Pub Date : 2022-05-01DOI: 10.1109/iccps54341.2022.00009
S. Sheikhi, Edward Kim, Parasara Sridhar Duggirala, Stanley Bak
Fuzz testing is an indispensable test-generation tool in software security. Fuzz testing uses automated directed randomness to explore a variety of execution paths in software, trying to expose defects such as buffer overflows. Since cyber-physical systems (CPS) are often safety-critical, testing models of CPS can also expose faults. However, while existing coverage-guided fuzz testing methods are effective for software, results can be disappointing when applied to CPS, where systems have continuous states and inputs are applied at different points in time. In this work, we propose three changes to customize coverage-guided fuzz testing methods to better leverage characteristics of CPS. First, we introduce a notion of coverage to be used to evaluate a fuzz testing algorithm's effectiveness for a particular CPS, analogous to often-used code coverage metrics of a software system. Second, this modified coverage metric is used in a customized power schedule, which selects which previous input sequences hold the most promise to find failures in new system states. Third, we modify the input mutation strategy used to reason with the causal nature of a CPS. Our proposed system, which we call CPS-Fuzz, is compared with three other fuzz testing frameworks on a autonomous car racing software and provides a superior coverage score by generating more crashes at different positions around the track.
{"title":"Coverage-Guided Fuzz Testing for Cyber-Physical Systems","authors":"S. Sheikhi, Edward Kim, Parasara Sridhar Duggirala, Stanley Bak","doi":"10.1109/iccps54341.2022.00009","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00009","url":null,"abstract":"Fuzz testing is an indispensable test-generation tool in software security. Fuzz testing uses automated directed randomness to explore a variety of execution paths in software, trying to expose defects such as buffer overflows. Since cyber-physical systems (CPS) are often safety-critical, testing models of CPS can also expose faults. However, while existing coverage-guided fuzz testing methods are effective for software, results can be disappointing when applied to CPS, where systems have continuous states and inputs are applied at different points in time. In this work, we propose three changes to customize coverage-guided fuzz testing methods to better leverage characteristics of CPS. First, we introduce a notion of coverage to be used to evaluate a fuzz testing algorithm's effectiveness for a particular CPS, analogous to often-used code coverage metrics of a software system. Second, this modified coverage metric is used in a customized power schedule, which selects which previous input sequences hold the most promise to find failures in new system states. Third, we modify the input mutation strategy used to reason with the causal nature of a CPS. Our proposed system, which we call CPS-Fuzz, is compared with three other fuzz testing frameworks on a autonomous car racing software and provides a superior coverage score by generating more crashes at different positions around the track.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121815683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1109/iccps54341.2022.00008
Abolfazl Karimi, Parasara Sridhar Duggirala
This paper presents a new framework for generating test-case scenarios for autonomous vehicles. We address two challenges in automatic test-case generation: first, a formal notion of test-case complexity, and second, an algorithm to generate more-complex test-cases. We characterize the complexity of a test-case by its set of solutions, and compare two complexities by the subset relation. The novelty of our definition is that it only relies on the pass-fail criteria of the test-case, rather than indirect or subjective assessments of what may challenge an ego vehicle to pass a test-case. Given a test-case, we model the problem of generating a more complex test-case as a constraint-satisfaction search problem. The search variables are the changes to the given test-case, and the search constraints define a solution to the search problem. The constraints include steering geometry of cars, the geometry of lanes, the shape of cars, traffic rules, bounds on longitudinal acceleration of cars, etc. To conquer the computational challenge, we divide the constraints to three cat-egories and satisfy them with simulation, answer set programming, and SMT solving. We have implemented our algorithm using the Scenic libraries and the CARLA simulator and generate test-cases for several 3-way and 4-way intersections with different topologies. Our experiments demonstrate that both CARLA's autopilot and autopilot-plus-RSS (Responsibility-Sensitive Safety) can fail as the complexity of test-cases increase.
{"title":"Automatic Generation of Test-cases of Increasing Complexity for Autonomous Vehicles at Intersections","authors":"Abolfazl Karimi, Parasara Sridhar Duggirala","doi":"10.1109/iccps54341.2022.00008","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00008","url":null,"abstract":"This paper presents a new framework for generating test-case scenarios for autonomous vehicles. We address two challenges in automatic test-case generation: first, a formal notion of test-case complexity, and second, an algorithm to generate more-complex test-cases. We characterize the complexity of a test-case by its set of solutions, and compare two complexities by the subset relation. The novelty of our definition is that it only relies on the pass-fail criteria of the test-case, rather than indirect or subjective assessments of what may challenge an ego vehicle to pass a test-case. Given a test-case, we model the problem of generating a more complex test-case as a constraint-satisfaction search problem. The search variables are the changes to the given test-case, and the search constraints define a solution to the search problem. The constraints include steering geometry of cars, the geometry of lanes, the shape of cars, traffic rules, bounds on longitudinal acceleration of cars, etc. To conquer the computational challenge, we divide the constraints to three cat-egories and satisfy them with simulation, answer set programming, and SMT solving. We have implemented our algorithm using the Scenic libraries and the CARLA simulator and generate test-cases for several 3-way and 4-way intersections with different topologies. Our experiments demonstrate that both CARLA's autopilot and autopilot-plus-RSS (Responsibility-Sensitive Safety) can fail as the complexity of test-cases increase.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129514084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1109/iccps54341.2022.00023
Hsin-Yu Liu, Bharathan Balaji, Sicun Gao, Rajesh K. Gupta, Dezhi Hong
Buildings account for 30% of energy use worldwide, and approxi-mately half of it is ascribed to HVAC systems. Reinforcement Learning (RL) has improved upon traditional control methods in increasing the energy efficiency of HVAC systems. However, prior works use online RL methods that require configuring complex thermal simulators to train or use historical data-driven thermal models that can take at least 104 time steps to reach rule-based performance Also, due to the distribution drift from simulator to real buildings, RL solutions are therefore seldom deployed in the real world. On the other hand, batch RL methods can learn from the historical data and improve upon the existing policy without any interactions with the real buildings or simulators during the training. With the existing rule-based policy as the priors, the policies learned with batch RL are better than the existing control from the first day of deployment with very few training steps compared with online methods. Our algorithm incorporates a Kullback-Leibler (KL) regularization term to penalize policies that deviate far from the previous ones. We evaluate our framework on a real multi-zone, multi-floor building-it achieves 7.2% in energy reduction cf. the state-of-the-art batch RL method, and outperforms other BRL methods in occu-pants' thermal comfort, and 16.7% energy reduction compared to the default rule-based control.
{"title":"Safe HVAC Control via Batch Reinforcement Learning","authors":"Hsin-Yu Liu, Bharathan Balaji, Sicun Gao, Rajesh K. Gupta, Dezhi Hong","doi":"10.1109/iccps54341.2022.00023","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00023","url":null,"abstract":"Buildings account for 30% of energy use worldwide, and approxi-mately half of it is ascribed to HVAC systems. Reinforcement Learning (RL) has improved upon traditional control methods in increasing the energy efficiency of HVAC systems. However, prior works use online RL methods that require configuring complex thermal simulators to train or use historical data-driven thermal models that can take at least 104 time steps to reach rule-based performance Also, due to the distribution drift from simulator to real buildings, RL solutions are therefore seldom deployed in the real world. On the other hand, batch RL methods can learn from the historical data and improve upon the existing policy without any interactions with the real buildings or simulators during the training. With the existing rule-based policy as the priors, the policies learned with batch RL are better than the existing control from the first day of deployment with very few training steps compared with online methods. Our algorithm incorporates a Kullback-Leibler (KL) regularization term to penalize policies that deviate far from the previous ones. We evaluate our framework on a real multi-zone, multi-floor building-it achieves 7.2% in energy reduction cf. the state-of-the-art batch RL method, and outperforms other BRL methods in occu-pants' thermal comfort, and 16.7% energy reduction compared to the default rule-based control.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127701186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1109/iccps54341.2022.00039
B. Sudharsan, Panchakarla S. Rahul, Piyush Yadav, S. Gupta, Vimal Kumar, Duc-Duy Nguyen, M. Ali, J. Breslin
With the introduction of ultra-low-power machine learning (TinyML), IoT devices are becoming smarter as they are driven by ML models. However, any loss of communication at the device level can lead to a failure of the entire IoT system or misleading information trans-mission. Since there exist numerous heterogeneous devices within an IoT system, it is not feasible to centrally monitor all devices or explore system logs to determine communication loss. In this work, to maintain the highest possible communication quality and enable devices adapt according to context changes, we implement a lightweight ML-based adaptive strategy (ASB) and deploy it using a memory-optimized approach over the designed Pycom FiPy based multi-protocol IoT hardware. In real-world ex-periments, ASB equipped FiPy board accurately predicted the RSSI of WiFi 4 & WiFi 5 in real-time and switched between protocols - demonstrating interoperability amongst multiple IoT communication protocols and resilience against communication breakdown.
{"title":"RIS-IoT: Towards Resilient, Interoperable, Scalable IoT","authors":"B. Sudharsan, Panchakarla S. Rahul, Piyush Yadav, S. Gupta, Vimal Kumar, Duc-Duy Nguyen, M. Ali, J. Breslin","doi":"10.1109/iccps54341.2022.00039","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00039","url":null,"abstract":"With the introduction of ultra-low-power machine learning (TinyML), IoT devices are becoming smarter as they are driven by ML models. However, any loss of communication at the device level can lead to a failure of the entire IoT system or misleading information trans-mission. Since there exist numerous heterogeneous devices within an IoT system, it is not feasible to centrally monitor all devices or explore system logs to determine communication loss. In this work, to maintain the highest possible communication quality and enable devices adapt according to context changes, we implement a lightweight ML-based adaptive strategy (ASB) and deploy it using a memory-optimized approach over the designed Pycom FiPy based multi-protocol IoT hardware. In real-world ex-periments, ASB equipped FiPy board accurately predicted the RSSI of WiFi 4 & WiFi 5 in real-time and switched between protocols - demonstrating interoperability amongst multiple IoT communication protocols and resilience against communication breakdown.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128853819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1109/iccps54341.2022.00047
Kate Sanborn, Alex Richardson, J. Sprinkle
The goal of this paper is to automate the creation of naturalistic driving data sets of dash camera footage that is tagged with information captured from the vehicle's Controller Area Network (CAN) bus, using only a standard dash camera and CAN reader. The paper describes pairing and synchronizing dash camera videos with CAN bus data gathered from a vehicle with advanced driver assistance features. That data is then used to label the dash camera videos with telemetric information. Further, with the synchronized videos and CAN bus data, it is possible to identify video clips with meaningful events such as following a lead vehicle, cars passing in front of the vehicle, braking, turns, etc. This method of data-gathering and data set creation is significantly cheaper and more scalable than other driving data sets, while having competitive quality in terms of telemetric attributes. This could significantly increase the quantity, diversity, and in turn, quality of driving data sets in the future.
{"title":"Semantic Tagging of CAN and Dash Camera Data from Naturalistic Drives","authors":"Kate Sanborn, Alex Richardson, J. Sprinkle","doi":"10.1109/iccps54341.2022.00047","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00047","url":null,"abstract":"The goal of this paper is to automate the creation of naturalistic driving data sets of dash camera footage that is tagged with information captured from the vehicle's Controller Area Network (CAN) bus, using only a standard dash camera and CAN reader. The paper describes pairing and synchronizing dash camera videos with CAN bus data gathered from a vehicle with advanced driver assistance features. That data is then used to label the dash camera videos with telemetric information. Further, with the synchronized videos and CAN bus data, it is possible to identify video clips with meaningful events such as following a lead vehicle, cars passing in front of the vehicle, braking, turns, etc. This method of data-gathering and data set creation is significantly cheaper and more scalable than other driving data sets, while having competitive quality in terms of telemetric attributes. This could significantly increase the quantity, diversity, and in turn, quality of driving data sets in the future.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122340720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Uncertainty in safety-critical cyber-physical systems can be modeled using a finite number of parameters or input signals. Given a system specification in Signal Temporal Logic (STL), we would like to verify that for all (infinite) values of the model parameters/input signals, the system satisfies its specification. Unfortunately, this problem is undecidable in general. Statistical model checking (SMC) offers a solution by providing guarantees on the correctness of CPS models by statistically reasoning on model simulations. We propose a new approach for statistical verification of CPS models for user-provided distribution on the model parameters. Our technique uses model simulations to learn surrogate models, and uses conformal inference to provide probabilistic guarantees on the satisfaction of a given STL property. Additionally, we can provide prediction intervals containing the quantitative satisfaction values of the given STL property for any user-specified confidence level. We also propose a refinement procedure based on Gaussian Process (GP)-based surrogate models for obtaining fine-grained probabilistic guarantees over sub-regions in the parameter space. This in turn enables the CPS designer to choose assured validity domains in the parameter space for safety-critical applications. Finally, we demonstrate the efficacy of our technique on several CPS models.
{"title":"Statistical Verification of Cyber-Physical Systems using Surrogate Models and Conformal Inference","authors":"Xin Qin, Yuan Xian, Aditya Zutshi, Chuchu Fan, Jyotirmoy V. Deshmukh","doi":"10.1109/iccps54341.2022.00017","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00017","url":null,"abstract":"Uncertainty in safety-critical cyber-physical systems can be modeled using a finite number of parameters or input signals. Given a system specification in Signal Temporal Logic (STL), we would like to verify that for all (infinite) values of the model parameters/input signals, the system satisfies its specification. Unfortunately, this problem is undecidable in general. Statistical model checking (SMC) offers a solution by providing guarantees on the correctness of CPS models by statistically reasoning on model simulations. We propose a new approach for statistical verification of CPS models for user-provided distribution on the model parameters. Our technique uses model simulations to learn surrogate models, and uses conformal inference to provide probabilistic guarantees on the satisfaction of a given STL property. Additionally, we can provide prediction intervals containing the quantitative satisfaction values of the given STL property for any user-specified confidence level. We also propose a refinement procedure based on Gaussian Process (GP)-based surrogate models for obtaining fine-grained probabilistic guarantees over sub-regions in the parameter space. This in turn enables the CPS designer to choose assured validity domains in the parameter space for safety-critical applications. Finally, we demonstrate the efficacy of our technique on several CPS models.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121620474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1109/iccps54341.2022.00036
Ryan Silva, Cameron Hickert, Nicolas Sarfaraz, Jeff Brush, Joshua Silbermann, Tamim I. Sookoor
Achieving agile and resilient autonomous capabilities for cyber defense requires moving past indicators and situational awareness into automated response and recovery capabilities. The objective of the AlphaSOC project is to use state of the art sequential decision-making methods to automatically investigate and mitigate attacks on cyber physical systems (CPS). To demonstrate this, we developed a simulation environment that models the distributed navigation control system and physics of a large ship with two rudders and thrusters for propulsion. Defending this control network requires processing large volumes of cyber and physical signals to coordi-nate defensive actions over many devices with minimal disruption to nominal operation. We are developing a Reinforcement Learning (RL)-based approach to solve the resulting sequential decision-making problem that has large observation and action spaces.
{"title":"AlphaSOC: Reinforcement Learning-based Cybersecurity Automation for Cyber-Physical Systems","authors":"Ryan Silva, Cameron Hickert, Nicolas Sarfaraz, Jeff Brush, Joshua Silbermann, Tamim I. Sookoor","doi":"10.1109/iccps54341.2022.00036","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00036","url":null,"abstract":"Achieving agile and resilient autonomous capabilities for cyber defense requires moving past indicators and situational awareness into automated response and recovery capabilities. The objective of the AlphaSOC project is to use state of the art sequential decision-making methods to automatically investigate and mitigate attacks on cyber physical systems (CPS). To demonstrate this, we developed a simulation environment that models the distributed navigation control system and physics of a large ship with two rudders and thrusters for propulsion. Defending this control network requires processing large volumes of cyber and physical signals to coordi-nate defensive actions over many devices with minimal disruption to nominal operation. We are developing a Reinforcement Learning (RL)-based approach to solve the resulting sequential decision-making problem that has large observation and action spaces.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116145010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1109/iccps54341.2022.00031
Aqsa Kashaf, V. Sekar, Yuvraj Agarwal
Many smart home frameworks use applications to automate devices in a smart home. When these applications interact in the same environment, they may cause unintended actions which can lead to a safety violation (e.g., the door is unlocked when the user is not at home). While recent efforts have attempted to address this problem, they do not capture complex app behaviors such as: 1) timed behavior and user inputs (e.g., a door can remain unlocked for a long time because of a lock-door app that locks the door after x duration, if x is set too large.) and 2) interactions between devices and the environment they implicitly affect (e.g., water sprinklers cannot be turned on if the water supply is off). Hence, prior work leads to many false positives and false negatives. In this paper, we present PSA, a practical framework to identify safety intent violations in a smart home. PSA uses parameterized timed automata (PTA) as an expressive abstraction to model smart apps. To parse these apps into PTA, we define mappings from smart app APIs to equivalent PTA primitives. We also provide toolkits to model devices, environments, and their interactions. We evaluate PSA on 86 apps in the Samsung SmartThings IoT ecosystem. We compare PSA against two state-of-the-art baselines and find: (a) 19 new intent violations and (b) 35% fewer false positives than baselines.
{"title":"Protecting Smart Homes from Unintended Application Actions","authors":"Aqsa Kashaf, V. Sekar, Yuvraj Agarwal","doi":"10.1109/iccps54341.2022.00031","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00031","url":null,"abstract":"Many smart home frameworks use applications to automate devices in a smart home. When these applications interact in the same environment, they may cause unintended actions which can lead to a safety violation (e.g., the door is unlocked when the user is not at home). While recent efforts have attempted to address this problem, they do not capture complex app behaviors such as: 1) timed behavior and user inputs (e.g., a door can remain unlocked for a long time because of a lock-door app that locks the door after x duration, if x is set too large.) and 2) interactions between devices and the environment they implicitly affect (e.g., water sprinklers cannot be turned on if the water supply is off). Hence, prior work leads to many false positives and false negatives. In this paper, we present PSA, a practical framework to identify safety intent violations in a smart home. PSA uses parameterized timed automata (PTA) as an expressive abstraction to model smart apps. To parse these apps into PTA, we define mappings from smart app APIs to equivalent PTA primitives. We also provide toolkits to model devices, environments, and their interactions. We evaluate PSA on 86 apps in the Samsung SmartThings IoT ecosystem. We compare PSA against two state-of-the-art baselines and find: (a) 19 new intent violations and (b) 35% fewer false positives than baselines.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116762544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1109/iccps54341.2022.00052
Edward J. Kim, Jay Shenoy, Sebastian Junges, Daniel J. Fremont, A. Sangiovanni-Vincentelli, S. Seshia
Simulation-based testing is becoming a core element of assessing the safety of autonomous vehicles (AVs) by government and industry. For example, the National Highway Traffic Safety Administration stated that self-driving technology should be tested in simulation before deployment [1], and Waymo recently used simulation to sup-port the claim that self-driving cars are safer than human drivers [2]. A number of open-source simulation environments designed to sup-port automated AV testing are available [3]–[5], as well as simulators which focus on realistic rendering of specific types of sensors such as LiDAR and radar [6], [7]. There are also a variety of black-box and white-box techniques to search for failure scenarios causing an AV to violate its safety specifications (e.g. [8]–[13]).
{"title":"Demo: Querying Labelled Data with Scenario Programs for Sim-to-Real Validation","authors":"Edward J. Kim, Jay Shenoy, Sebastian Junges, Daniel J. Fremont, A. Sangiovanni-Vincentelli, S. Seshia","doi":"10.1109/iccps54341.2022.00052","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00052","url":null,"abstract":"Simulation-based testing is becoming a core element of assessing the safety of autonomous vehicles (AVs) by government and industry. For example, the National Highway Traffic Safety Administration stated that self-driving technology should be tested in simulation before deployment [1], and Waymo recently used simulation to sup-port the claim that self-driving cars are safer than human drivers [2]. A number of open-source simulation environments designed to sup-port automated AV testing are available [3]–[5], as well as simulators which focus on realistic rendering of specific types of sensors such as LiDAR and radar [6], [7]. There are also a variety of black-box and white-box techniques to search for failure scenarios causing an AV to violate its safety specifications (e.g. [8]–[13]).","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131275042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1109/iccps54341.2022.00049
Nilotpal Chakraborty, Roshni Chakraborty, E. Kalaimannan
In this paper, we take up the problem of scheduling flexible devices, which can be operated at different power levels, having different power and timing requirements, under the constraint of peak load demand to minimize the overall finishing time. We present a formal mathematical programming formulation and have proposed effi-cient heuristic algorithm to solve the problem efficiently for larger systems.
{"title":"Scheduling Energy Flexible Devices Under Constrained Peak Load Consumption in Smart Grid","authors":"Nilotpal Chakraborty, Roshni Chakraborty, E. Kalaimannan","doi":"10.1109/iccps54341.2022.00049","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00049","url":null,"abstract":"In this paper, we take up the problem of scheduling flexible devices, which can be operated at different power levels, having different power and timing requirements, under the constraint of peak load demand to minimize the overall finishing time. We present a formal mathematical programming formulation and have proposed effi-cient heuristic algorithm to solve the problem efficiently for larger systems.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130864226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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