Minimization of Sensor Activation in Discrete-Event Systems with Control Delays and Observation Delays

In discrete-event systems, to save sensor resources, the agent continuously adjusts sensor activation decisions according to a sensor activation policy based on the changing observations. However, new challenges arise for sensor activations in networked discrete-event systems, where observation delays and control delays exist between the sensor systems and the agent. In this paper, a new framework for activating sensors in networked discrete-event systems is established. In this framework, we construct a communication automaton that explicitly expresses the interaction process between the agent and the sensor systems over the observation channel and the control channel. Based on the communication automaton, we can define dynamic observations of a communicated string. To guarantee that a sensor activation policy is physically implementable and insensitive to random control delays and observation delays, we further introduce the definition of delay feasibility. We show that a delay feasible sensor activation policy can be used to dynamically activate sensors even if control delays and observation delays exist. A set of algorithms are developed to minimize sensor activations in a transition-based domain while ensuring a given specification condition is satisfied. A practical example is provided to show the application of the developed sensor activation methods. Finally, we briefly discuss how to extend the proposed framework to a decentralized sensing architecture

Pedestrian Behavior Study to Advance Pedestrian Safety in Smart Transportation Systems Using Innovative LiDAR Sensors

Pedestrian safety is critical to improving walkability in cities. Although walking trips have increased in the last decade, pedestrian safety remains a top concern. In 2020, 6,516 pedestrians were killed in traffic crashes, representing the most deaths since 1990 (NHTSA, 2020). Approximately 15% of these occurred at signalized intersections where a variety of modes converge, leading to the increased propensity of conflicts. Current signal timing and detection technologies are heavily biased towards vehicular traffic, often leading to higher delays and insufficient walk times for pedestrians, which could result in risky behaviors such as noncompliance. Current detection systems for pedestrians at signalized intersections consist primarily of push buttons. Limitations include the inability to provide feedback to the pedestrian that they have been detected, especially with older devices, and not being able to dynamically extend the walk times if the pedestrians fail to clear the crosswalk. Smart transportation systems play a vital role in enhancing mobility and safety and provide innovative techniques to connect pedestrians, vehicles, and infrastructure. Most research on smart and connected technologies is focused on vehicles; however, there is a critical need to harness the power of these technologies to study pedestrian behavior, as pedestrians are the most vulnerable users of the transportation system. While a few studies have used location technologies to detect pedestrians, this coverage is usually small and favors people with smartphones. However, the transportation system must consider a full spectrum of pedestrians and accommodate everyone. In this research, the investigators first review the previous studies on pedestrian behavior data and sensing technologies. Then the research team developed a pedestrian behavioral data collecting system based on the emerging LiDAR sensors. The system was deployed at two signalized intersections. Two studies were conducted: (a) pedestrian behaviors study at signalized intersections, analyzing the pedestrian waiting time before crossing, generalized perception-reaction time to WALK sign and crossing speed; and (b) a novel dynamic flashing yellow arrow (D-FYA) solution to separate permissive left-turn vehicles from concurrent crossing pedestrians. The results reveal that the pedestrian behaviors may have evolved compared with the recommended behaviors in the pedestrian facility design guideline (e.g., AASHTO’s “Green Book”). The D-FYA solution was also evaluated on the cabinet-in-theloop simulation platform and the improvements were promising. The findings in this study will advance the body of knowledge on equitable traffic safety, especially for pedestrian safety in the future

Property Enforcement for Partially-Observed Discrete-Event Systems

Engineering systems that involve physical elements, such as automobiles, aircraft, or electric power pants, that are controlled by a computational infrastructure that consists of several computers that communicate through a communication network, are called Cyber-Physical Systems. Ever-increasing demands for safety, security, performance, and certi cation of these critical systems put stringent constraints on their design and necessitate the use of formal model-based approaches to synthesize provably-correct feedback controllers. This dissertation aims to tackle these challenges by developing a novel methodology for synthesis of control and sensing strategies for Discrete Event Systems (DES), an important class of cyber-physical systems. First, we develop a uniform approach for synthesizing property enforcing supervisors for a wide class of properties called information-state-based (IS-based) properties. We then consider the enforcement of non-blockingness in addition to IS-based properties. We develop a nite structure called the All Enforcement Structure (AES) that embeds all valid supervisors. Furthermore, we propose novel and general approaches to solve the sensor activation problem for partially-observed DES. We extend our results for the sensor activation problem from the centralized case to the decentralized case. The methodology in the dissertation has the following novel features: (i) it explicitly considers and handles imperfect state information, due to sensor noise, and limited controllability, due to unexpected environmental disturbances; (ii) it is a uniform information-state-based approach that can be applied to a variety of user-speci ed requirements; (iii) it is a formal model-based approach, which results in provably correct solutions; and (iv) the methodology and associated theoretical foundations developed are generic and applicable to many types of networked cyber-physical systems with safety-critical requirements, in particular networked systems such as aircraft electric power systems and intelligent transportation systems.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137097/1/xiangyin_1.pd

Discrete Event System Methods for Control Problems Arising in Cyber-physical Systems.

We consider two problems in cyber-physical systems. The first is that of dynamic fault diagnosis. Specifically, we assume that a plant model is available in the form of a discrete event system (DES) containing special fault events whose occurrences are to be diagnosed. Furthermore, it is assumed that there exist sensors that can be turned on or off and are capable of detecting some subset of the system’s non-faulty events. The problem to be solved consists of constructing a compact structure, called the most permissive observer (MPO), containing the set of all sequences of sensor activations that ensure the timely diagnosis of any fault event’s occurrence. We solve this problem by defining an appropriate notion of information state summarizing the information obtained from the past sequence of observations and sensor activations. The resulting MPO has a better space complexity than that of the previous approach in the literature. The second problem considered in this thesis is that of controlling vehicles through an intersection. Specifically, we wish to obtain a supervisor for the vehicles that is safe, non-deadlocking, and maximally permissive. Furthermore, we solve this problem in the presence of uncontrolled vehicles, bounded disturbances in the dynamics, and measurement uncertainty. Our approach consists of discretizing the system in time and space, obtaining a DES abstraction, solving for maximally permissive supervisors in the abstracted domain, and refining the supervisor to one for the original, continuous, problem domain. We provide general results under which this approach yields maximally permissive memoryless supervisors for the original system and show that, under certain conditions, the resulting supervisor will be maximally permissive over the class of all supervisors, not merely memoryless ones. Our contributions are as follows. First, by constructing DES abstractions from continuous systems, we can leverage the supervisory control theory of DES, which is well-suited to finding maximally permissive supervisors under safety and non-blocking constraints. Second, we define different types of relations between transition systems and their abstractions and, for each relation, characterize the class of supervisors over which the supervisors obtained under our approach are maximally permissive.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108720/1/edallal_1.pd

Pedestrian Behavior Study to Advance Pedestrian Safety in Smart Transportation Systems Using Innovative LiDAR Sensors

69A3551747112Pedestrian safety is critical to improving walkability in cities. Although walking trips have increased in the last decade, pedestrian safety remains a top concern. In 2020, 6,516 pedestrians were killed in traffic crashes, representing the most deaths since 1990 (NHTSA, 2020). Approximately 15% of these occurred at signalized intersections where a variety of modes converge, leading to the increased propensity of conflicts. Current signal timing and detection technologies are heavily biased towards vehicular traffic, often leading to higher delays and insufficient walk times for pedestrians, which could result in risky behaviors such as noncompliance. Current detection systems for pedestrians at signalized intersections consist primarily of push buttons. Limitations include the inability to provide feedback to the pedestrian that they have been detected, especially with older devices, and not being able to dynamically extend the walk times if the pedestrians fail to clear the crosswalk. Smart transportation systems play a vital role in enhancing mobility and safety and provide innovative techniques to connect pedestrians, vehicles, and infrastructure. Most research on smart and connected technologies is focused on vehicles; however, there is a critical need to harness the power of these technologies to study pedestrian behavior, as pedestrians are the most vulnerable users of the transportation system. While a few studies have used location technologies to detect pedestrians, this coverage is usually small and favors people with smartphones. However, the transportation system must consider a full spectrum of pedestrians and accommodate everyone. In this research, the investigators first review the previous studies on pedestrian behavior data and sensing technologies. Then the research team developed a pedestrian behavioral data collecting system based on the emerging LiDAR sensors. The system was deployed at two signalized intersections. Two studies were conducted: (a) pedestrian behaviors study at signalized intersections, analyzing the pedestrian waiting time before crossing, generalized perception-reaction time to WALK sign and crossing speed; and (b) a novel dynamic flashing yellow arrow (D-FYA) solution to separate permissive left-turn vehicles from concurrent crossing pedestrians. The results reveal that the pedestrian behaviors may have evolved compared with the recommended behaviors in the pedestrian facility design guideline (e.g., AASHTO\u2019s \u201cGreen Book\u201d). The D-FYA solution was also evaluated on the cabinet-in-the-loop simulation platform and the improvements were promising. The findings in this study will advance the body of knowledge on equitable traffic safety, especially for pedestrian safety in the future

Deep Learning Methods for Streaming Image Reconstruction in Fixed-camera Settings

A streaming video reconstruction system is described and implemented as a convolutional neural network. The system performs combined 2x super-resolution and H.264 artefacts removal with a processing speed of about 6 frames per second at 1920×1080 output resolution on current workstation-grade hardware. In 4x super-resolution mode, the system can output 3840×2160 video at a similar rate. The base system provides quality improvements of 0.010–0.025 SSIM over Lanczos filtering. Scene-specific training, in which the system automatically adapts to the current scene viewed by the camera, is shown to achieve up to 0.030 SSIM additional improvement in some scenarios. It is further shown that scene-specific training can provide some improvement even when reconstructing an unfamiliar scene, as long as the camera and capture settings remain the same.Många kameror sitter fast monterade och filmar samma plats varje dag. Tänk om kameror kunde tränas till att minnas vad de sett

Hierarchical Reasoning about Faults in Cyber-Physical Energy Systems using Temporal Causal Diagrams

Fault management systems that observe the state of the system, decide if there is an anomaly and then take automated actions to isolate faults. For example, in electrical networks relays and breaks isolate faults in order to arrest failure propagation and protect the healthy parts of the system. However, due to the limited situational awareness and hidden failures the protection devices themselves, through their operation (or mis-operation) may cause overloading and the disconnection of parts of an otherwise healthy system. Additionally, often there can be faults in the management system itself leading to situations where it is difficult to isolate failures. Our work presented in this paper is geared towards solution of this problem by describing the formalism of Temporal Causal Diagrams (TCD-s) that augment the failure models for the physical systems with discrete time models of protection elements, accounting for the complex interactions between the protection devices and the physical plants. We use the case study of the standard Western System Coordinating Council (WSCC) 9 bus system to describe four different fault scenarios and illustrate how our approach can help isolate these failures. Though, we use power networks as exemplars in this paper our approach can be applied to other distributed cyberphysical systems, for example water networks

A Domain-Specific Language for Multitask Systems, Applying Discrete Controller Synthesis

International audienceWe propose a simple programming language, called Nemo, specific to the domain of multitask real-time control systems, such as in robotic, automotive, or avionics systems. It can be used to specify a set of resources with usage constraints, a set of tasks that consume them according to various modes, and applications sequencing the tasks. We automatically obtain an application-specific task handler that correctly manages the constraints (if there exists one), through a compilation-like process including a phase of discrete controller synthesis. This way, this formal technique contributes to the safety of the designed systems, while being encapsulated in a tool that makes it usable by application experts. Our approach is based on the synchronous modelling techniques, languages, and tools

Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems

Understanding the components of Transportation Cyber-Physical Systems (TCPS), and inter-relation and interactions among these components are key factors to leverage the full potentials of Connected and Automated Vehicles (CAVs). In a connected environment, CAVs can communicate with other components of TCPS, which include other CAVs, other connected road users, and digital infrastructure. Deploying supporting infrastructure for TCPS, and developing and testing CAV-specific applications in a TCPS environment are mandatory to achieve the CAV potentials. This dissertation specifically focuses on the study of current TCPS infrastructure (Part 1), and the development and verification of CAV applications for an urban TCPS environment (Part 2). Among the TCPS components, digital infrastructure bears sheer importance as without connected infrastructure, the Vehicle-to-Infrastructure (V2I) applications cannot be implemented. While focusing on the V2I applications in Part 1, this dissertation evaluates the current digital roadway infrastructure status. The dissertation presents a set of recommendations, based on a review of current practices and future needs. In Part 2, To synergize the digital infrastructure deployment with CAV deployments, two V2I applications are developed for CAVs for an urban TCPS environment. At first, a real-time adaptive traffic signal control algorithm is developed, which utilizes CAV data to compute the signal timing parameters for an urban arterial in the near-congested traffic condition. The analysis reveals that the CAV-based adaptive signal control provides operational benefits to both CVs and non-CVs with limited data from 5% CVs, with 5.6% average speed increase, and 66.7% and 32.4% average maximum queue length and stopped delay reduction, respectively, on a corridor compared to the actuated coordinated scenario. The second application includes the development of a situation-aware left-turning CAV controller module, which optimizes CAV speed based on the follower driver\u27s aggressiveness. Existing autonomous vehicle controllers do not consider the surrounding driver\u27s behavior, which may lead to road rage, and rear-end crashes. The analysis shows that the average travel time reduction for the scenarios with 600, 800 and 1000 veh/hr/lane opposite traffic stream are 61%, 23%, and 41%, respectively, for the follower vehicles, if the follower driver\u27s behavior is considered by CAVs