| 1. |
- Chen, Xiaochang, et al.
(författare)
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Automatic Overtaking on Two-way Roads with Vehicle Interactions Based on Proximal Policy Optimization
- 2021
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Ingår i: 2021 32nd IEEE Intelligent Vehicles Symposium (IV). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1057-1064
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Konferensbidrag (refereegranskat)abstract
- Overtaking the lead vehicle on two-way roads in the presence of several oncoming vehicles is a complex task for autonomous vehicles. In this paper, we formulate the overtaking behavior of an ego vehicle based on a deep reinforcement learning (DRL) method. First, a two-way urban road is created, wherein the ego vehicle aims to reach the destination safely and efficiently while considering multiple traffic participants. We use different intelligent driver model (IDM) parameters to account for different drivers' habits. Furthermore, we introduce different responses of other vehicles when the ego vehicle takes overtaking maneuver. Then, a hierarchical control framework is proposed to manage vehicles on the road, which supervises vehicle behaviors at the high layer and controls the motion at the lower layer. The DRL method named Proximal Policy Optimization is applied to derive the high-level decision-making policies. A self-attention mechanism is further introduced to improve the performance of our algorithm. Finally, the overtaking maneuvers of the ego vehicle in different training timesteps are analyzed and how the responses of other vehicles affect the ego one's overtaking behavior is investigated. Simulation results show that our approach can achieve good performance to deal with the two-way road autonomous overtaking task. Supplementary video is available at https://youtu.be/jPEGjM7cBuk.
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| 2. |
- Ding, Kemi, et al.
(författare)
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Defensive deception against reactive jamming attacks in remote state estimation
- 2020
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 113
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Tidskriftsartikel (refereegranskat)abstract
- This paper considers a synthetic counter-measure, combining transmission scheduling and defensive deception, to defend against jamming attacks in remote state estimation. In the setup studied, an attacker sabotages packet transmissions from a sensor to a remote estimator by congesting the communication channel between them. In order to efficiently degrade the estimation accuracy, the intelligent attacker tailors its jamming strategy by reacting to the real-time information it collects. In response to the jamming attacks, the sensor with a long-term goal will select the transmission power level at each stage. In addition, by modifying the real-time information intentionally, the sensor creates asymmetric uncertainty to mislead the attacker and thus mitigate attacks. Considering the dynamic nature of the process, we model the strategic interaction between the sensor and the attacker by a general stochastic game with asymmetric information structure. To obtain stationary optimal strategies for each player, we convert this game into a belief-based dynamic game and analyze the existence of its optimal solution. For a tractable implementation, we present an algorithm that finds equilibrium strategies based on multi-agent reinforcement learning for symmetric-information stochastic games. Numerical examples illustrate properties of the proposed algorithm.
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| 3. |
- Gao, Yulong, et al.
(författare)
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Reachability-based Human-in-the-Loop Control with Uncertain Specifications
- 2020
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Ingår i: IFAC PAPERSONLINE. - : Elsevier BV. - 2405-8963. ; , s. 1880-1887
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Konferensbidrag (refereegranskat)abstract
- We propose a shared autonomy approach for implementing human operator decisions onto an automated system during multi-objective missions, while guaranteeing safety and mission completion. A mission is specified as a set of linear temporal logic (LTL) formulae. Then, using a novel correspondence between LTL and reachability analysis, we synthesize a set of controllers for assisting the human operator to complete the mission, while guaranteeing that the system maintains specified spatial and temporal properties. We assume the human operator's exact preference of how to complete the mission is unknown. Instead, we use a datadriven approach to infer and update the automated system's internal belief of which specified objective the human intends to complete. If, while the human is operating the system, she provides inputs that violate any of the invariances prescribed by the LTL formula, our verified controller will use its internal belief of the human operator's intended objective to guide the operator back on track. Moreover, we show that as long as the specifications are initially feasible, our controller will stay feasible and can guide the human to complete the mission despite some unexpected human errors. We illustrate our approach with a simple, but practical, experimental setup where a remote operator is parking a vehicle in a parking lot with multiple parking options. In these experiments, we show that our approach is able to infer the human operator's preference over parking spots in real-time and guarantee that the human will park in the spot safely.
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| 4. |
- He, Xingkang, et al.
(författare)
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Secure Distributed Filtering for Unstable Dynamics Under Compromised Observations
- 2019
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Ingår i: 2019 IEEE 58th Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 5344-5349
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Konferensbidrag (refereegranskat)abstract
- In this paper, we consider a secure distributed filtering problem for linear time-invariant systems with bounded noises and unstable dynamics under compromised observations. A malicious attacker is able to compromise a subset of the agents and manipulate the observations arbitrarily. We first propose a recursive distributed filter consisting of two parts at each time. The first part employs a saturation like scheme, which gives a small gain if the innovation is too large. The second part is a consensus operation of state estimates among neighboring agents. A sufficient condition is then established for the boundedness of estimation error, which is with respect to network topology, system structure, and the maximal compromised agent subset. We further provide an equivalent statement, which connects to 2s-sparse observability in the centralized framework in certain scenarios, such that the sufficient condition is feasible. Numerical simulations are finally provided to illustrate the developed results.
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| 5. |
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| 6. |
- Ren, Xiaoqiang, et al.
(författare)
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Infinite Horizon Optimal Transmission Power Control for Remote State Estimation Over Fading Channels
- 2018
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Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286 .- 1558-2523. ; 63:1, s. 85-100
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies the joint design over an infinite horizon of the transmission power controller and remote estimator for state estimation over fading channels. A sensor observes a dynamic process and sends its observations to a remote estimator over a wireless fading channel characterized by a time-homogeneous Markov chain. The successful transmission probability depends on both the channel gains and the transmission power used by the sensor. The transmission power control rule and the remote estimator should be jointly designed, aiming to minimize an infinite-horizon cost consisting of the power usage and the remote estimation error. We formulate the joint optimization problem as an average cost belief-state Markov decision process and prove that there exists an optimal deterministic and stationary policy. We then show that when the monitored dynamic process is scalar or the system matrix is orthogonal, the optimal remote estimates depend only on the most recently received sensor observation, and the optimal transmission power is symmetric and monotonically increasing with respect to the norm of the innovation error.
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| 7. |
- Ren, Xiaoqiang, et al.
(författare)
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Quickest Change Detection in Adaptive Censoring Sensor Networks
- 2018
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Ingår i: IEEE Transactions on Control of Network Systems. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2325-5870. ; 5:1, s. 239-250
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Tidskriftsartikel (refereegranskat)abstract
- The problem of quickest change detection with communication rate constraints is studied. A network of wireless sensors with limited computation capability monitors the environment and sends observations to a fusion center via wireless channels. At an unknown time instant, the distributions of observations at all the sensor nodes change simultaneously. Due to limited energy, the sensors cannot transmit at all the time instants. The objective is to detect the change at the fusion center as quickly as possible, subject to constraints on false detection and average communication rate between the sensors and the fusion center. A minimax formulation is proposed. The cumulative sum (CuSum) algorithm is used at the fusion center and censoring strategies are used at the sensor nodes. The censoring strategies, which are adaptive to the CuSum statistic, are fed back by the fusion center. The sensors only send observations that fall into prescribed sets to the fusion center. This CuSum adaptive censoring (CuSum-AC) algorithm is proved to be an equalizer rule and to be globally asymptotically optimal for any positive communication rate constraint, as the average run length to false alarm goes to infinity. It is also shown, by numerical examples, that the CuSum-AC algorithm provides a suitable trade-off between the detection performance and the communication rate.
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| 8. |
- Ren, Xiaoqiang, et al.
(författare)
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Quickest Change Detection With Observation Scheduling
- 2017
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE. - 0018-9286 .- 1558-2523. ; 62:6, s. 2635-2647
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Tidskriftsartikel (refereegranskat)abstract
- The quickest change detection problem is to detect an abrupt change event as quickly as possible subject to constraints on false detection. Unlike the classical problem, where the decision maker can access only one sequence of observations, in this paper, the decision maker chooses one of two different sequences of observations at each time instant. The information quality and sampling cost of the two sequences of observations are different. We present an asymptotically optimal joint design of observation scheduling policy and stopping time such that the detection delay is minimized subject to constraints on both average run length to false alarm (ARLFA) and average cost per sample. The observation scheduling policy has a threshold structure and the detection scheme is a variant of the cumulative sum test where the detection statistic stochastically crosses the threshold that is used to switch observation modes. We further study the decentralized case in a multi-channel setting. We show that if each sensor uses the proposed observation scheduling policy locally and the fusion center uses the Nsum algorithm, by which the center declares the change when the sum of the sensors' local detection statistics crosses a certain threshold, the detection delay is asymptotically minimized for any possible combination of the affected sensors subject to constraints on both global ARLFA and average cost per sample at each sensor node. Numerical examples are given to illustrate the main results.
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| 9. |
- Ren, Xiaoqiang, et al.
(författare)
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Secure Detection : Performance Metric and Sensor Deployment Strategy
- 2018
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 66:17, s. 4450-4460
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies how to deploy sensors in the context of detection in adversarial environments. A fusion center is performing a binary hypothesis testing based on measurements from remotely deployed heterogeneous sensors. An attacker may compromise some of the deployed sensors, which send arbitrary measurements to the fusion center. The problems of interest are: to characterize the performance of the system under attack and, thus, develop a performance metric; and to deploy sensors within a cost budget, such that the proposed performance metric is maximized. In this paper, we first present a performance metric by formulating the detection in adversarial environments in a game theoretic way. A Nash equilibrium pair of the detection algorithm and attack strategy, with the deployed sensors given, is provided and the corresponding detection performance is adopted as the performance metric. We then show that the optimal sensor deployment can be determined approximately by solving a group of unbounded knapsack problems. We also show that the performance metric gap between the optimal sensor deployment and the optimal one with sensors being identical is within a fixed constant for any cost budget. The main results are illustrated by numerical examples.
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| 10. |
- Ren, Xiaoqiang, et al.
(författare)
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Secure State Estimation With Byzantine Sensors : A Probabilistic Approach
- 2020
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE. - 0018-9286 .- 1558-2523. ; 65:9, s. 3742-3757
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Tidskriftsartikel (refereegranskat)abstract
- This article studies static state estimation in multisensor settings, with a caveat that an unknown subset of the sensors are compromised by an adversary, whose measurements can be manipulated arbitrarily. The attacker is able to compromise q out of m sensors. A new performance metric, which quantifies the asymptotic decay rate for the probability of having an estimation error larger than delta, is proposed. We develop an optimal estimator for the new performance metric with a fixed delta, which is the Cheby-shev center of a union of ellipsoids. We further provide an estimator that is optimal for every delta, for the special case where the sensors are homogeneous. Numerical examples are given to elaborate the results.
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