Machine learning from observation to detect abnormal driving behavior in humans

Detection of abnormal behavior is the catalyst for many applications that seek to react to deviations from behavioral expectations. However, this is often difficult to do when direct communication with the performer is impractical. Therefore, we propose to create models of normal human performance and then compare their performance to a human's actual behavior. Any detected deviations can be then used to determine what condition(s) could possibly be influencing the deviant behavior. We build the models of human behavior through machine learning from observation; more specifically, we employ the Genetic Context Learning algorithm to create models of normal car driving behaviors of different humans with and without ADHD (Attention Deficit Hyperactivity Disorder). We use a car simulator for our studies to eliminate risk to our test subjects and to other drivers. Our results show that different driving situations have varying utility in abnormal behavior detection. Learning from Observation was successful in building models to be applied to abnormal behavior detection.