Bayesian On-line Learning of Driving Behaviors

This paper presents a novel self-supervised online learning method to discover driving behaviors from data acquired with an inertial measurement unit (IMU) and a camera. Both sensors where mounted in a car that was driven by a human through a typical city environment with intersections, pedestrian crossings and traffic lights. The presented system extracts motion segments from the IMU data and relates them to visual cues obtained from camera data. It employs a Bayesian on-line estimation method to discover the motion segments based on change-point detection and uses a Dirichlet Compound Multinomial (DCM) model to represent the visual features extracted from the camera images. By incorporating these visual cues into the on-line estimation process, labels are computed that are equal for similar motion segments. As a result, typical traffic situations such as braking maneuvers in front of a red light can be identified automatically. Furthermore, appropriate actions in form of observed motion changes are associated to the discovered traffic situations. The approach is evaluated on a real data set acquired in the center of Zurich.