The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Marcus T Pearce - One of the best experts on this subject based on the ideXlab platform.
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ppm decay a computational model of auditory Prediction with memory decay
PLOS Computational Biology, 2020Co-Authors: Peter M C Harrison, Roberta Bianco, Maria Chait, Marcus T PearceAbstract:Statistical learning and Probabilistic Prediction are fundamental processes in auditory cognition. A prominent computational model of these processes is Prediction by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n-grams from training sequences. However, PPM has limitations as a cognitive model: in particular, it has a perfect memory that weights all historic observations equally, which is inconsistent with memory capacity constraints and recency effects observed in human cognition. We address these limitations with PPM-Decay, a new variant of PPM that introduces a customizable memory decay kernel. In three studies—one with artificially generated sequences, one with chord sequences from Western music, and one with new behavioral data from an auditory pattern detection experiment—we show how this decay kernel improves the model’s predictive performance for sequences whose underlying statistics change over time, and enables the model to capture effects of memory constraints on auditory pattern detection. The resulting model is available in our new open-source R package, ppm (https://github.com/pmcharrison/ppm).
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decomposing neural responses to melodic surprise in musicians and non musicians evidence for a hierarchy of Predictions in the auditory system
NeuroImage, 2020Co-Authors: David Ricardo Quirogamartinez, Marcus T Pearce, Niels Christian Hansen, Andreas Hojlund, Elvira Brattico, Peter VuustAbstract:Abstract Neural responses to auditory surprise are typically studied with highly unexpected, disruptive sounds. Consequently, little is known about auditory Prediction in everyday contexts that are characterized by fine-grained, non-disruptive fluctuations of auditory surprise. To address this issue, we used IDyOM, a computational model of auditory expectation, to obtain continuous surprise estimates for a set of newly composed melodies. Our main goal was to assess whether the neural correlates of non-disruptive surprising sounds in a musical context are affected by musical expertise. Using magnetoencephalography (MEG), auditory responses were recorded from musicians and non-musicians while they listened to the melodies. Consistent with a previous study, the amplitude of the N1m component increased with higher levels of computationally estimated surprise. This effect, however, was not different between the two groups. Further analyses offered an explanation for this finding: Pitch interval size itself, rather than Probabilistic Prediction, was responsible for the modulation of the N1m, thus pointing to low-level sensory adaptation as the underlying mechanism. In turn, the formation of auditory regularities and proper Probabilistic Prediction were reflected in later components: The mismatch negativity (MMNm) and the P3am, respectively. Overall, our findings reveal a hierarchy of expectations in the auditory system and highlight the need to properly account for sensory adaptation in research addressing statistical learning.
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decomposing neural responses to melodic surprise in musicians and non musicians evidence for a hierarchy of Predictions in the auditory system
bioRxiv, 2020Co-Authors: David Ricardo Quirogamartinez, Marcus T Pearce, Niels Christian Hansen, Andreas Hojlund, Elvira Brattico, Peter VuustAbstract:Abstract Neural responses to auditory surprise are typically studied with highly unexpected, disruptive sounds. Consequently, little is known about auditory Prediction in everyday contexts that are characterized by fine-grained, non-disruptive fluctuations of auditory surprise. To address this issue, we used IDyOM, a computational model of auditory expectation, to obtain continuous surprise estimates for a set of newly composed melodies. Our main goal was to assess whether the neural correlates of non-disruptive surprising sounds in a musical context are affected by musical expertise. Using magnetoencephalography (MEG), auditory responses were recorded from musicians and non-musicians while they listened to the melodies. Consistent with a previous study, the amplitude of the N1m component increased with higher levels of computationally estimated surprise. This effect, however, was not different between the two groups. Further analyses offered an explanation for this finding: Pitch interval size itself, rather than Probabilistic Prediction, was responsible for the modulation of the N1m, thus pointing to low-level sensory adaptation as the underlying mechanism. In turn, the formation of auditory regularities and proper Probabilistic Prediction were reflected in later components: the mismatch negativity (MMNm) and the P3am, respectively. Overall, our findings reveal a hierarchy of expectations in the auditory system and highlight the need to properly account for sensory adaptation in research addressing statistical learning. Highlights - In melodies, sound expectedness (modeled with IDyOM) is associated with the amplitude of the N1m. - This effect is not different between musicians and non-musicians. - Sensory adaptation related to melodic pitch intervals explains better the N1m effect. - Auditory regularities and the expectations captured by IDyOM are reflected in the MMNm and P3am. - Evidence for a hierarchy of auditory Predictions during melodic listening.
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Statistical learning and Probabilistic Prediction in music cognition: mechanisms of stylistic enculturation.
Annals of the New York Academy of Sciences, 2018Co-Authors: Marcus T PearceAbstract:Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) Probabilistic Prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of Probabilistic Prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of Probabilistic Prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here.
Ardalan Vahidi - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Anticipation and Control in Autonomous Car Following
IEEE Transactions on Control Systems Technology, 2019Co-Authors: Nianfeng Wan, Chen Zhang, Ardalan VahidiAbstract:Human-driven and autonomously driven cars of today act often reactively to the decisions of the cars they follow, which could lead to uncomfortable, inefficient, and sometimes unsafe situations in stop and go traffic. This paper proposes methods for Probabilistic anticipation of the motion of the preceding vehicle and for the control of motion of the ego vehicle. We construct: 1) a Markov chain predictor based on the observed behavior of preceding vehicle and 2) a maximum likelihood motion predictor based on historical traffic speed at different locations and times. Heuristics are proposed for combining the two Predictions to determine a probability distribution on the position of the preceding vehicle over a future planning horizon. A chance-constrained model predictive control framework is employed to optimize the motion of the ego vehicle, given the Probabilistic Prediction of motion of preceding vehicle. Effectiveness of the proposed approach is evaluated in multiple simulation scenarios.
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an optimal velocity planning scheme for vehicle energy efficiency through Probabilistic Prediction of traffic signal timing
IEEE Transactions on Intelligent Transportation Systems, 2014Co-Authors: Grant Mahler, Ardalan VahidiAbstract:The main contribution of this paper is the formulation of a predictive optimal velocity-planning algorithm that uses Probabilistic traffic-signal phase and timing (SPAT) information to increase a vehicle's energy efficiency. We introduce a signal-phase Prediction model that uses historically averaged timing data and real-time phase data to determine the probability of green for upcoming traffic lights. In an optimal control framework, we then calculate the best velocity trajectory that maximizes the chance of going through green lights. The case study results from a multisignal simulation indicating that energy efficiency can be increased with Probabilistic timing data and real-time phase data. Monte Carlo simulations are used to confirm that the case study results are valid, on average. Finally, simulated vehicles are driven through a series of traffic signals, using recorded data from a real-world set of traffic-adaptive signals, to determine the applicability of these predictive models to various types of traffic signals.
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reducing idling at red lights based on Probabilistic Prediction of traffic signal timings
Advances in Computing and Communications, 2012Co-Authors: Grant Mahler, Ardalan VahidiAbstract:A predictive optimal velocity planning algorithm is proposed in this paper that uses traffic Signal Phase And Timing (SPAT) information to increase a vehicle's energy efficiency. Encouraged by positive results based on full SPAT information in [1], [2], this current paper focuses on benefits attainable with partial Probabilistic information. Availability of signal phase data is categorized into none, real-time only, and full-future knowledge. Dynamic Programming (DP) with full future knowledge of SPAT provides an energy efficiency maximum. The case with no knowledge of phase or timing represents an uninformed driver, and provides an energy efficiency minimum. In between, a signal phase Prediction model which could use historically-averaged timing data and real-time phase data is evaluated, as it represents a technology which is available today. Results from a multi-signal simulation indicate that energy efficiency can be increased with Probabilistic timing data and real-time phase data.
Bo Liu - One of the best experts on this subject based on the ideXlab platform.
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optimal kernel elm and variational mode decomposition for Probabilistic pv power Prediction
Energies, 2020Co-Authors: Chun Sing Lai, Chenchen Bai, Loi Lei Lai, Qi Zhang, Bo LiuAbstract:A Probabilistic Prediction interval (PI) model based on variational mode decomposition (VMD) and a kernel extreme learning machine using the firefly algorithm (FA-KELM) is presented to tackle the problem of photovoltaic (PV) power for intra-day-ahead Prediction. Firstly, considering the non-stationary and nonlinear characteristics of a PV power output sequence, the decomposition of the original PV power output series is carried out using VMD. Secondly, to further improve the Prediction accuracy, KELM is established for each decomposed component and the firefly algorithm is introduced to optimize the penalty factor and kernel parameter. Finally, the point predicted value is obtained through the summation of predicted results of each component and then using the nonlinear kernel density estimation to fit it. The cubic spline interpolation algorithm is applied to obtain the shortest confidence interval. Results from practical cases show that this Probabilistic Prediction interval could achieve higher accuracy as compared with other Prediction models.
Jonathan Ko - One of the best experts on this subject based on the ideXlab platform.
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gp bayesfilters bayesian filtering using gaussian process Prediction and observation models
Autonomous Robots, 2009Co-Authors: Jonathan KoAbstract:Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. Key components of each Bayes filter are Probabilistic Prediction and observation models. This paper shows how non-parametric Gaussian process (GP) regression can be used for learning such models from training data. We also show how Gaussian process models can be integrated into different versions of Bayes filters, namely particle filters and extended and unscented Kalman filters. The resulting GP-BayesFilters can have several advantages over standard (parametric) filters. Most importantly, GP-BayesFilters do not require an accurate, parametric model of the system. Given enough training data, they enable improved tracking accuracy compared to parametric models, and they degrade gracefully with increased model uncertainty. These advantages stem from the fact that GPs consider both the noise in the system and the uncertainty in the model. If an approximate parametric model is available, it can be incorporated into the GP, resulting in further performance improvements. In experiments, we show different properties of GP-BayesFilters using data collected with an autonomous micro-blimp as well as synthetic data.
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gp bayesfilters bayesian filtering using gaussian process Prediction and observation models
Intelligent Robots and Systems, 2008Co-Authors: Jonathan KoAbstract:Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filters (extended and unscented) and particle filters. Key components of each Bayes filter are Probabilistic Prediction and observation models. Recently, Gaussian processes have been introduced as a non-parametric technique for learning such models from training data. In the context of unscented Kalman filters, these models have been shown to provide estimates that can be superior to those achieved with standard, parametric models. In this paper we show how Gaussian process models can be integrated into other Bayes filters, namely particle filters and extended Kalman filters. We provide a complexity analysis of these filters and evaluate the alternative techniques using data collected with an autonomous micro-blimp.
Masayoshi Tomizuka - One of the best experts on this subject based on the ideXlab platform.
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generic tracking and Probabilistic Prediction framework and its application in autonomous driving
IEEE Transactions on Intelligent Transportation Systems, 2020Co-Authors: Wei Zhan, Masayoshi TomizukaAbstract:Accurately tracking and predicting behaviors of surrounding objects are key prerequisites for intelligent systems such as autonomous vehicles to achieve safe and high-quality decision making and motion planning. However, there still remain challenges for multi-target tracking due to object number fluctuation and occlusion. To overcome these challenges, we propose a constrained mixture sequential Monte Carlo (CMSMC) method in which a mixture representation is incorporated in the estimated posterior distribution to maintain multi-modality. Multiple targets can be tracked simultaneously within a unified framework without explicit data association between observations and tracking targets. The framework can incorporate an arbitrary Prediction model as the implicit proposal distribution of the CMSMC method. An example in this paper is a learning-based model for hierarchical time-series Prediction, which consists of a behavior recognition module and a state evolution module. Both modules in the proposed model are generic and flexible so as to be applied to a class of time-series Prediction problems where behaviors can be separated into different levels. Finally, the proposed framework is applied to a numerical case study as well as a task of on-road vehicle tracking, behavior recognition, and Prediction in highway scenarios. Instead of only focusing on forecasting trajectory of a single entity, we jointly predict continuous motions for interactive entities simultaneously. The proposed approaches are evaluated from multiple aspects, which demonstrate great potential for intelligent vehicular systems and traffic surveillance systems.
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multi modal Probabilistic Prediction of interactive behavior via an interpretable model
IEEE Intelligent Vehicles Symposium, 2019Co-Authors: Wei Zhan, Liting Sun, Masayoshi TomizukaAbstract:For autonomous agents to successfully operate in real world, the ability to anticipate future motions of surrounding entities in the scene can greatly enhance their safety levels since potentially dangerous situations could be avoided in advance. While impressive results have been shown on predicting each agent's behavior independently, we argue that it is not valid to consider road entities individually since transitions of vehicle states are highly coupled. Moreover, as the predicted horizon becomes longer, modeling Prediction uncertainties and multi-modal distributions over future sequences will turn into a more challenging task. In this paper, we address this challenge by presenting a multi-modal Probabilistic Prediction approach. The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents. Most importantly, our model is interpretable, which can explain the underneath logic as well as obtain more reliability to use in real applications. A complicate real-world roundabout scenario is utilized to implement and examine the proposed method.
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Probabilistic Prediction of interactive driving behavior via hierarchical inverse reinforcement learning
arXiv: Learning, 2018Co-Authors: Liting Sun, Wei Zhan, Masayoshi TomizukaAbstract:Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly. Such Prediction should be Probabilistic, to address the uncertainties in human behavior. Such Prediction should also be interactive, since the distribution over all possible trajectories of the predicted vehicle depends not only on historical information, but also on future plans of other vehicles that interact with it. To achieve such interaction-aware Predictions, we propose a Probabilistic Prediction approach based on hierarchical inverse reinforcement learning (IRL). First, we explicitly consider the hierarchical trajectory-generation process of human drivers involving both discrete and continuous driving decisions. Based on this, the distribution over all future trajectories of the predicted vehicle is formulated as a mixture of distributions partitioned by the discrete decisions. Then we apply IRL hierarchically to learn the distributions from real human demonstrations. A case study for the ramp-merging driving scenario is provided. The quantitative results show that the proposed approach can accurately predict both the discrete driving decisions such as yield or pass as well as the continuous trajectories.
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Probabilistic Prediction from Planning Perspective: Problem Formulation, Representation Simplification and Evaluation Metric
2018 IEEE Intelligent Vehicles Symposium (IV), 2018Co-Authors: Wei Zhan, Arnaud De La Fortelle, Ching-yao Chan, Yi-ting Chen, Masayoshi TomizukaAbstract:Accurate Probabilistic Prediction for intention and motion of road users is a key prerequisite to achieve safe and high-quality decision-making and motion planning for autonomous driving. Typically, the performance of Probabilistic Predictions was only evaluated by learning metrics for approximation to the motion distribution in the dataset. However, as a module supporting decision and planning, Probabilistic Prediction should also be evaluated from decision and planning perspective. Moreover, the evaluation of Probabilistic Prediction highly relies on the problem formulation variation and motion representation simplification, which lacks a formal foundation in a comprehensive framework. To address such concerns, we provide a systematic and unified framework for the analysis of three under-explored aspects of Probabilistic Prediction: problem formulation, representation simplification and evaluation metric. More importantly, we address the omitted but crucial problems in the three aspects from decision and planning perspective. In addition to a review of learning metrics, metrics to be considered from planning perspective are highlighted, such as planning consequence of inaccurate and erroneous Prediction, as well as violations of predicted motions to planning constraints. We address practical formulation variations of Prediction problems, such as decision-maker view and blind view for viewpoint, as well as reactive Prediction for interaction, so that decision and planning can be facilitated.
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Probabilistic Prediction of vehicle semantic intention and motion
IEEE Intelligent Vehicles Symposium, 2018Co-Authors: Wei Zhan, Masayoshi TomizukaAbstract:Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving maneuvers. Therefore, a intention Prediction method that can adapt to different traffic scenarios is needed. To further improve the overall vehicle Prediction performance, motion information is usually incorporated with classified intentions. As suggested in some literature, the methods that directly predict possible goal locations can achieve better performance for long-term motion Prediction than other approaches due to their automatic incorporation of environment constraints. Moreover, by obtaining the temporal information of the predicted destinations, the optimal trajectories for predicted vehicles as well as the desirable path for ego autonomous vehicle could be easily generated. In this paper, we propose a Semantic based Intention and Motion Prediction (SIMP) method, which can be adapted to any driving scenarios by using semantic defined vehicle behaviors. It utilizes a Probabilistic framework based on deep neural network to estimate the intentions, final locations, and the corresponding time information for surrounding vehicles. An exemplar real-world scenario was used to implement and examine the proposed method.
