Currently, self-driving vehicles have trouble detecting partially and fully occluded objects such as pedestrians, vehicles, and static obstacles. It has been proven that a drone surveilling the area around the vehicle improves the vehicle's awareness of its surroundings. This work explores planning strategies for the drone and evaluates how much the strategies assist the vehicle. Previous work proposed a metric called PKL, which describes the awareness of a vehicle as a function of the detections of the vehicle using a planner model. Subtracting this metric calculated using detections done by the drone from the metric without these detections results in an awareness improvement metric. This metric was used to evaluate drone positions and therefore the drone planning strategies. It was found that aiming to hover directly above the autonomous vehicle improves the awareness of the vehicle measured by median PKL improvement by 1.8%. Using only the relative position of the autonomous vehicle to the drone, the velocity of the vehicle in x and y directions and the number of vehicles visible to the drone, an imitation learning-based strategy performed the best with 11.6% median PKL improvement. If the drone's planner knows the future position of the autonomous vehicle or the positions of all vehicles in the area, the median PKL improvement is 74.5% and 80.8%, respectively. Optimizing the trajectories using a genetic algorithm further improves the performance to 96.7%. From these numbers, we can conclude that aiming to stay directly above the vehicle does not benefit the autonomous vehicle the most. We show that intelligent drone trajectory planning strategies can be learned which improve the awareness of the autonomous vehicle and therefore the safety of the people in and around this vehicle.

WiECG aims to create a prototype device which enable ambulance personnel to perform a 12-lead ECG without wires connecting the patient to the monitor. The proposed solution consists of a transmitter and a receiver device. The transmitter transmits the measured signals, the receiver sends the measured signals to the monitor, which shows the measured signals.
This thesis describes the design and implementation process for the hardware. This concerns the amplification and filtering of the signals produced by the heart of the patient as well as the reconstruction
and attenuation at the output of the receiver module. These processes should be done for 9 signals. Furthermore, component selection, design decisions and the process of implementing this analog signal processing on a printed circuit board is described including the interfaces with the modules used by the other subgroups of this thesis. The prototype built during this project was able to filter the 9 signals and send it from transmitter to receiver while only adding 1.572 V RMS noise to the output ECG signal.