Unsupervised drive topic finding from driving behavioral data

Continuous driving-behavioral data can be converted automatically into sequences of “drive topics” in natural language; for example, “gas pedal operating,” “high-speed cruise,” then “stopping and standing still with brakes on.” In regard to developing advanced driver-assistance systems (ADASs), various methods for recognizing driver behavior have been proposed. Most of these methods employ a supervised approach based on human tags. Unfortunately, preparing complete annotations is practically impossible with massive driving-behavioral data because of the great variety of driving scenes. To overcome that difficulty, in this study, a double articulation analyzer (DAA) is used to segment continuous driving-behavioral data into sequences of discrete driving scenes. Thereafter, latent Dirichlet allocation (LDA) is used for clustering the driving scenes into a small number of so-called “drive topics” according to emergence frequency of physical features observed in the scenes. Because both DAA and LDA are unsupervised methods, they achieve data-driven scene segmentation and drive topic estimation without human tags. Labels of the extracted drive topics are also determined automatically by using distributions of the physical behavioral features included in each drive topic. The proposed framework therefore translates the output of sensors monitoring the driver and the driving environment into natural language. Efficiency of proposed method is evaluated by using a massive data set of driving behavior, including 90 drives for more than 78 hours over 3700km in total.