Predicting driving behavior is an important component for developing safer and more reliable driver assistance systems. Modern technologies such as the Google Self-Driving Car and the Mobileye system are good examples on using sensors or cameras to detect road conditions and send out instructions to the cars and drivers when potential dangers are about to occur. However, these technologies can be less reliable when driving in extreme weather conditions, such as heavy rain or darkness at night. Thus many researchers in the engineering field have attempted to analyze driver behavior to predict what will happen next. This project proposes the use of driver behavior data to predict the possibility of an upcoming traffic accident. The project is in collaboration with Professor Kazushi Ikeda from Nara Institute of Science and Technology in Japan. Professor Ikeda and his laboratory are experts in analyzing traffic data.
Hidden Markov Models (HMM) is a popular technique used in the engineering field to analyze traffic data. One can view HMM as an unsupervised clustering technique, where one tries to cluster the different driving states into groups exhibiting similar data pattern. Much work has been proposed on estimating and predicting the different driving states using HMM, however, little work has extended this idea to predicting the occurrence of accidents. This project attempts to close this gap by combining existing methods with an additional change point detection procedure from time series analysis to predict the occurrence of an accident if the driver behaves abnormally. In brief, HMM will first be used to learn the different driving states. Once the states are estimated, a global trend within each state can be obtained and removed, then change point analysis will be applied to analyze the residual of the data, where the goal is to detect sudden changes in drivers? behavior which might signify an accident is about to occur. This can be viewed as a preprocessing step using unsupervised learning (HMM) followed by a statistical modeling approach (change point detection) to build the prediction model.
This award under the East Asia and Pacific Summer Institutes program supports summer research by a U.S. graduate student and is jointly funded by NSF and the Japan Society for the Promotion of Science.
