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Physical Layer Key Generation: Securing Wireless Communication in Automotive Cyber-Physical Systems

Modern automotive Cyber-Physical Systems (CPS) are increasingly adopting wireless communications for Intra-Vehicular, Vehicle-to-Vehicle (V2V), and Vehicle-to-Infrastructure (V2I) protocols as a promising solution for challenges such as the wire harnessing problem, collision detection, and collision avoidance, traffic control, and environmental... (more)

Tradeoffs in Neuroevolutionary Learning-Based Real-Time Robotic Task Design in the Imprecise Computation Framework

A cyberphysical avatar is a semi-autonomous robot that adjusts to an unstructured environment and... (more)

TORUS: Scalable Requirements Traceability for Large-Scale Cyber-Physical Systems

Cyber-Physical Systems (CPS) contain intertwined and distributed software, hardware, and physical components to control complex physical processes. They find wide application in industrial systems, such as smart grid protection systems, which face increasingly complex communication and computation needs. Due to the scale and complexity of the... (more)

Anonymous, Fault-Tolerant Distributed Queries for Smart Devices

Applications that aggregate and query data from distributed embedded devices are of interest in many settings, such as smart buildings and cities, the... (more)

Inferring Smart Schedules for Dumb Thermostats

Heating, ventilation, and air conditioning (HVAC) accounts for over 50% of a typical home’s energy usage. A thermostat generally controls HVAC usage in a home to ensure user comfort. In this article, we focus on making existing “dumb” programmable thermostats smart by applying energy analytics on smart meter data to infer... (more)

Threat Analysis in Systems-of-Systems: An Emergence-Oriented Approach

Cyber-physical Systems of Systems (SoSs) are large-scale systems made of independent and autonomous cyber-physical Constituent Systems (CSs) which may interoperate to achieve high-level goals also with the intervention of humans. Providing security in such SoSs means, among other features, forecasting and anticipating evolving SoS functionalities,... (more)

Model-Based Quantitative Evaluation of Repair Procedures in Gas Distribution Networks

We propose an approach for assessing the impact of multi-phased repair procedures on gas distribution networks, capturing load profiles that can... (more)

Looking Under the Hood of Z-Wave: Volatile Memory Introspection for the ZW0301 Transceiver

Z-Wave is a proprietary Internet of Things substrate providing distributed home and office automation services. The proprietary nature of Z-Wave devices makes it difficult to determine their security aptitude. While there are a variety of open source tools for analyzing Z-Wave frames, inspecting non-volatile memory, and disassembling firmware,... (more)

National-scale Traffic Model Calibration in Real Time with Multi-source Incomplete Data

Real-time traffic modeling at national scale is essential to many applications, but its calibration is extremely challenging due to its large spatial... (more)

Reinforcement Learning for UAV Attitude Control

Autopilot systems are typically composed of an “inner loop” providing stability and control, whereas an “outer loop” is responsible for mission-level objectives, such as way-point navigation. Autopilot systems for unmanned aerial vehicles are predominately implemented using... (more)

Building Virtual Power Meters for Online Load Tracking

Many energy optimizations require fine-grained, load-level energy data collected in real time, most typically by a plug-level energy meter. Online... (more)

NEWS

CFP: Special Issue on Security and Privacy for Connected Cyber-Physical Systems
This special issue focuses on security & privacy aspects of emerging trends and applications involving Machine-to-Machine Cyber Physical Systems (M2M CPSs) in both generic and specific domain of interests. We invite original research articles proposing innovative solutions to improve IoT security and privacy, taking in account the low resource characteristics of CPS components, the distributed nature of CPSs, and connectivity constraints of IoT devices. For more information, visit the Special Issue webpage.

CFP: Special Issue on Time for CPS
Timing is crucial for safety, security, and responsiveness of Cyber-Physical System (CPS). This special issue invites manuscripts that study any aspect of the interaction of CPS and its timing. For more information, visit the Special Issue webpage.

CFP: Special Issue on User-Centric Security and Safety for Cyber-Physical Systems
This special issue focuses on user-centric security and safety aspects of cyber-physical systems (CPS), with the aims of filling gaps between the user behaviour and the design of complex cyber-physical systems. For more information, visit the Special Issue webpage.

CFP: Special Issue on Human-Interaction-Aware Data Analytics for Cyber-Physical Systems
This special issue focuses on fundamental problems involving human-interaction-aware data analytics with future CPS. The aim of this special issue is to provide a platform for researchers and practitioners from academia, government and industry to present their state-of-the-art research results in the area of human-interaction-aware data analytics for CPS. For more information, visit the Special Issue webpage.

CFP: Special Issue on Self-Awareness in
Resource Constrained Cyber-Physical Systems

This special issue seeks original manuscripts which will cover recent development on methods, architecture, design, validation and application of resource-constrained cyber-physical systems that exhibit a degree of self-awareness. For more information, visit the Special Issue webpage.

CFP: Special Issue on Real-Time aspects in Cyber-Physical Systems
This special issue invites original, high-quality work that report the latest advances in real-time aspects in CPSs. Featured articles should present novel strategies that address real-time issues in different aspects of CPS design and implementation, including theory, system software, middleware, applications, network, tool chains, test beds, and case studies. For more information, visit the Special Issue webpage.

CFP: Special Issue on Transportation Cyber-Physical Systems
The aim of this special issue will be to feature articles on new technologies that will impact future transportation systems. They might span across vehicular technologies – such as autonomous vehicles, vehicle platooning and electric cars, communication technologies to enable vehicle-to-vehicle and vehicle-to-infrastructure communication, security mechanisms, infrastructure-level technologies to support transportation, as well as management systems and policies such as traffic light control, intersection management, dynamic toll pricing and parking management. In addition to terrestrial transportation, traffic control and autonomous management of aerial vehicles and maritime ships are also of interest. For more information, visit the Special Issue webpage.

About TCPS

Cyber-Physical Systems (CPS) has emerged as a unifying name for systems where the cyber parts, i.e., the computing and communication parts, and the physical parts are tightly integrated, both at the design time and during operation. Such systems use computations and communication deeply embedded in and interacting with physical processes to add new capabilities to physical systems. These cyber-physical systems range from miniscule (pace makers) to large-scale (a national power-grid). There is an emerging consensus that new methodologies and tools need to be developed to support cyber-physical systems.  READ MORE

Forthcoming Articles
Improving the Security of Visual Challenges

This paper proposes new tools to detect the tampering of video feeds from surveillance cameras. Our proposal illustrates the unique cyber-physical properties that sensor devices can leverage for their cyber-security. While traditional authentication and attestation algorithms exchange digital challenges between devices authenticating each other, our work instead proposes challenges that manifest physically in the field of view of the camera (e.g., a QR code in a display, a change of color in lighting, an infrared light, etc.). This physical (challenge) and cyber (verification) attestation mechanism can help protect systems even when the sensors (cameras) and actuators (Display, IR LEDs, Color Lightbulbs) are compromised.

A Novel Dynamic Routing Framework for Shared Mobility Services

Travel time in urban centers is a significant contributor to the quality of living of its citizens. Mobility on Demand (MoD) services such as Uber and Lyft have revolutionized the transportation infrastructure, enabling new solutions for passengers. Shared MoD services have shown that a continuum of solutions can be provided between the traditional private transport for an individual and the public mass transit based transport, by making use of the underlying cyber-physical substrate that provides advanced, distributed, and networked computational and communicational support. In this paper, we propose a novel shared mobility service using a dynamic framework. This framework generates a dynamic route for multi-passenger transport, optimized to reduce time costs for both the shuttle and the passengers and is designed using a new concept of a space window. This concept introduces a degree of freedom that helps reduce the cost of the system involved in designing the optimal route. A specific algorithm based on the Alternating Minimization approach is proposed. Its analytical properties are characterized. Detailed computational experiments are carried out to demonstrate the advantages of the proposed approach and are shown to result in an order of magnitude improvement in the computational efficiency with minimal optimality gap when compared to a standard Mixed Integer Quadratically Constrained Programming based algorithm.

A Predictive Framework for Dynamic Heavy-Duty Vehicle Platoon Coordination

This article describes a system to facilitate dynamic en route formation of truck platoons with the goal of reducing fuel consumption. Safe truck platooning is a maturing technology which leverages modern sensor, control, and communication technology to automatically regulate the inter-vehicle distances. Truck platooning has been shown to reduce fuel consumption through slipstreaming by up to ten percent under realistic highway conditions. In order to further benefit from this technology, a platoon coordinator is proposed, which interfaces with fleet management systems and suggests how platoons can be formed in a fuel-efficient manner over a large region. The coordinator frequently updates the plans to react to newly available information. This way, it requires a minimum of information about the logistic operations. We discuss the system architecture in detail and introduce important underlying methodological foundations. Plans are derived in computationally tractable stages optimizing fuel savings from platooning. The effectiveness of this approach is verified in a simulation study. It shows that the coordinated platooning system can improve over spontaneously occurring platooning even under the presence of disturbances. A real demonstrator has also been developed. We present data from an experiment in which three vehicles were coordinated to form a platoon on public highways under normal traffic conditions. It demonstrates the feasibility of coordinated en route platoon formation with current communication and on-board technology. Simulations and experiments support that the proposed system is technically feasible and a potential solution to the problem of using truck platooning in an operational context.

Designing a controller with image-based pipelined sensing and additive uncertainties

Pipelined control is an image-based control that uses parallel instances of its image-processing algorithm in a pipelined fashion to improve the quality of control. A higher number of pipes improves the controller settling time resulting in a trade-off between resources and control performance. In real-life applications, it is common to have a continuous-time model with additive uncertainties in one or more parameters that may affect the controller performance and therefore, the trade-off analysis. We consider models with uncertainties denoted by matrices with a single non-zero element, potentially caused by multiple uncertain parameters in the model. We analyse the impact of such uncertainties in the before-mentioned trade-off. To do so, we introduce a discretization technique for the uncertain model. Next, we use the discretized model with uncertainties to analyse the robustness of a pipelined controller designed to enhance performance. Such an analysis captures the relationship between resource usage, control performance, and robustness. Our results show that the tolerable uncertainties for a pipelined controller decreases when increasing the number of pipes. We also show the feasibility of our technique by implementing a realistic example in a Hardware-In-the-Loop simulation.

Publish or Drop Traffic Event Alerts? Quality-aware Decision Making in Participatory Sensing-based Vehicular CPS

The vehicular cyber-physical systems (VCPS), among several other applications, may help in addressing the ever increasing problem of congestions in large cities. Nevertheless, this may be hindered by the problem of data falsification, which results out of either wrong perception of a traffic event or generation of fake information by the participating vehicles. Such information fabrication may cause re-routing of vehicles and artificial congestions, leading to economic, public safety, environmental, and health hazards. Thus, it is imperative to infer truthful traffic information at real-time for restoration of operation reliability of the VCPS. In this work, we propose a novel reputation scoring and decision support framework, called Spoofed and False Report Eradicator (SAFE), which offers a cost-effective and efficient solution to handle data falsification problem in the VCPS domain. It includes humans in the sensing loop by exploiting the paradigm of participatory sensing and a concept of mobile security agent (MSA) to nullify the effects of deliberate false contribution, and a variant of the distance bounding mechanism to thwart location-spoofing attacks. A regression-based model integrates these effects to generate the expected truthfulness of a participants contribution. To determine if any contribution is true or not, a generalized linear model is used to transform expected truthfulness into a Quality of Contribution (QoC) score. The QoC of different contributions are aggregated to compute the user reputation. Such reputation enables classification of different participation behaviors. Finally, an Expected Utility Theory (EUT)-based decision model is proposed which utilizes the reputation score to determine if an information should be published or dropped. To evaluate SAFE through experimental study, we compare the reputation-based user segregation performance achieved by our framework with that generated by the state-of-the-art reputation mechanisms. Experimental results demonstrate that SAFE is able to better capture subtle differences in user behaviors based on quality, quantity and location accuracy, and significantly improves operational reliability through accurate publishing of only legitimate information.

Bayesian Spatiotemporal Gaussian Process for Short-term Load Forecasting Using Combined Transportation and Electricity Load Data

Smart cities can be viewed as large-scale Cyber-Physical Systems (CPS) that different sensors and devices record the cyber and physical indicators of the urban environment. Those records are being used for improving urban life by offering improved efficiencies with accurate electric load forecasting, efficient traffic management, etc. Accurate forecasting is mostly dependent on the sufficient and reliable data. Traditional data collection methods are necessary but not sufficient due to their limited coverage and expensive cost of implementation and maintenance. For example, continuous traffic data collection is mostly limited to major highways only in many cities whereas secondary and local roadways are usually covered once or twice a year. The advances in sensor networks and recent technological developments such as methods based on vehicle locations and in-vehicle devices through mobile phones or GPS-based systems in transportation networks provide such an opportunity. Although these technologies also have the potential to connect the physical components and processes with the cyber world that leading to a Cyber-Physical Systems (CPS), they also have significant drawbacks. Specifically, they usually suffer from limited resolution due to limitations on time frame, cost, accuracy, and reliability. One way for improving the limited resolution is data fusion. Furthermore, a city should be considered as a collection of the layers of tangled city infrastructure networks which connects people, places, and resources. Therefore, the study of traffic or electricity consumption forecasting should go beyond the transportation and electricity networks, and merge with each other and even with other city networks such as environmental networks. As such, this paper proposes a traffic and electric load forecasting methodology which benefits from the data fusion techniques in order to fill the lack of sufficient information in any of these aforementioned networks. For this purpose, a Bayesian spatiotemporal Gaussian Process model is proposed which employs the most informative spatiotemporal interdependency among its own network, and covariates from other city networks. The proposed load forecasting fusion method is compared with other state-of-the-art methods including Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX), Multivariate Linear Regression, Support Vector Regression and Neural Networks Regression using real-life data obtained from the City of Tallahassee in Florida. Results show that multi-network data fusion framework improves the accuracy of load forecasting, and the proposed Bayesian spatiotemporal Gaussian Process model outperforms all the above-mentioned methods.

Model Conformance for Cyber-Physical Systems: A Survey

Model-based development is an important paradigm for developing cyber-physical systems (CPS). Early verification and validation of embedded software speeds up the development process and saves costs. This is especially challenging, since CPSs interact with complex environments through sensors and actuators requiring models of the relevant CPS and its context. Therefore, the strong underlying assumption is that models are adequate for the verification task. Conformance testing addresses this problem by checking that two models of the same CPS are conformant, i. e., produce equivalent behavior w. r. t. the verification task. Although conformance is in general undecidable, for the relevant models of CPSs in practice, non-formal conformance checking procedures typically succeed in verifying conformance. In this work, we survey conformance checking for CPS  we do not only perform a comparison of approaches for the evaluation of conformance, but also survey the required input generation.

Design and Analysis of Delay-Tolerant Intelligent Intersection Management

The rapid development of vehicular network and autonomous driving technologies provides opportunities to significantly improve transportation safety and efficiency. One promising application is centralized intelligent intersection management, where an intersection manager accepts requests from approaching vehicles (via vehicle-to-infrastructure communication messages) and schedules the order for those vehicles to safely crossing the intersection. However, communication delays and packet losses may occur due to the unreliable nature of wireless communication or malicious security attacks (such as jamming and flooding), and could cause deadlocks and unsafe situations. In our previous work, we considered these issues and proposed a delay-tolerant intersection management protocol for intersections with a single lane in each direction. In this work, we address key challenges in efficiency and deadlock when there are multiple lanes from each direction, and propose a delay-tolerant protocol for general multi-lane intersection management. We prove that this protocol is deadlock-free, safe and satisfying the liveness property. Furthermore, we extend the traffic simulation suite SUMO with communication modules, implement our protocol in the extended simulator, and quantitatively analyze its performance with the consideration of communication delays. Finally, we also model systems using smart traffic lights with back-pressure scheduling in SUMO, and compare our delay-tolerant intelligent intersection protocol with smart traffic lights in cases of a single intersection and a network of interconnected intersections. Simulation results demonstrate the effectiveness of our approach.

Catering to Your Concerns: Automatic Generation of Personalised Security-Centric Descriptions for Android Apps

Android users are increasingly concerned with the privacy of their data and security of their devices. To improve the security awareness of users, recent automatic techniques produce security-centric descriptions by performing program analysis. However, the generated text does not always address users? concerns as they are generally too technical to be understood by ordinary users. Moreover, different users have varied linguistic preferences, which do not match the text. Motivated by this challenge, we develop an innovative scheme to help users avoid malware and privacy-breaching apps by generating security descriptions that explain the privacy and security related aspects of an Android app in clear and understandable terms. We implement a prototype system, PERSCRIPTION, to generate personalised security-centric descriptions that automatically learn users? security concerns and linguistic preferences to produce user-oriented descriptions. We evaluate our scheme through experiments and user studies. The results clearly demonstrate the improvement on readability and users? security awareness of PERSCRIPTION?s descriptions compared to existing description generators.

Modeling and Optimization for Self-Power Non-Volatile IoT Edge Devices with Ultra-Low Harvesting Power

Energy harvesters are becoming increasingly popular as power sources for IoT edge devices. However, one of the intrinsic problems of energy harvester is that the harvesting power is often weak and frequently interrupted. Therefore, energy harvesting powered edge devices have to work intermittently. To maintain execution progress, execution states need to be checkpointed into the non-volatile memory before each power failure. In this way, previous execution states can be resumed after power comes back again. Nevertheless, frequent checkpointing and low charging efficiency generate significant energy overhead. To alleviate these problems, this paper conducts a thorough energy efficiency analysis and proposes three algorithms to maximize the energy efficiency of program execution. First, a non-volatile processor (NVP) aware task scheduling (NTS) algorithm is proposed to reduce the size of checkpointing data. Second, a tentative checkpointing avoidance (TCA) technique is proposed to avoid checkpointing for further reduction of checkpointing overhead. Finally, a dynamic wake-up strategy (DWS) is proposed to wake up the edge device at proper voltages where the total hardware and software overhead is minimized for further energy efficiency maximization. The experiments on a real testbed demonstrate that, with the proposed algorithms, an edge device is resilient to extremely weak and intermittent power supply and the energy efficiency is as $2\times$ high as the baseline technique.

A Sustainable and User Behavior Aware Cyber-Physical System for Home Energy Management

There is a growing trend for employing cyber-physical systems to help smart homes to improve the comfort of residents. However, a residential cyber-physical system is differed from a common cyber-physical system since it directly involves human interaction, which is full of uncertainty. The existing solutions could be effective for performance enhancement in some cases when no inherent and dominant human factors are involved. Besides, The rapidly rising interest in the deployments of cyber-physical systems at home does not normally integrate with energy management schemes, which is a central issue that smart homes have to face. In this paper, we propose a cyber-physical system based energy management framework to enable a sustainable edge computing paradigm while meeting the needs of home energy management and residents. This framework aims to enable the full use of renewable energy while reducing electricity bills for households. A prototype system was implemented using real world hardware. The experiment results demonstrated that renewable energy is fully capable of supporting the reliable running of home appliances most of the time and electricity bills could be cut by up to 60% when our proposed framework was employed.

TruckSTM: Runtime Realization of Operational State Transitions for Medium and Heavy Duty Vehicles

Embedded computing devices play an integral role in the mechanical operations of modern-day vehicles. These devices exchange information that contains critical vehicle parameters that reflect the current of state of operations. Such information can be captured for various purposes like diagnostics, fleet management, and even independent research. Although monitoring individual parameters can be useful for some applications, monitoring distinct combinations of parameters can reveal more complex and higher level states that may be worth observing. Existing monitoring systems either lack user configurability and control or present simple user interfaces that make it difficult to monitor and collate different parameters in order to observe high-level vehicle states. In this work, we present TruckSTM, a novel application that realizes user-defined states from messages seen in the embedded networks of medium and heavy duty vehicles and displays state transitions on an interactive user-interface. We begin by symbolically formulating some of the in-vehicle networking concepts and formally defining the concept of operational states and state transitions. We then elaborate on the operations performed by TruckSTM in mapping network obtained vehicle parameters to states that can be defined in standard JSON format. Finally, we evaluate TruckSTM's asymptotic performance and present the results for the worst-case scenario.

Combining Detection and Verification for Secure Vehicular Cooperation Groups

Coordinated vehicles for intelligent traffic management are instances of a cyber-physical systems with strict correctness requirements. A key building block for these systems is the ability to establish a group membership view that accurately captures the locations of all vehicles in a particular area of interest. We formally define view correctness in terms of soundness and completeness and establish theoretical bounds for the ability to verify view correctness. Moreover, we present an architecture for an online view detection and verification process that uses the information available locally to a vehicle. This architecture uses an SMT solver to automatically prove view correctness. We evaluate this architecture and demonstrate that the ability to verify view correctness is on par with the ability to detect view violations.

A Distributed Tensor-Train Decomposition Method for Cyber-Physical-Social Services

Cyber-Physical-Social Systems (CPSS) integrating the cyber, physical and social worlds, is a key technology to provide proactive and personalized services for humans. In this paper, we studied CPSS, by taking human-interaction-aware big data (HIBD) as the starting point. However, the HIBD collected from all aspects of our daily lives are of high-order and large-scale, which brings ever-increasing challenges for their cleaning, integration, processing and interpretation. Therefore, new strategies of representing and processing of HIBD becomes increasingly important in the provision of CPSS services. As an emerging technique, tensor, is proving to be a suitable and promising representation and processing tool of HIBD. In particular, tensor networks, as a kind of significant tensor decomposition, bring advantages of computing, storage and application of HIBD. Furthermore, Tensor-Train (TT), another kind of tensor networks, is particularly well suited for representing and processing high-order data by decomposing a high-order tensor into a series of low order tensors. However, at present, there is still need for an efficient Tensor-Train decomposition method for massive data. Therefore, for lager-scale HIBD, a highly-efficient computational method of Tensor-Train is required. In this paper, a distributed Tensor-Train (DTT) decomposition method is proposed to process the high-order and large-scale HIBD. The high performance of the proposed DTT such as the execution time is demonstrated with a case study on a typical CPSS data - CT (Computed Tomography) image data. Furthermore, as a typical CPSS application for HIBD - recognition was carried out in TT to illustrate the advantage of DTT.

Data Integrity Threats and Countermeasures in Railway Spot Transmission Systems

Modern trains rely on balises (communication beacons) located on the track to provide location information as they traverse a rail network. Balises, such as those conforming to the Eurobalise standard, were not designed with security in mind and are thus vulnerable to cyber attacks targeting data availability, integrity, or authenticity. In this work, we discuss data integrity threats to balise transmission modules and use high-fidelity simulation to study the risks posed by data integrity attacks. To mitigate such risk, we propose a practical two-layer solution: at the device level, we design a lightweight and low-cost cryptographic solution to protect the integrity of the location information; at the system layer, we devise a secure hybrid train speed controller to mitigate the impact under various attacks. Our simulation results demonstrate the effectiveness of our proposed solutions.

Efficient Multi-Factor User Authentication Protocol with Forward Secrecy for Real-Time Data Access in WSNs

It is challenging to design a secure and efficient multi-factor authentication scheme for real-time user data access in wireless sensor networks (WSNs). On the one hand, such real-time applications are generally security-critical, and various security goals need to be met. On the other hand, sensor nodes and users' mobile devices are typically of resource-constrained nature, and expensive cryptographic primitives cannot be used. In this work, we first revisit four foremost multi-factor authentication schemes, i.e., Srinivas et al.'s (IEEE TDSC'18), Amin et al.'s (JNCA'18), Li et al.'s (JNCA'18) and Li et al.'s (IEEE TII'18) schemes, and use them as case studies to reveal the difficulties and challenges in designing a multi-factor authentication scheme for WSNs right. We identify the root causes for their failures in achieving truly multi-factor security and forward secrecy. We further propose a robust multi-factor authentication scheme that makes use of the imbalanced computational nature of the RSA cryptosystem, particularly suitable for scenarios where sensor nodes (but not the user's device) are the main energy bottleneck. Comparison results demonstrate the superiority of our scheme. As far as we know, it is the first one that can satisfy all the twelve criteria of the state-of-the-art evaluation metric under the harshest adversary model so far.

Test Specification and Generation for Connected and Autonomous Vehicle in Virtual Environment

The trend of connected / autonomous features adds significant complexity to the traditional automotive systems to improve driving safety and comfort. Engineers are facing significant challenges in designing test environments that are more complex than ever. We propose a test framework that allows one to automatically generate various virtual road environments from the path specification and the behavior specification. The path specification intends to characterize geometric paths that an environmental object (e.g., roadways or pedestrians) needs to be visualized or move over. We characterize this aspect in the form of linear or nonlinear constraints of 3-Dimensional coordinates. Then, we introduce a test coverage, called an area coverage, to quantify the quality of generated paths in terms of how wide area the generated paths can cover. We propose an algorithm that automatically generate such paths using a SMT (Satisfiability Modulo Theories) solver. On the other hand, the behavioral specification intends to characterize how an environmental object changes its mode changes over time by interacting with other objects (e.g., a pedestrian waits for a signal or start crossing). We characterize this aspect in the form of timed automata. Then, we introduce a test coverage, called an edge/location coverage, to quantify the quality of the generated mode changes in terms of how many modes or transitions are visited. We propose a method that automatically generates many different mode changes using a model-checking method. To demonstrate the test framework, we developed the right turn pedestrian warning system in intersection scenarios and generated many different types of pedestrian paths and behaviors to analyze the effectiveness of the system.

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