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Integrated Model Predictive Control of High-Speed Railway Running Gears with Driven Independently Rotating Wheels
Apart from lateral guidance, the longitudinal control is of crucial importance for railway safety.
Traffic Queue Length and Pressure Estimation for Road Networks with Geometric Deep Learning Algorithms
This thesis addresses this problem, where relatively inexpensive and easy to install loop-detectors are used by a geometric deep learning algorithm, which uses loop-detector data in a spatial context of a road network, to estimate queue length in front of signalized intersections, which can be then used for following traffic control tasks.
Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs
Deep neural networks can be powerful tools, but require careful application-specific design to ensure that the most informative relationships in the data are learnable.
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