guidance Systems

Clinical guidance systems have been widely adopted to help medical staffs to avoid preventable medical errors such as delay in diagnosis, treatment or untended deviations from best practice guidelines. However, because patient condition changes rapidly and medical staffs are usually overloaded in acute care setting, how to ensure the correctness of the system reaction to those rapid changes and managing interaction with physicians remains challenging. In this paper, we propose a domain-specific model-driven design approach to address these challenges for designing clinical guidance systems. Firstly, we translate the relevant medical knowledge described in generic literature and different best practice guidelines into a set of executable and indirectly verifiable finite state machine models. We introduce an organ-centric paradigm to construct clinical models, and also develop a physician model to track physician-system interactions and deviations. Secondly, for verification of the compositional system model, we translate the model into timed automata, based on which, we formalize a set of clinical and system safety requirements as computation tree logic(CTL) formulas and use the UPPAAL model checking tool to formally verify those requirements. In this way, the correctness of the model can be mathematically proved. Finally, we can automatically generate the executable code from the verified model using the corresponding code generation tools for finite state machine. For evaluation, we apply the approach to the design of clinical guidance systems for cardiac arrest. The generated code can be deployed and interact with existing guidance systems.

preprint2015arXiv

Learning Execution Contexts from System Call Distributions for Intrusion Detection in Embedded Systems

Existing techniques used for intrusion detection do not fully utilize the intrinsic properties of embedded systems. In this paper, we propose a lightweight method for detecting anomalous executions using a distribution of system call frequencies. We use a cluster analysis to learn the legitimate execution contexts of embedded applications and then monitor them at run-time to capture abnormal executions. We also present an architectural framework with minor processor modifications to aid in this process. Our prototype shows that the proposed method can effectively detect anomalous executions without relying on sophisticated analyses or affecting the critical execution paths.

preprint2015arXiv

SINk: A Middleware for Synchronization of Heterogeneous Software Interfaces

Software is everywhere. The increasing requirement of supporting a wide variety of domains has rapidly increased the complexity of software systems, making them hard to maintain and the training process harder for end-users, which in turn ultimately demanded the development of user-friendly application software with simple interfaces that makes them easy, especially for non-professional personnel, to employ, and interact with. However, due to the lack of source code access for third-party software and the lack of non-graphical interfaces such as web-services or RMI (Remote Method Invocation) access to application functionality, synchronization between heterogeneous closed-box software interfaces and a user-friendly version of those interfaces has become a major challenge. In this paper, we design SINk, a middleware that enables synchronization of multiple heterogeneous software applications, using only graphical interface, without the need for source code access or access to the entire platform's control. SINk helps with synchronization of closed-box industry software, where in fact the only possible way of communication is through software interfaces. It leverages efficient client sever architecture, socket based protocol, adaptation to resolution changes, and parameter mapping mechanisms to transfer control events to ensure the real-time requirements of synchronization among multiple interfaces are met. Our proof-of-concept evaluation shows there is in fact potential usage of our middleware in a wide variety of domains.

preprint2012arXiv

S3A: Secure System Simplex Architecture for Enhanced Security of Cyber-Physical Systems

Until recently, cyber-physical systems, especially those with safety-critical properties that manage critical infrastructure (e.g. power generation plants, water treatment facilities, etc.) were considered to be invulnerable against software security breaches. The recently discovered 'W32.Stuxnet' worm has drastically changed this perception by demonstrating that such systems are susceptible to external attacks. Here we present an architecture that enhances the security of safety-critical cyber-physical systems despite the presence of such malware. Our architecture uses the property that control systems have deterministic execution behavior, to detect an intrusion within 0.6 μs while still guaranteeing the safety of the plant. We also show that even if an attack is successful, the overall state of the physical system will still remain safe. Even if the operating system's administrative privileges have been compromised, our architecture will still be able to protect the physical system from coming to harm.

preprint2011arXiv

Deterministic Real-time Thread Scheduling

Race condition is a timing sensitive problem. A significant source of timing variation comes from nondeterministic hardware interactions such as cache misses. While data race detectors and model checkers can check races, the enormous state space of complex software makes it difficult to identify all of the races and those residual implementation errors still remain a big challenge. In this paper, we propose deterministic real-time scheduling methods to address scheduling nondeterminism in uniprocessor systems. The main idea is to use timing insensitive deterministic events, e.g, an instruction counter, in conjunction with a real-time clock to schedule threads. By introducing the concept of Worst Case Executable Instructions (WCEI), we guarantee both determinism and real-time performance.