Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization

Chen, Zhaozhong; Heckman, Christoffer; Julier, Simon; Ahmed, Nisar

doi:10.23919/icif.2018.8454982

Cited by 62 publications

(41 citation statements)

References 8 publications

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“…Another state uncertainty metric that additionally measures whether an estimated object state is consistent with the tracker's estimate of its own Gaussian state uncertainty is the Normalized Estimation Error Squared (NEES, see Ref. [92]).…”

Section: Test Criteria and Metricsmentioning

confidence: 99%

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

2022

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Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. The paper focuses on testing for verification and validation purposes at the interface between perception and planning, and structures the analysis along the three axes (1) test criteria and metrics, (2) test scenarios, and (3) reference data. Furthermore, the analyzed literature includes related safety standards, safety-independent perception algorithm benchmarking, and sensor modeling. It is found that the realization of safety-oriented perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.

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Section: Test Criteria and Metricsmentioning

confidence: 99%

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

2022

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“…As seen in Figure 1, there are three sensory conflict signals (e a , e f , e ω ), each of which is three-dimensional and vary in units. To combine these disparate, multi-dimensional signals into a single, useful metric of sensory conflict for each hypothesis, we use a statistic from adaptive estimation theory called Normalized Innovation Squared (NIS) (Bar-Shalom et al, 2001;Chen et al, 2018):…”

Section: Computational Implementationmentioning

confidence: 99%

COMPASS: Computations for Orientation and Motion Perception in Altered Sensorimotor States

Kravets

Dixon

Ahmed

et al. 2021

Front. Neural Circuits

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Reliable perception of self-motion and orientation requires the central nervous system (CNS) to adapt to changing environments, stimuli, and sensory organ function. The proposed computations required of neural systems for this adaptation process remain conceptual, limiting our understanding and ability to quantitatively predict adaptation and mitigate any resulting impairment prior to completing adaptation. Here, we have implemented a computational model of the internal calculations involved in the orientation perception system’s adaptation to changes in the magnitude of gravity. In summary, we propose that the CNS considers parallel, alternative hypotheses of the parameter of interest (in this case, the CNS’s internal estimate of the magnitude of gravity) and uses the associated sensory conflict signals (i.e., difference between sensory measurements and the expectation of them) to sequentially update the posterior probability of each hypothesis using Bayes rule. Over time, an updated central estimate of the internal magnitude of gravity emerges from the posterior probability distribution, which is then used to process sensory information and produce perceptions of self-motion and orientation. We have implemented these hypotheses in a computational model and performed various simulations to demonstrate quantitative model predictions of adaptation of the orientation perception system to changes in the magnitude of gravity, similar to those experienced by astronauts during space exploration missions. These model predictions serve as quantitative hypotheses to inspire future experimental assessments.

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“…Secondly, we consider the uncertainties associated with the forecasts by assessing the NEES, which is a popular method in the field of signal processing and tracking [23] and recently applied to epidemiological forecasts in [6], to determine if the estimated variance of forecasts from an algorithm differs from the true variance. If the variance is larger than the true variance, then the algorithm is over-cautious, and if the estimated variance is smaller than the true variance, it is over-confident.…”

Section: Computational Experimentsmentioning

confidence: 99%

Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths

Conor¹,

Moore²,

Carter³

et al. 2021

Preprint

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The emergence of the novel coronavirus (COVID-19) has generated a need to quickly and accurately assemble up-to-date information related to its spread. While it is possible to use deaths to provide a reliable information feed, the latency of data derived from deaths is significant. Confirmed cases derived from positive test results potentially provide a lower latency data feed. However, the sampling of those tested varies with time and the reason for testing is often not recorded. Hospital admissions typically occur around 1-2 weeks after infection and can be considered out of date in relation to the time of initial infection. The extent to which these issues are problematic is likely to vary over time and between countries.We use a machine learning algorithm for natural language processing, trained in multiple languages, to identify symptomatic individuals derived from social media and, in particular Twitter, in real-time. We then use an extended SEIRD epidemiological model to fuse combinations of low-latency feeds, including the symptomatic counts from Twitter, with death data to estimate parameters of the model and nowcast the number of people in each compartment. The model is implemented in the probabilistic programming language Stan and uses a bespoke numerical integrator. We present results showing that using specific low-latency data feeds along with death data provides more consistent and accurate forecasts of COVID-19 related deaths than using death data alone.

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Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization

Cited by 62 publications

References 8 publications

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

COMPASS: Computations for Orientation and Motion Perception in Altered Sensorimotor States

Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths

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