Autonomous driving is handled as one of the key technologies of the current era. Safety proofs for this form of transportation are partly carried out using simulations. In this paper the response of roads made of rough asphalts and concrete to radar signals is investigated, to create a database improving the accuracy of such simulations. The measurements are performed in a laboratory using specific samples of roads. The setup is validated as well, and the results are described mathematically.

A typical logistical RFID use-case is presented. The signal coverage is computed using accurate simulation techniques. A subsequent evaluation of the simulation results is performed. Emphasis is put on the derivation of expressive quantities to facilitate the evaluation and interpretation of the simulation results. Accurate simulations of the planned environment are essential to predict the real behavior and uncover flaws in the planned system, to avoid costly a-posteriori modifications.

On the way to autonomous driving a safety proof is inevitable. Here simulators can help, but their accuracy is not good enough, yet. In order to improve such simulations, we analyzed the behaviour of road surfaces on radar sensors. Therefore, laboratory measurements and open space measurements were performed. Differences depending on material and roughness could be proved. Furthermore, material parameters were calculated, which can be mapped to roads in existing simulation setups.

This work presents a deep learning approach to classify road users as pedestrians, cyclists or cars using a lidar sensor for detection and a radar sensor for classification. A dataset was gathered on urban roads to train and test the deep learning algorithms. The results show that the system reliably classifies cars, but has trouble with pedestrians and cyclists. The results are improved after aggregating decisions with a Bayes filter. Overlapping targets remain a challenge for the system.

A target simulator for RFID direction of arrival estimation systems is proposed. This simulator can be used in the evaluation of such systems and offers a method for fast, economical and, above all, reproducible and transferable analysis of their performance. Signal models and descriptions of two typical detrimental propagation effects are derived. The developed system structure and hardware modules are presented and exemplary evaluation results are given, showing the application of this method.