In this study, we investigate integrating eye tracking with auditory attention decoding (AAD) using portable EEG devices, specifically a mobile EEG cap and cEEGrid, in a preliminary analysis with a single participant. A novel audiovisual dataset was collected using a mobile EEG system designed to simulate real-life listening environments. Our study has two main objectives: (1) to use eye tracking data to automatically infer the labels of attended and unattended speech streams, and (2) to train an AAD model using these estimated labels, evaluating its performance through speech reconstruction accuracy. The results demonstrate the feasibility of using eye tracking data to estimate attended speech labels, which were then used to train speech reconstruction models. We validated our models with varying amounts of training data and a second dataset from the same participant to assess generalization. Additionally, we examined the impact of mislabeling on AAD accuracy. These findings provide preliminary evidence that eye tracking can be used to infer speech labels, offering a potential pathway for brain-controlled hearing aids, where true labels are unknown.

The deployment of automated functions that can operate without direct human supervision has changed safety evaluation in domains seeking higher levels of automation. Unlike conventional systems that rely on human operators, these functions require new assessment frameworks to demonstrate that they do not introduce unacceptable risks under real-world conditions. To make a convincing safety claim, the developer must present a thorough justification argument, supported by evidence, that a function is free from unreasonable risk when operated in its intended context. The key concept relevant to the presented work is the intended context, often captured by an Operational Design Domain specification (ODD) specification. ODD formalization is challenging due to the need to maintain flexibility in adopting diverse specification formats while preserving consistency and traceability and integrating seamlessly into the development, validation, and assessment. This paper presents a way to formalize an ODD in the Pkl language, addressing central challenges in specifying ODDs while improving usability through specialized configuration language features. The approach is illustrated with an automotive example but can be broadly applied to ensure rigorous assessments of operational contexts.

Clusters of neurons generate electrical signals which propagate in all directions through brain tissue, skull, and scalp of different conductivity. Measuring these signals with electroencephalography (EEG) sensors placed on the scalp results in noisy data. This can have severe impact on estimation, such as, source localization and temporal response functions (TRFs). We hypothesize that some of the noise is due to a Wiener-structured signal propagation with both linear and nonlinear components. We have developed a simple nonlinearity detection and compensation method for EEG data analysis and utilize a model for estimating source-level (SL) TRFs for evaluation. Our results indicate that the nonlinearity compensation method produce more precise and synchronized SL TRFs compared to the original EEG data.

Coherence estimation between speech envelope and electroencephalography (EEG) is a proven method in neural speech tracking. This paper proposes an improved coherence estimation algorithm which utilises phase sensitive multitaper cross-spectral estimation. Estimated EEG coherence differences between attended and ignored speech envelopes for a hearing impaired (HI) population are evaluated and compared. Testing was made on 31 HI subjects and showed significant coherence differences for grand averages over the delta, theta, and alpha EEG bands. Significance of increased coherence for attended speech was stronger for the new method compared to the traditional method. The new method of estimating EEG coherence, improves statistical detection performance and enables more rigorous data-based hypothesis-testing results.

This letter investigates relationships between iterated filtering algorithms based on statistical linearization, such as the iterated unscented Kalman filter (iukf), and filtering algorithms based on quasi–Newton (qn) methods, such as theqniterated extended Kalman filter (qn–iekf). Firstly, it is shown that theiukfand the iterated posterior linearization filter (iplf) can be viewed asqnalgorithms, by finding a Hessian correction in theqn–iekfsuch that theiplfiterate updates are identical to that of theqn–iekf. Secondly, it is shown that theiplf/iukfupdate can be rewritten such that it is approximately identical to theqn–iekf, albeit for an additional correction term. This enables a richer understanding of the properties of iterated filtering algorithms based on statistical linearization.

An evaluation of safety and security properties performed by an independent organisation can be an important step towards establishing trust in Automated Driving Systems (ADS), bridging the gap between the marketing portrayal and the actual performance of such systems in real operating conditions. However, due to the complexity of an ADS's behaviour and dangers involved in performing real environment security attacks, we believe assessments that can be performed with a combination of simulation and validation at test facilities is the way forward.In this paper, we outline an approach to derive test suites applicable to generic ADS feature classes, where classes would have similar capabilities and comparable assessment results. The goal is to support black box testing of such feature classes as part of an independent evaluation. By the means of co-simulation of post-attack behaviour and critical scenarios, we derive a representative set of physical certification tests, to gain an understanding of the interplay between safety and security. During the initial tests an ADS is subjected to various attacks and its reactions recorded. These reactions such as reduced functionality, fall back etc., together with relevant scenarios for the class is further analysed to check for safety implications.

Connected and automated vehicles with a large variety in operating modes and operational contexts are now emerging. A vital safety assurance issue, also stressed by recent standards and guidelines, is the safety of human-machine interaction (HMI). This paper proposes, and shows a small example of using, a framework for human interaction safety analysis. It is intended for integration in an iterative development lifecycle and to be used in conjunction with relevant standards. In the framework, an analysis is first conducted to elicit all agreements between humans and the automated function, then an interaction analysis method is used to find potential problems with proposed interfaces affecting each agreement. Risk assessment is conducted to determine if risk reduction is necessary, and verification and validation activities are used to provide support for the analysis results and evidence of HMI safety for an assurance case.

A wearable microphone array platform is used to localize stationary sound sources and amplify the sound in the desired directions using several beamforming methods. The platform is equipped with inertial sensors and a magnetometer allowing predictions of source locations during orientation changes and compensation for the displacement in the array configuration. The platform is modular, open and 3D printed to allow for easy reconfiguration of the array and for reuse in other applications, e.g., mobile robotics. The software components are based on open source. A new method for source localization and signal reconstruction using Taylor expansion of the signals is proposed. This and various standard and non-standard Direction of Arrival (DOA) methods are evaluated in simulation and experiments with the platform to track and reconstruct multiple and single sources. Results show that sound sources can be localized and tracked robustly and accurately while rotating the platform and that the proposed method outperforms standard methods at reconstructing the signals.

This paper presents three iterative methods for orientation estimation. The first two are based on iterated Extended Kalman filter (IEKF) formulations with different state representations. The first is using the well-known unit quaternion as state (q-IEKF) while the other is using orientation deviation which we call IMEKF. The third method is based on nonlinear least squares (NLS) estimation of the angular velocity which is used to parametrise the orientation. The results are obtained using Monte Carlo simulations and the comparison is done with the non-iterative EKF and multiplicative EKF (MEKF) as baseline. The result clearly shows that the IMEKF and the NLS-based method are superior to q-IEKF and all three outperform the non-iterative methods.

The performance of low-cost RTK (real-time kinematic) GPS receivers has been compared to a state-of-the-art system as well to each other. Both static and dynamic performances have been compared. The dynamic performance has been evaluated using a vehicle with driving robot on the AstaZero proving ground. The assembly of the low-cost RTK GPS receivers is presented, and the test set-ups described. Besides having a lower data output frequency, two of the low-cost receivers have static and dynamic performance not far from that of the state-of-the-art system.

This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work.

This paper presents a system which combines a zero-velocity-update-(ZUPT-)aided inertial navigation system (INS), using a foot-mounted inertial measurement unit (IMU), with opportunistic use of multi-frequency received signal strength (RSS) measurements. The system does not rely on maps or pre-collected data from surveys of the radio-frequency (RF) environment. Instead it builds its own database of collected RSS measurements during the course of the operation. New RSS measurements are continuously compared with the stored values in the database, and when the user returns to a previously visited area this can thus be detected. This enables loop-closures to be detected online and used for error drift correction. The system utilises a distributed particle simultaneous localization and mapping (DP-SLAM) algorithm which provides a flexible 2D navigation platform that can be extended with more sensors. The experimental results presented in this paper indicates that the developed RSS SLAM algorithm can, in many cases, significantly improve the positioning performance of a foot-mounted INS.

The parameter estimation variance of the Single Point Active Alignment Method (SPAAM) is studied through an experiment where 11 subjects are instructed to create alignments using an Optical See-Through Head Mounted Display (OSTHMD) such that three separate correspondence point distributions are acquired. Modeling the OSTHMD and the subject's dominant eye as a pinhole camera, findings show that a correspondence point distribution well distributed along the user's line of sight yields less variant parameter estimates. The estimated eye point location is studied in particular detail. The findings of the experiment are complemented with simulated data which show that image plane orientation is sensitive to the number of correspondence points. The simulated data also illustrates some interesting properties on the numerical stability of the calibration problem as a function of alignment noise, number of correspondence points, and correspondence point distribution.