Understanding Network Traffic States using Transfer Learning

Large-scale network traffic analysis is crucial for many transport applications, ranging from estimation and prediction to control and planning. One of the key issues is how to integrate spatial and temporal analyses efficiently. Deep Learning is gaining momentum as a go-to approach for artificial vision, and transfer learning approaches allow...

Design of haptic feedback control for Steer-by-Wire

This paper illustrates a comparison of different haptic feedback control strategies; primarily focusing on open and closed-loop methods for a Force-Feedback Steer-by-Wire system. Due to shortcomings caused by the feedback motor impedance in the open loop architecture, the tracking performance is deteriorated. Consequently it is shown that the...

Using road topology to improve cyclist path prediction

We learn motion models for cyclist path prediction on real-world tracks obtained from a moving vehicle, and propose to exploit the local road topology to obtain better predictive distributions. The tracks are extracted from the Tsinghua-Daimler Cyclist Benchmark for cyclist detection, and corrected for vehicle egomotion. Tracks are then...