This study uses behavioural data from the complete drive for a subset of 54 participants from the automation expectation mismatch set of test track experiments and aims to develop an algorithm that can predict which d...
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This study uses behavioural data from the complete drive for a subset of 54 participants from the automation expectation mismatch set of test track experiments and aims to develop an algorithm that can predict which drivers are likely to crash. Participants experienced 30 min of highly reliable supervised automation and were required to intervene to avoid crashing with a stationary object at the end of the drive. Many of them still crashed, despite having their eyes on the conflict object. They were informed about their role as supervisors, automation limitations, and received attention reminders if visually distracted. Three pre-conflict behavioural patterns were found to be associated with increased risk of crash involvement: low levels of visual attention to the forward path, high per cent road centre (i.e. gaze concentration), and long visual response times to attention reminders. One algorithm showed very high performance in classifying crashers when combining metrics related to all three behaviours. This algorithm is possible to implement as a real-time function in eye-tracker equipped vehicles. The algorithm can detect drivers that are not sufficiently engaged in the driving task, and provide feedback (e.g. reduce function performance, turn off function) to increase their engagement.
The authors consider the problem of direction-of-arrival (DOA) estimation for the uniform noise (UN) or non-UN (NUN) in a passive radar. The radar waveform to be estimated is modelled as a deterministic and unknown pr...
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The authors consider the problem of direction-of-arrival (DOA) estimation for the uniform noise (UN) or non-UN (NUN) in a passive radar. The radar waveform to be estimated is modelled as a deterministic and unknown process. Under this condition, the well-known expectation maximisation (EM) algorithm can be utilised for the estimation. In the conventional EM algorithm, the DOA is updated in every iteration, whereas the noise power ratio for each signal component is kept constant. Hence, they have to specify the noise power ratio before implementing the EM algorithm. Clearly, the DOA estimation performance of the conventional EM algorithm is sensitive to this initial noise power ratio. To deal with this problem, they propose the adaptive EM (aem) algorithms for the UN and NUN, respectively. In the authors' proposed aem algorithm, the noise power and DOA are jointly estimated and updated in each iteration, which indicates that the noise power ratio for each signal component would be adaptively changed. Therefore, the DOA performance of their proposed aem algorithm is less sensitive to the initial noise power ratio, thereby achieving better estimation results. Finally, extensive experiments are carried out to validate the effectiveness of their proposed aem algorithm.
The EM algorithm is a widely applicable algorithm for modal estimation but often criticized for its slow convergence. A new hybrid accelerator named APX-EM is proposed for speeding up the convergence of EM algorithm, ...
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The EM algorithm is a widely applicable algorithm for modal estimation but often criticized for its slow convergence. A new hybrid accelerator named APX-EM is proposed for speeding up the convergence of EM algorithm, which is based on both Linearly Preconditioned Nonlinear Conjugate Gradient (PNCG) and PX-EM algorithm. The intuitive idea is that, each step of the PX-EM algorithm can be viewed approximately as a generalized gradient just like the EM algorithm, then the linearly PNCG method can be used to accelerate the EM algorithm. Essentially, this method is an adjustment of the aem algorithm, and it usually achieves a faster convergence rate than the aem algorithm by sacrificing a little simplicity. The convergence of the APX-EM algorithm, includes a global convergence result for this method under suitable conditions, is discussed. This method is illustrated for factor analysis and a random-effects model. (C) 2020 Elsevier B.V. All rights reserved.
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