| 1. |
- Cheng, Xiaogang, et al.
(författare)
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A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning
- 2019
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417 .- 1454-5101. ; 9:7
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Tidskriftsartikel (refereegranskat)abstract
- Featured Application The NISDL method proposed in this paper can be used for real time contactless measuring of human skin temperature, which reflects human body thermal comfort status and can be used for control HVAC devices. Abstract In human-centered intelligent building, real-time measurements of human thermal comfort play critical roles and supply feedback control signals for building heating, ventilation, and air conditioning (HVAC) systems. Due to the challenges of intra- and inter-individual differences and skin subtleness variations, there has not been any satisfactory solution for thermal comfort measurements until now. In this paper, a contactless measuring method based on a skin sensitivity index and deep learning (NISDL) was proposed to measure real-time skin temperature. A new evaluating index, named the skin sensitivity index (SSI), was defined to overcome individual differences and skin subtleness variations. To illustrate the effectiveness of SSI proposed, a two multi-layers deep learning framework (NISDL method I and II) was designed and the DenseNet201 was used for extracting features from skin images. The partly personal saturation temperature (NIPST) algorithm was use for algorithm comparisons. Another deep learning algorithm without SSI (DL) was also generated for algorithm comparisons. Finally, a total of 1.44 million image data was used for algorithm validation. The results show that 55.62% and 52.25% error values (NISDL method I, II) are scattered at (0 degrees C, 0.25 degrees C), and the same error intervals distribution of NIPST is 35.39%.
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| 2. |
- Gaspar Sánchez, José Manuel, et al.
(författare)
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Edge computing for cyber-physical systems : A Systematic Mapping Study Emphasizing Trustworthiness
- 2022
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Ingår i: ACM Transactions on Cyber-Physical Systems. - : Association for Computing Machinery (ACM). - 2378-962X .- 2378-9638. ; 6:3, s. 1-28
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Tidskriftsartikel (refereegranskat)abstract
- Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Edge computing is today driven by strong market forces stemming from IT/cloud, telecom, and networking - with corresponding multiple interpretations of ”edge computing” (e.g. device edge, network edge, distributed cloud, etc.). Considering the strong drivers for edge-computing and the relative novelty of the field, it becomes important to understand the specific requirements and characteristics of edge-based CPS, and to ensure that research is guided adequately, e.g. avoiding specific gaps.Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS including their experimental evaluation. Such research needs to consider the multiple interrelated attributes of trustworthiness including safety, security, and predictability, and new methodologies and architectures to address them. It is further important to provide bridges and collaboration between edge computing and CPS disciplines.
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| 3. |
- Ji, Qinglei, et al.
(författare)
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Synthesizing the optimal gait of a quadruped robot with soft actuators using deep reinforcement learning
- 2022
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier. - 0736-5845 .- 1879-2537. ; 78, s. 102382-102382
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Tidskriftsartikel (refereegranskat)abstract
- Quadruped robots have the advantages of traversing complex terrains that are difficult for wheeled robots. Most of the reported quadruped robots are built by rigid parts. This paper proposes a new design of quadruped robots using soft actuators driven by tendons as the four legs. Compared to the rigid robots, the proposed soft quadruped robot has inherent safety, less weight and simpler mechanism for fabrication and control, but the corresponding challenge is that the accurate mathematical model applicable to model-based control design of the soft robot is difficult to derive by dynamics. To synthesize the optimal gait controller of the soft-legged robot, the paper makes the following contributions. First, the flexible components of the quadruped robot are modeled with different finite element and lumped parameter methods. The model accuracy and computation efficiency are analyzed. Second, soft actor–critic methods and curriculum learning are applied to learn the optimal gaits for different walking tasks. Third, The learned gaits are implemented in an in-house robot to transport hand tools. Preliminary results show that the robot can walk forward and correct the walking directions.
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| 4. |
- Liu, Tong, PhD Candidate, et al.
(författare)
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A Low-Complexity and High-Performance Energy Management Strategy of a Hybrid Electric Vehicle by Model Approximation
- 2022
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Ingår i: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). - Mexico City, Mexico : IEEE. ; , s. 455-462
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Konferensbidrag (refereegranskat)abstract
- The fuel economy of a hybrid electric vehicle(HEV) is determined by its energy management strategy (EMS), while the conventional EMS usually suffers from enormous computation loads when solving a nonlinear optimization problem. To resolve this issue, this paper presents a computationally efficient EMS with close-to-optimal performance using very limited computation resources. Relying on the optimal solutions by offline dynamic programming (DP), a constrained model predictive control (MPC) can quickly determine the engine on/off status and then the torque split problem is solved by a value-based Pontryagin’s minimum principle (PMP). Two measures are taken to further reduce the online computation cost: by surface fitting, the tabular value function is replaced by piecewise linear polynomials and thus the memory occupation is greatly reduced; and by model approximation, the nonlinear torque split problem becomes a quadratic programming one that can be more rapidly solved. The testing results from processor-in-the-loop (PIL) simulation indicate that the proposed EMS can generate a fuel efficiency close to the one by DP, but saves 70% onboard memory space and 30% CPU utilization compared with the benchmark EMS without taking the two measures.
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| 5. |
- Liu, Tong, PhD Candidate, et al.
(författare)
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Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming
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Tidskriftsartikel (refereegranskat)abstract
- Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This paper presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin’s minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and then has higher fuel efficiency than a non-adaptive dynamic programming (DP)controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The AVF data structure enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization.
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| 6. |
- Liu, Tong, PhD Candidate, et al.
(författare)
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Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming
- 2024
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 9:2, s. 4085-4099
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Tidskriftsartikel (refereegranskat)abstract
- Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This paper presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin's minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via the parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and thus has higher fuel efficiency than a non-adaptive dynamic programming (DP) controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The concise data structure of the AVF enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization.
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| 7. |
- Liu, Tong, PhD Candidate, et al.
(författare)
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Optimal and Adaptive Engine Switch Control for a Parallel Hybrid Electric Vehicle Using a Computationally Efficient Actor-Critic Method
- 2023
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Ingår i: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 416-423
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Konferensbidrag (refereegranskat)abstract
- Energy management strategies (EMSs) are crucial to the fuel economy of hybrid electric vehicles (HEVs). However, due to the lack of efficient solving approaches, most of existing EMSs mainly focus on the optimal torque split between the internal combustion engine (ICE) and the electric motor but neglect improper ICE on/off switches, and thus usually suffer degraded fuel economy and even unacceptable drivability in practice. To tackle this issue, this paper presents a novel EMS that uses an efficient actor-critic (AC) method to regulate ICE switches with limited computation resources. While common AC methods use complex neural networks (NNs) with arbitrary initialization, the proposed AC uses piecewise cubic polynomials whose parameters are initialized based on optimized solutions of dynamic programming (DP). By this means, the AC can quickly converge with high computation efficiency. The testing results from processor-in-the-loop (PIL) simulations showcase that, compared with a rule-based EMS with tabular value functions, the proposed EMS can greatly improve the equivalent fuel economy by eliminating improper ICE switches after only several iterations of adaptive learning and dramatically save onboard memory space owing to the concise AC structure.
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| 8. |
- Song, Qunying, et al.
(författare)
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An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software
- 2021
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Ingår i: Proceedings - 3rd IEEE International Conference on Artificial Intelligence Testing, AITest 2021. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 81-82
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Konferensbidrag (refereegranskat)abstract
- Testing of autonomous vehicles involves enormous challenges for the automotive industry. The number of real-world driving scenarios is extremely large, and choosing effective test scenarios is essential, as well as combining simulated and real world testing. We present an industrial workbench of tools and workflows to generate efficient and effective test scenarios for active safety and autonomous driving functions. The workbench is based on existing engineering tools, and helps smoothly integrate simulated testing, with real vehicle parameters and software. We aim to validate the workbench with real cases and further refine the input model parameters and distributions.
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| 9. |
- Song, Qunying, et al.
(författare)
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Critical scenario identification for realistic testing of autonomous driving systems
- 2023
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Ingår i: Software quality journal. - : Springer Nature. - 0963-9314 .- 1573-1367. ; 31:2, s. 441-469
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Tidskriftsartikel (refereegranskat)abstract
- Autonomous driving has become an important research area for road traffic, whereas testing of autonomous driving systems to ensure a safe and reliable operation remains an open challenge. Substantial real-world testing or massive driving data collection does not scale since the potential test scenarios in real-world traffic are infinite, and covering large shares of them in the test is impractical. Thus, critical ones have to be prioritized. We have developed an approach for critical test scenario identification and in this study, we implement the approach and validate it on two real autonomous driving systems from industry by integrating it into their tool-chain. Our main contribution in this work is the demonstration and validation of our approach for critical scenario identification for testing real autonomous driving systems.
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| 10. |
- Tan, Kaige, et al.
(författare)
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Collaborative Collision Avoidance of Connected Vehicles Using ADMM with PI-Regulated Lagrangian Multipliers
- 2023
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Ingår i: 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- The decentralized approach is popular for the collaborative collision avoidance of connected vehicles in many scenarios. By modeling the task as a collaborative optimal control problem, Lagrangian methods are widely used to decouple the constraints and enable the decentralized solution. However, potential constraint-violating behavior will lead to oscillations during the Lagrangian update, resulting in more iterations and lower real-time efficiency. Existing methods generally neither address this shortcoming, nor explore the Lagrangian update mechanism. This study takes a control perspective, and solves this collaborative optimal control problem based on an extension of the Alternating Directions Method of Multipliers (ADMM) algorithm by performing the iteration update with a Proportional-Integral-(PI-) regulated controller. The link between the Lagrangian optimization and the PI controller improves the convergence performance during iterations. Simulation results in traffic intersection scenarios demonstrate the advantage of the proposed approach.
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| 11. |
- Tan, Kaige, et al.
(författare)
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Decentralized Convex Optimization for Joint Task Offloading and Resource Allocation of Vehicular Edge Computing Systems
- 2022
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Ingår i: IEEE Transactions on Vehicular Technology. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9545 .- 1939-9359. ; , s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- Vehicular Edge Computing (VEC) systems exploit resources on both vehicles and Roadside Units (RSUs) to provide services for real-time vehicular applications that cannot be completed in the vehicles alone. Two types of decisions are critical for VEC: one is for task offloading to migrate vehicular tasks to suitable RSUs, and the other is for resource allocation at the RSUs to provide the optimal amount of computational resource to the migrated tasks under constraints on response time and energy consumption. Most of the published optimization-based methods determine the optimal solutions of the two types of decisions jointly within one optimization problem at RSUs, but the complexity of solving the optimization problem is extraordinary, because the problem is not convex and has discrete variables. Meanwhile, the nature of centralized solutions requires extra information exchange between vehicles and RSUs, which is challenged by the additional communication delay and security issues. The contribution of this paper is to decompose the joint optimization problem into two decoupled subproblems: task offloading and resource allocation. Both subproblems are reformulated for efficient solutions. The resource allocation problem is simplified by dual decomposition and can be solved at vehicles in a decentralized way. The task offloading problem is transformed from a discrete problem to a continuous convex one by a probability-based solution. Our new method efficiently achieves a near-optimal solution through decentralized optimizations, and the error bound between the solution and the true optimum is analyzed. Simulation results demonstrate the advantage of the proposed approach.
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| 12. |
- Tan, Kaige, et al.
(författare)
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Edge-enabled Adaptive Shape Estimation of 3D Printed Soft Actuators with Gaussian Processes and Unscented Kalman Filters
- 2023
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Ingår i: IEEE Transactions on Industrial Electronics. - : IEEE. - 0278-0046 .- 1557-9948. ; , s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- Soft actuators have the advantages of compliance and adaptability when working with vulnerable objects, but the deformation shape of the soft actuators is difficult to measure or estimate. Soft sensors made of highly flexible and responsive materials are promising new approaches to the shape estimation of soft actuators, but suffer from highly nonlinear, hysteresis, and time-variant properties. A nonlinear and adaptive state observer is essential for the shape estimation from soft sensors. Current state estimation methods rely on complex nonlinear data-fitting models, and the robustness of the estimation methods is questionable. This study investigates the soft actuator dynamics and the soft sensor model as a stochastic process characterized by the Gaussian Process (GP) model. The unscented Kalman filter (UKF) is applied to the GP model for more reliable variance adjustment during the sequential state estimation process than conventional methods. In addition, a major limitation of the GP model is its computational complexity during online inference. To improve the real-time performance while guaranteeing accuracy, we introduce an edge server to decrease the onboard computational and memory overhead. The experiments showcase a significant improvement in estimation accuracy and real-time performance compared to baseline methods.
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| 13. |
- Tan, Kaige, et al.
(författare)
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Shape Estimation of a 3D Printed Soft Sensor Using Multi-Hypothesis Extended Kalman Filter
- 2022
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766 .- 2377-3774. ; 7:3, s. 8383-8390
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Tidskriftsartikel (refereegranskat)abstract
- This study develops a multi-hypothesis extended Kalman filter (MH-EKF) for the online estimation of the bending angle of a 3D printed soft sensor attached to soft actuators. Despite the advantage of compliance and low interference, the 3D printed soft sensor is susceptible to the hysteresis property and nonlinear effects. Improving measurement accuracy for sensors with hysteresis is a common challenge. Current studies mainly apply complex models and highly nonlinear functions to characterize the hysteresis, requiring a complicated parameter identification process and challenging real-time applications. This study enhances the model simplicity and the real-time performance for the hysteresis characterization. We identify the hysteresis by combining multiple polynomial functions and improving the sensor estimation with the proposed MH-EKF. We examine the performance of the filter in the real-time closed-loop control system. Compared with the baseline methods, the proposed approach shows improvements in the estimation accuracy with low computational complexity.
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| 14. |
- Yang, Junjun, et al.
(författare)
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A model-based deep reinforcement learning approach to the nonblocking coordination of modular supervisors of discrete event systems
- 2023
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Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 630, s. 305-321
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Tidskriftsartikel (refereegranskat)abstract
- Modular supervisory control may lead to conflicts among the modular supervisors for large-scale discrete event systems. The existing methods for ensuring nonblocking control of modular supervisors either exploit favorable structures in the system model to guarantee the nonblocking property of modular supervisors or employ hierarchical model abstraction methods for reducing the computational complexity of designing a nonblocking coordinator. The nonblocking modular control problem is, in general, NP-hard. This study integrates supervisory control theory and a model-based deep reinforcement learning method to synthesize a nonblocking coordinator for the modular supervisors. The deep reinforcement learning method significantly reduces the computational complexity by avoiding the computation of synchronization of multiple modular supervisors and the plant models. The supervisory control function is approximated by the deep neural network instead of a large-sized finite automaton. Furthermore, the proposed model-based deep reinforcement learning method is more efficient than the standard deep Q network algorithm.
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| 15. |
- Yang, Junjun, et al.
(författare)
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Reducing the Learning Time of Reinforcement Learning for the Supervisory Control of Discrete Event Systems
- 2023
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 11, s. 59840-59853
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Tidskriftsartikel (refereegranskat)abstract
- Reinforcement learning (RL) can obtain the supervisory controller for discrete-event systems modeled by finite automata and temporal logic. The published methods often have two limitations. First, a large number of training data are required to learn the RL controller. Second, the RL algorithms do not consider uncontrollable events, which are essential for supervisory control theory (SCT). To address the limitations, we first apply SCT to find the supervisors for the specifications modeled by automata. These supervisors remove illegal training data violating these specifications and hence reduce the exploration space of the RL algorithm. For the remaining specifications modeled by temporal logic, the RL algorithm is applied to search for the optimal control decision within the confined exploration space. Uncontrollable events are considered by the RL algorithm as uncertainties in the plant model. The proposed method can obtain a nonblocking supervisor for all specifications with less learning time than the published methods.
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| 16. |
- Zhang, Xinhai, et al.
(författare)
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Finding critical scenarios for automated driving systems : The data extraction form
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This is the data extraction form for the systematic literature review work for finding critical scenarios for automated driving systems. The extracted data from the primary studies is structured in the following tables. Primary studies in Tables 1 to 5 correspond to the five clusters defined in Section 6 of the main paper. Please note that some primary studies in these tables are classified as out of the scope of the literature study. These studies are marked in the Purpose column. Primary studies in Tables 6 and 7 are eventually considered as out of the scope. The tables are designed aligned with the taxonomy proposed in Section 4 of the main paper.
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| 17. |
- Zhang, Xinhai, et al.
(författare)
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Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study
- 2022
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Ingår i: IEEE Transactions on Software Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 0098-5589 .- 1939-3520 .- 2326-3881. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.
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