Högskolan i Skövde

Progress in artificial intelligence (AI), onboard computation capabilities, and the integration of advanced sensors in cars have facilitated the development of Advanced Driver Assistance Systems (ADAS). These systems aim to continuously minimize human driving errors. {An example application of an ADAS could be to support a human driver by informing if an intended driving maneuver is safe to pursue given the current state of the driving environment. One of the components enabling such an ADAS is recognizing the driver's intentions. Driver intention recognition (DIR) concerns the identification of what driving maneuver a driver aspires to perform in the near future, commonly spanning a few seconds. A challenging aspect of integrating such a system into a car is the ability of the ADAS to handle unseen scenarios. Deploying any AI-based system in an environment where mistakes can cause harm to human beings is considered a high-risk AI system. Upcoming AI regulations require a car manufacturer to motivate the design, performance-complexity trade-off, and the understanding of potential blind spots of a high-risk AI system.} Therefore, this licentiate thesis focuses on AI-based DIR systems and presents an overview of the current state of the DIR research field. Additionally, experimental results are included that demonstrate the process of empirically motivating and evaluating the design of deep neural networks for DIR. To avoid the reliance on sequential Monte Carlo sampling techniques to produce an uncertainty estimation, we evaluated a surrogate model to reproduce uncertainty estimations learned from probabilistic deep-learning models. Lastly, to contextualize the results within the broader scope of safely integrating future high-risk AI-based systems into a car, we propose a foundational conceptual framework.