In many practical systems there is a delay in some ofthe sensor devices, for instance vision measurementsthat may have a long processing time. How to fusethese measurements in a Kalman filter is not a trivialproblem if the computational delay is critical. Dependingon how much time there is at hand, the designerhas to make trade offs between optimality and computationalburden of the filter. In this paper various methodsin the literature along with a new method proposedby the authors...

The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this paper, the problem is posed as anomaly detection instead, where convolutional autoencoders are applied to identify any objects deviating from the normal pattern. Training an autoencoder network to reconstruct normal patterns in agricultural fields makes it possible to detect unknown objects by high reconstruction error. Basic autoencoder (AE), vector-quantized variational autoencoder (VQ-VAE), denoising autoencoder (DAE) and semisupervised autoencoder (SSAE) with a max-margin-inspired loss function are investigated and compared with a baseline object detector based on YOLOv5. Results indicate that SSAE with an area under the curve for precision/recall (PR AUC) of 0.9353 outperforms other autoencoder models and is comparable to an object detector with a PR AUC of 0.9794. Qualitative results show that SSAE is capable of detecting unknown objects, whereas the object detector is unable to do so and fails to identify known classes of objects in specific cases.

The NNSYSID toolset for System Identification has been developed as an add on to MATLAB®. The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. This paper gives an overview of the design of NNSYSID and demonstrates its features in an example.