Vision Based Lane Change Detection Using True Flow Features

Detection of lane change maneuvers of other traffic participants is one key element looking at highly automated driving and future driver assistance systems. A wide number of publications have shown approaches exploiting solely high level features from processed sensor data. Looking at low level features, vision based systems offer high potential for the tasks of lane change detection in the image domain, like the positioning of objects relative to the lane markings or the evaluation of their movement towards adjacent lanes. Among conventional image processing algorithms deployed, optical flow is an appropriate approach towards the latter task, that is affected by translation and rotation of the ego vehicle. In this paper we introduce true flow as an addition to conventional optical flow. Translational and rotational influence of ego motion is eliminated, allowing to estimate solely the observed object behavior. Features for lane change detection are presented and their invariance is shown under variable conditions, using simulation data. Experimental evaluation shows feature performance and predictability based on a real world data test set.