Road traffic accidents remain a major public health concern worldwide. Technological advances in vehicle sensing, automation, and artificial intelligence present novel opportunities to assess and improve human driving. This dissertation explores these opportunities by developing and evaluating algorithms to assess the behavior of car and truck drivers.

Initial research establishes the perspectives of driving examiners and professional truck drivers on the acceptance of data-driven tools to assess driver behavior. The work then demonstrates that practical methods using readily available GPS and accelerometer data can successfully identify driving styles and predict negative outcomes like fines and damage incidents at a population level. However, these simple metrics prove insufficient for fair individual assessment due to the lack of situational context embedded in such data.

To address this limitation, the thesis explores modern AI-based approaches. It demonstrates how AI systems from automated driving can provide continuous behavioral references to evaluate human performance, and concludes by showing that vision-language models can establish a more holistic, "context-aware" risk assessment using images of typical traffic situations.