The autonomous vehicle industry has the potential to revolutionize the future of driving, making the understanding of vehicle-driver interactions crucial as we progress towards fully autonomous systems. Advanced Driver Assistance Systems (ADAS) are integral in this evolution, bridging the gap between automated and manual driving. Research indicates that ADAS influences human driving behavior, impacting safety. While short-term behavioral effects are documented, long-term adaptations remain underexplored. Addressing this knowledge gap is vital for enhancing safety and optimizing ADAS performance. This thesis proposes a method to automatically detect long-term behavioral adaptations using a neural network and a novelty detection technique. The neural network, designed for driver identification, detects individual behavioral changes, while the novelty detection component classifies these changes as normal or abnormal. Key considerations include the need for a representative dataset, informative input features, a robust neural network architecture, and effective training. The system uses multiple Long Short-Term Memory (LSTM) layers and a novelty monitor to identify unfamiliar inputs. Evaluation using the CARLA simulator and the Intelligent Driver Model shows the approach's effectiveness in detecting behavioral adaptations, though improvements are needed for capturing subtle changes. The method's primary limitation is its lack of realism, suggesting future testing in dynamic, real-world conditions. Future work should also address defining 'normal' behavior in diverse scenarios and developing automatic parameter-setting techniques for real-time application.

The AI World Cup is a virtual competition in which teams of five players compete in a football match. The defensive strategies for the goalkeeper in this environment are yet to be researched, however. In previous editions of the competition the participating teams use a basic goalkeeper that can only dive but not position itself. This project provides research into the use of different positioning techniques to improve the performance of the goalkeeper.
In the project two different positioning techniques are presented and evaluated. In the first technique, the goalkeeper moves on an imaginary arc in front of the goal and in the second technique the goalkeeper moves on a straight line in front of the goal. Moreover, teamworking techniques between a defender and the goalkeeper are used to create a better coverage of the goal. The evaluations for the positioning techniques consist of 250 shots shot by an attacker from random distances and angles. The evaluations indicate that both goalkeepers have an improvement in performance over the basic goalkeeper, used by the other teams. The goalkeeper positioning on a line has the best performance, with an increase in performance of 27\% over the basic goalkeeper. These results demonstrate the value of positioning techniques for a goalkeeper in the AI World Cup.