Motion Uncertainty Research Articles

Optimal Probabilistic Motion Planning With Potential Infeasible LTL Constraints

This paper studies optimal motion planning subject to motion and environment uncertainties. By modeling the system as a probabilistic labeled Markov decision process (PL-MDP), the control objective is to synthesize a finite-memory policy, under which the agent satisfies complex high-level tasks expressed as linear temporal logic (LTL) with desired satisfaction probability. In particular, the cost optimization of the trajectory that satisfies infinite horizon tasks is considered, and the trade-off between reducing the expected mean cost and maximizing the probability of task satisfaction is analyzed. The LTL formulas are converted to limit-deterministic Büchi automata (LDBA) with a reachability acceptance condition and a compact graph structure. The novelty of this work lies in considering the cases where LTL specifications can be potentially infeasible and developing a relaxed product MDP between PL- MDP and LDBA. The relaxed product MDP allows the agent to revise its motion plan whenever the task is not fully feasible and quantify the revised plan’s violation measurement. A multi- objective optimization problem is then formulated to jointly consider the probability of task satisfaction, the violation with respect to original task constraints, and the implementation cost of the policy execution. The formulated problem can be solved via coupled linear programs. This work first bridges the gap between probabilistic planning revision of potential infeasible LTL specifications and optimal control synthesis of both plan prefix and plan suffix of the trajectory over the infinite horizons. Experimental results are provided to demonstrate the effectiveness of the proposed framework.

DragNet: Learning-based deformable registration for realistic cardiac MR sequence generation from a single frame.

Deformable image registration (DIR) can be used to track cardiac motion. Conventional DIR algorithms aim to establish a dense and non-linear correspondence between independent pairs of images. They are, nevertheless, computationally intensive and do not consider temporal dependencies to regulate the estimated motion in a cardiac cycle. In this paper, leveraging deep learning methods, we formulate a novel hierarchical probabilistic model, termed DragNet, for fast and reliable spatio-temporal registration in cine cardiac magnetic resonance (CMR) images and for generating synthetic heart motion sequences. DragNet is a variational inference framework, which takes an image from the sequence in combination with the hidden states of a recurrent neural network (RNN) as inputs to an inference network per time step. As part of this framework, we condition the prior probability of the latent variables on the hidden states of the RNN utilised to capture temporal dependencies. We further condition the posterior of the motion field on a latent variable from hierarchy and features from the moving image. Subsequently, the RNN updates the hidden state variables based on the feature maps of the fixed image and the latent variables. Different from traditional methods, DragNet performs registration on unseen sequences in a forward pass, which significantly expedites the registration process. Besides, DragNet enables generating a large number of realistic synthetic image sequences given only one frame, where the corresponding deformations are also retrieved. The probabilistic framework allows for computing spatio-temporal uncertainties in the estimated motion fields. Our results show that DragNet performance is comparable with state-of-the-art methods in terms of registration accuracy, with the advantage of offering analytical pixel-wise motion uncertainty estimation across a cardiac cycle and being a motion generator. We will make our code publicly available.

Probabilistic seismic design of pile-supported building structure incorporating inherent variability of subsoil and ground motion uncertainty

Probabilistic seismic design of pile-supported building structure incorporating inherent variability of subsoil and ground motion uncertainty

ROBUST OPTIMAL DESIGN BASED ON STORY DUCTILITY RATIO OF ELASTIC-PLASTIC BUILDING STRUCTURES WITH UNCERTAIN OIL DAMPERS SUBJECTED TO CRITICAL DOUBLE IMPULSE

A new robustness evaluation method based on the specified story ductility ratio is proposed for a building model with uncertain oil dampers. Since the influence of input motion uncertainty on the response variation can be simply taken into account, the critical double impulse input is used instead of input ground motions. In the proposed method, the robustness function is derived as a function of the input velocity amplitude for a specified ductility ratio. This procedure is suitable for the performance-based design. A robust optimal design method on damper placement is also presented based on the proposed new robustness function. Numerical examples of robust optimal design of dampers are presented for a 12-story elasto-plastic building model with linear oil dampers.

Predictive Trajectory Planning for On-Road Autonomous Vehicles Based on a Spatiotemporal Risk Field

Trajectory planning plays a crucial role in autonomous driving. It generates feasible geometric paths within a certain time horizon to guide autonomous vehicles. However, typical planning methods give little consideration to the motion uncertainty of traffic objects. Therefore, this article proposes a predictive trajectory planning method based on a spatiotemporal risk potential field. And the key notion of this method is to separate the coupled executions, i.e., trajectory generation and risk assessment, into two parallel and independent modules in the curvilinear coordinate. First, the current spatial risks are quantified by artificial potential-like functions. Then, we measure the probabilities of surrounding vehicles driving toward each lane using an interacting multiple model structure. The temporal evolution of the spatial field is computed by incorporating both the probabilities and predicted trajectories. Meanwhile, the generation module focuses on providing sufficient trajectory candidates for the ego vehicle. To allocate computing resources reasonably, feasibility probabilities are calculated with a relational grid, and then candidates are biased sampled in lateral and longitudinal directions. Finally, taking into account driving safety, comfort, and efficiency, the planner selects an optimal trajectory by minimizing a well-designed cost function. In this way, the feasibility of planned trajectories can be greatly improved with limited time consumption. The performance of this proposed method is evaluated in various scenarios using a high-fidelity Unity3D environment.

Compound intensity measures for improved seismic performance assessment in cross-fault hydraulic tunnels using partial least-squares methodology

Seismic performance assessment of cross-fault hydraulic tunnels is typically expressed in terms of the probability of reaching or exceeding specific damage states related to earthquake intensity measures (IMs) and engineering demand parameters (EDPs). Obviously, this would require determining the appropriate earthquake IMs to reflect the seismological characteristics of ground motions and improving the predictive ability for the EDPs of cross-fault hydraulic tunnels. It is worth emphasising that an unreasonable IM may increase the ground motion uncertainty, which in turn introduces additional uncertainty to the EDP estimates of cross-fault hydraulic tunnels. Hence, this study presents a compound IM, integrating multiple earthquake IMs, to improve the reliability of the seismic fragility analysis of a cross-fault hydraulic tunnel based on the partial least squares methodology. To this end, seven scalar IMs are first determined from the 20 examined earthquake IMs as the fundamental element of the compound IM using four test criteria. Subsequently, a 1080 complete nonlinear dynamic time history analysis of the cross-fault hydraulic tunnel using the incremental dynamic analysis method is conducted to generate a large amount of data. The findings of this study highlight that the compound IM-based probabilistic seismic demand models of the cross-fault hydraulic tunnel incorporate various sources of uncertainties, which enhances the correlation, efficiency, practicality, and proficiency of probabilistic seismic demand analysis and eventually leads to a more reasonable estimation of probabilistic failure. Moreover, the potential of nonlinear polynomial regression of compound IM can further improve the correlation and efficiency of probabilistic seismic demand analysis. Relative to the classical fragility curves utilising a single optimum-IM peak ground velocity, the probability of damage to the cross-fault hydraulic tunnel under a particular damage state related to the compound IM is significantly larger than the corresponding single optimum-IM peak ground velocity. Similarly, the probability of damage to the cross-fault hydraulic tunnel under the Wenchuan seismic event is underestimated, derived from a single optimum-IM peak ground velocity. The abovementioned phenomena demonstrate that a compound IM based on the partial least squares methodology can be suggested for use in estimating the probability of structural damage in performance-based earthquake engineering.

Estimating fire following earthquake risk for Wellington City, New Zealand

Fire following earthquake (FFE) is a significant hazard in urban areas subject to high seismicity. Wellington City has many characteristics that make it susceptible to ignitions and fire spread. These include proximity to major active faults, closely spaced timber-clad buildings, vulnerable water and gas infrastructure, frequent high winds and challenging access for emergency services. We modelled the ignitions, fire spread and suppression for five earthquake sources. Uncertainty in ground motions, the number and location of ignitions, weather conditions and firefighting capacity were accounted for. The mean loss per burn zone (area burnt due to ignition and fire spread) is $46m without fire suppression, indicating the potential property damage avoided by controlling the fire spread. The mean total loss for earthquake scenarios ranges from $0.28b for the Wairau Fault through to $3.17b for a Hikurangi Subduction Zone scenario, including the influence of fire suppression. Wind speed has a strong influence on the potential losses for each simulation and is a more significant factor than the number of ignitions for evaluating losses. Areas in Wellington City of relatively high risk are identified, which may inform risk mitigation strategies. The models may be applied to other urban areas.

Deep Reinforcement Learning-Based End-to-End Control for UAV Dynamic Target Tracking.

Uncertainty of target motion, limited perception ability of onboard cameras, and constrained control have brought new challenges to unmanned aerial vehicle (UAV) dynamic target tracking control. In virtue of the powerful fitting ability and learning ability of the neural network, this paper proposes a new deep reinforcement learning (DRL)-based end-to-end control method for UAV dynamic target tracking. Firstly, a DRL-based framework using onboard camera image is established, which simplifies the traditional modularization paradigm. Secondly, neural network architecture, reward functions, and soft actor-critic (SAC)-based speed command perception algorithm are designed to train the policy network. The output of the policy network is denormalized and directly used as speed control command, which realizes the UAV dynamic target tracking. Finally, the feasibility of the proposed end-to-end control method is demonstrated by numerical simulation. The results show that the proposed DRL-based framework is feasible to simplify the traditional modularization paradigm. The UAV can track the dynamic target with rapidly changing of speed and direction.

Cooperative Constrained Control of Autonomous Vehicles With Nonuniform Input Quantization

Responsive and highly controlled autonomous vehicles (AVs) on the road easily form platoons with small inter-vehicle gaps to increase traffic throughput and reduce energy consumption. Unlike human drivers, AVs typically employ discrete quantization signals and only generate finite discrete acceleration. Finite discrete acceleration easily causes unstable inter-vehicle gaps and inter-vehicle collisions, especially when there are inaccurate quantization parameters due to quantizer aging. Based on the backstepping technique, this paper proposes the cooperative constrained control algorithm under unknown quantization parameters for AVs with nonuniform input quantization and motion uncertainties. The control laws of each AV in the platoon only depend on the state information of its consecutive AVs. The position trajectories of consecutive AVs are constructed as inter-vehicle collision-free constraints to avoid emergency braking and inter-vehicle collisions. In addition, the consistent characteristic of two types of nonuniform quantizers (i.e., logarithmic quantizers and hysteresis quantizers) is proposed, and unknown disturbances in vehicle motion are compensated. The Lyapunov based local stability and string stability are proved. Numerical simulation shows that the control algorithm is applicable to AVs with either logarithmic quantizers or hysteresis quantizers. Compared with the previous control algorithms without considering input quantization and inter-vehicle collision avoidance, the control algorithm proposed reduces inter-vehicle spacing errors, avoids high acceleration, and avoids inter-vehicle collisions.

A Structure Economic Loss Optimization Method with the Uncertainty of Ground Motion Amplitude for Chinese Masonry Building

In the catastrophe insurance industry, it is impractical for a catastrophe model to simulate millions of sites’ environments in a short time. Hence, the attenuation relation is often adopted to simulate the ground motion on account of calculation speed, and both ground motion expectations and uncertainties must be calculated. Due to the vulnerability curves of our model being based on simulations with a large number of deterministic ground motions, it is necessary but not efficient for loss assessment to analyze all possible ground motion amplitudes and their corresponding loss rates. This paper develops a simplified method to rapidly simulate loss expectations and uncertainties. In this research, Chinese masonry buildings are the focus. The result shows that the modified method gives accurate loss results quickly.

Applicability of existing CRTS II ballastless track – Continuous girder bridge system under near-field transverse rare earthquake

The earthquake-induced applicability of key components of the existing high-speed railway CRTS II ballastless track-continuous girder bridge system (HSRCCB) is not clear. The fine simulation model of the HSRCCB is developed herein. Latin hypercube sampling is used to consider the uncertainty of ground motion and HSRCCB modeling parameters. Earthquake-induced residual deformation sample database for key components of the HSRCCB under near-field rare earthquakes is constructed. Furthermore, overlimit state analysis and applicability evaluation of bridge key components are performed using the statistical inference method. The characteristic curve of earthquake-induced residual deformation for track key components is developed using the hypothesis testing principle and statistical inference method, and the overlimit state analysis and applicability evaluation are carried out accordingly. The results show that after the near-field rare earthquake, the bearing undergoes large transverse residual deformation, as well as a longitudinal residual deformation. The box girder has an obvious transverse offset; However, the transverse and longitudinal residual deformation of track key components is smaller than that of the bearing. The maximum overlimit rate of residual deformation of fixed bearing and sliding bearing is 98%, indicating that most of the bearing gets destroyed during the process. Earthquake-induced bending and shear failure do not commonly occur at the abutment/pier bottom. The longitudinal residual deformation overlimit rate of the sliding layer and friction plate is relatively large, reaching more than 82% and 47%, respectively. Shear slots, shear steel bars, and fasteners are not easy to overrun. The overlimit rate of transverse residual deformation of lateral blocks is low, CA mortar and fasteners residual deformation overlimit rate tends to be 0. The bearing is no longer applicable. The shear force and bending moment at the abutment/pier bottom, shear slots, shear steel bars, CA mortar, and fasteners applicability all meet the requirements, while the sliding layer, friction plate, and lateral blocks are difficult to meet the applicability requirements. Among them, the applicability index value of the longitudinal residual deformation of the sliding layer is as high as 181.94, indicating that the sliding layer is far from meeting the seismic design requirements of near-field rare earthquakes. Thus, attention should be paid to the seismic design.

Investigation and Experimental Validation of Kriging-NARX Modeling for Real-Time Hybrid Simulation

ABSTRACT This study proposes the use of nonlinear autoregressive with exogenous input (NARX) to emulate dynamic behavior of analytical substructures in real-time hybrid simulation (RTHS). The training of the NARX model can be conducted numerically, and its implementation presents a good representation of structural dynamics with minimum computational demands on the RTHS equipment. A Kriging metamodel is further introduced to surrogate the NARX model coefficients to account for ground motion uncertainties. RTHS of a single-degree-of-freedom (SDOF) structure with a self-centering viscous damper are conducted as proof of concept to experimentally demonstrate the effectiveness of proposed Kriging-NARX metamodeling approach.

Seismic stability analysis of a high arch dam-foundation system considering ground motion and modeling parameter uncertainties

In this research, seismic stability uncertainty analysis of a high arch dam-foundation system is investigated considering ground motion and modeling parameter uncertainties. Ground motions and modeling parameters of shear strength on the potential sliding block of dam abutment are chosen as random variables in the probabilistic and statistic framework. Taking a high arch dam as a case study, a total number of 1000 nonlinear dynamic analyses are performed using incremental dynamic analysis (IDA) and Latin hypercube sampling (LHS). The peak ground acceleration (PGA) is taken as intensity measure (IM) and the residual sliding displacement and sliding area ratio are adopted as seismic sliding stability performance evaluation indexes, respectively. Comparative analyses are presented to discuss the effect of only modeling parameter uncertainty, only ground motion uncertainty, and both uncertainties on seismic sliding stability of the high arch dam-foundation system. The results reveal that obvious variability of seismic sliding stability of the high arch dam-foundation system is observed due to the existence of ground motion and modeling parameter uncertainties and it is sensitive to PGA. The variability owing to both ground motion and modeling parameter uncertainties is more remarkable compared with that caused by only one single uncertainty. Moreover, the ground motion uncertainty shows a more obvious impact on the variability of residual sliding displacement, while the modeling parameter uncertainty has a more notable effect on the variability of sliding area ratio. Therefore, it is necessary to consider both two kinds of uncertainties for seismic stability safety evaluation of a high arch dam-foundation system in the probabilistic and statistic framework.

The potential of biology-guided radiation therapy in thoracic cancer: A preliminary treatment planning study.

PurposeWe investigated the feasibility of biology-guided radiotherapy (BgRT), a technique that utilizes real-time positron emission imaging to minimize tumor motion uncertainties, to spare nearby organs at risk.MethodsVolumetric modulated arc therapy (VMAT), intensity-modulated proton (IMPT) therapy, and BgRT plans were created for a paratracheal node recurrence (case 1; 60 Gy in 10 fractions) and a primary peripheral left upper lobe adenocarcinoma (case 2; 50 Gy in four fractions).ResultsFor case 1, BgRT produced lower bronchus V40 values compared to VMAT and IMPT. For case 2, total lung V20 was lower in the BgRT case compared to VMAT and IMPT.ConclusionsBgRT has the potential to reduce the radiation dose to proximal critical structures but requires further detailed investigation.

A Holistic Review of GM/IM Selection Methods from a Structural Performance-Based Perspective

Current fully probabilistic approaches to performance-based earthquake engineering describe structures’ behavior under a wide range of seismic hazard levels. These approaches require a detailed representation of ground motion (GM) uncertainty at all considered hazard levels, yet different GM selection methods lead to different estimations of structural performance. This paper presents a holistic review of the current practices in GM representation and selection for structural demand analysis through a performance-based lens. The multidisciplinary nature of GM selection, ranging from earth science to engineering seismology and statistics, has created a preponderance of literature to find the best practice for probabilistic assessment of structures in terms of computational efficiency and statistical accuracy. Many of these studies focus individually on GM selection or structural analysis, and the relatively scarce review papers either focus on code-based GM selection or do not specifically address risk-based evaluations by overlooking the interaction between GM selection and structural analysis. This paper aims to aid researchers in selecting appropriate GMs as part of a statistically valid and robust probabilistic demand analysis without performing an exhaustive literature review. Discussion on the available computational tools and their trade-offs for risk-based assessment of single structures is provided. While the problem-specific nature of GM selection means that no pre-selected set of GM/IM is applicable to all cases, the comprehensive narrative of this paper is expected to aid analysts in reaching a more informed decision.

Almost sure exponential stability of nonlinear stochastic delay hybrid systems driven by G-Brownian motion

G-Brownian motion has potential applications in uncertainty problems and risk measures, which has attracted the attention of many scholars. This study investigates the almost sure exponential stability of nonlinear stochastic delay hybrid systems driven by G-Brownian motion. Due to the non-linearity of G-expectation and distribution uncertainty of G-Brownian motion, it is difficult to study this issue. Firstly, the existence of the global unique solution is derived under the linear growth condition and local Lipschitz condition. Secondly, the almost sure exponential stability of the system is analyzed by applying the G-Lyapunov function and G-Itô formula. Finally, an example is introduced to illustrate the stability. The conclusions of this paper can be applied to the stability and risk management of uncertain financial markets.

Interactive Trajectory Prediction Using a Driving Risk Map-Integrated Deep Learning Method for Surrounding Vehicles on Highways

Accurate trajectory prediction of surrounding vehicles is vital for automated vehicles to achieve high-level driving safety in complex situations. However, most state-of-the-art approaches for multi-vehicle trajectory prediction ignore vehicle motion uncertainty caused by different driving styles. Moreover, the interrelationship between the vehicle and the environment is seldom considered. To address the above problems, this paper proposes a driving risk map-integrated deep learning (DRM-DL) method for interactive trajectory prediction of surrounding vehicles, which comprehensively considers the motion uncertainty, trajectory intention uncertainty and interactions among vehicles, lane lines and road boundaries. Specifically, we adopt a conditional variational autoencoder (CVAE) to generate the candidate trajectories, in which the motion uncertainty is considered using a conditional Gaussian distribution. Furthermore, a driving risk map is constructed to realize a unified and interpretable representation of vehicle-vehicle and vehicle-environment interactions. The probability of each candidate trajectory is assigned using a trajectory probability model and a random selection is adopted to select a guided trajectory, which simulates the driver’s trajectory intention uncertainty. Finally, a relearning module is designed to obtain the precise trajectory prediction for surrounding vehicles. The proposed method is evaluated on the HighD dataset, and the results demonstrate a more accurate and reliable trajectory prediction for surrounding vehicles compared with state-of-the-art methods.

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.

Probabilistic Risk Metric for Highway Driving Leveraging Multi-Modal Trajectory Predictions

Road traffic safety has attracted increasing research attention, in particular in the current transition from human-driven vehicles to autonomous vehicles. Surrogate measures of safety are widely used to assess traffic safety but they typically ignore motion uncertainties and are inflexible in dealing with two-dimensional motion. Meanwhile, learning-based lane-change and trajectory prediction models have shown potential to provide accurate prediction results. We therefore propose a prediction-based driving risk metric for two-dimensional motion on multi-lane highways, expressed by the maximum risk value over different time instants within a prediction horizon. At each time instant, the risk of the vehicle is estimated as the sum of weighted risks over each mode in a finite set of lane-change maneuver possibilities. Under each maneuver mode, the risk is calculated as the product of three factors: lane-change maneuver mode probability, collision probability and expected crash severity. The three factors are estimated leveraging two-stage multi-modal trajectory predictions for surrounding vehicles: first a lane-change intention prediction module is invoked to provide lane-change maneuver mode possibilities, and then the mode possibilities are used as partial input for a multi-modal trajectory prediction module. Working with the empirical trajectory dataset highD and simulated highway scenarios, the proposed two-stage model achieves superior performance compared to a state-of-the-art prediction model. The proposed risk metric is computationally efficient for real-time applications, and effective to identify potential crashes earlier thanks to the employed prediction model.

Comprehensive treatment of uncertainties in risk‐targeted performance‐based seismic design and assessment of bridges

AbstractThis study complements and extends a recent work on the development of a rigorous framework for risk‐targeted performance‐based seismic design/assessment of ordinary standard bridges (OSBs) in California. Rooted in the formulation of this framework is an updated fully probabilistic performance‐based earthquake engineering (PBEE) assessment methodology wherein metrics of structural performance are formulated in terms of the mean return periods of exceedances for several strain‐based limit‐states (LSs). The originally proposed framework explicitly considering: (1) the uncertainty in the seismic input, and (2) the uncertainty in the capacity of the various LSs, is extended in this study to account for the following additional pertinent sources of uncertainty: (i) the aleatory uncertainty associated with finite element (FE) model parameters, and (ii) the epistemic parameter estimation uncertainty associated with using finite datasets to estimate the parameters of the probability distributions characterizing the FE model parameters and LS fragilities. These additional sources of uncertainty are commonly omitted or neglected in PBEE often by invoking that the earthquake ground motion uncertainty is the predominant source of uncertainty. However, their inclusion and consistent propagation in seismic performance‐based assessment of OSBs is imperative to obtain a more complete picture of seismic performance, thereby leading to a more comprehensive, transparent, and reliable design of these simple, yet essential bridges which represent an integral part of lifeline infrastructure systems especially in earthquake‐prone regions. The analytical and computational framework previously assembled is extended via modular incorporation of these additional sources of uncertainty. Four OSB testbeds and their risk‐targeted re‐designed versions are analyzed with and without these additional sources of uncertainty to evaluate their significance.