Högskolan i Skövde

Deep learning (DL) methods have advanced rapidly and are commonly applied in high-risk, resource-constrained environments such as advanced driver assistance systems (ADAS), where misclassifications can have serious consequences. With upcoming artificial intelligence (AI) legislation, it is essential to extensively evaluate and minimize the undesirable behavior of DL-based systems in such settings. An example is an ADAS that continuously evaluates whether a driver’s intended maneuvers are safe to execute given the current traffic context. Driver intention recognition (DIR), which predicts the maneuver a driver intends to perform in the near future, is a central DL-based component of such systems. Since deep neural networks (DNNs) do not inherently provide uncertainty estimates for their predictions, probabilistic deep learning (PDL) methods can be applied to improve the identification of scenarios where model outputs may be unreliable. In this thesis, we first review the current state of DIR research, focusing on the recent shift toward DL methods. We then examine how both established and novel PDL methods influence DIR performance. We evaluate the uncertainty estimations by analyzing their ability to distinguish between correct and incorrect predictions and by measuring their effectiveness in out-of-distribution (OOD) detection. Furthermore, we employ neural architecture search with multiple objectives and search strategies to explore how architectural complexity impacts DIR and OOD detection performance. Finally, we conduct a comparative experiment to evaluate human performance against that of DL-based models in video-based recognition of road user intentions.