Publications
Supervised machine learning for modeling human recognition of vehicle-driving situations
A classification system is developed to identify driving situations from labeled examples of previous occurrences. The purpose of the classifier is to provide physical context to a separate system that mitigates unnecessary distractions, allowing the driver to maintain focus during periods of high difficulty. While watching videos of driving, we asked different users to indicate their perceptions of the current situation. We then trained a classifier to emulate the human recognition of driving situations using the Sandia Cognitive Framework. In unstructured conditions, such as driving in urban areas and the German autobahn, the classifier was able to correctly predict human perceptions of driving situations over 95% of the time. This paper focuses on the learning algorithms used to train the driving-situation classifier. Future work will reduce the human efforts needed to train the system. © 2005 IEEE.