Generating realistic motion in a motion-based (dynamic) driving simulator is challenging due to the limited workspace of the motion system of the simulator compared to the motion range of the simulated vehicle. Motion Cueing Algorithms (MCAs) render accelerations by controlling the motion system of the simulators to provide the driver with a realistic driving experience. Commonly used methods such as Classical Washout-based MCA (CW-MCA) typically achieves suboptimal results due to scaling and filtering, which results in an inefficient usage of the workspace. The Model Predictive Control-based MCA (MPC-MCA) has been shown to achieve superior results and more efficient workspace use. However, it's performance is in practice constrained due to the computationally expensive operations and the requirement of an accurate prediction of future vehicle states. Finally, the Optimal Control (OC) has been shown to provide optimal cueing in an open-loop setup wherein the precalculated control signals are re-played to the driver. However, OC cannot be used in real-time with the driver-in-the-loop. Our work introduces a novel Neural Network-based MCA (NN-MCA), which is trained to imitate the behavior of the OC. After training, the NN-MCA provides an approximated model of the OC, which can run in real-time with the driver in-the-loop, while achieving similar quality. The experiments demonstrate the potential of this approach through objective evaluations of the generated motion-cues on the simulator model and the real simulator. A demonstration video for the performance comparison of the CW-MCA, Optimal-Control-based MCA (OC-MCA) and our proposed method is available at http://go.tum.de/708350.

A crucial part of safe navigation of autonomous vehicles is the robust detection of surrounding objects. While there are numerous approaches covering object detection in images or LiDAR point clouds, this paper addresses the problem of object detection in radar data. For this purpose, the fully convolutional neural network YOLOv3 is adapted to operate on sparse radar point clouds. In order to apply convolutions, the point cloud is transformed into a grid-like structure. The impact of this representation transformation is shown by comparison with a network based on Frustum PointNets, which directly processes point cloud data. The presented networks are trained and evaluated on the public nuScenes dataset. While experiments show that the point cloud-based network outperforms the grid-based approach in detection accuracy, the latter has a significantly faster inference time neglecting the grid conversion which is crucial for applications like autonomous driving.

Emails: {maximilian.kraus; seyedmajid.azimi; emec.ercelik; alois.knoll}@tum.de Sample aerial image with its overlaid annotations from the AerialMPT dataset taken over the BAUMA 2016 trade fair.

Non-invasive brain machine interfaces (BMIs) on motor imagery movements have been widely studied and used for many years to take advantage of the intuitive link between imagined motor tasks and natural actions. En route to future technical applications of neuromorphic computing, a major current challenge lies in the identification and implementation of brain inspired algorithms to decode recorded signals. Neuromorphic computing is believed to allow real-time implementation of large scale spiking models for processing and computation in non-invasive BMIs. Taking inspiration from the olfactory system of insects, we advance and implement a novel approach to decode and predict imaginary movements from electroencephalogram (EEG) signals. We use a spiking neural network implemented on SpiNNaker (4 chip, 64 cores) neuromorphic hardware. Our work provides a proof of concept for a successful implementation of a functional spiking neural network for decoding two motor imagery (MI) movements on the SpiNNaker system. The approach can be extended to classify more complex MI movements on larger SpiNNaker systems.