Abstract

Current advanced driver assistance systems (ADAS) measure and evaluate every environment scenario in each trip from scratch. They are not able to draw on information of previous trips because currently a memory function in production vehicles does not exist. Learning systems for vehicles were proposed to aggregate information gathered by driving routes multiple times. This information can be used to create a learning map and enable highly automated driving. Learned information can represent a certain state of the environment captured by sensors like speed limits or geo-positions of roadworks. Changes in the vehicle relevant environment will affect vehicle perception and may affect the distribution of measured variables over time. The instant where the underlying distribution of a variable changes is denoted as change point. This can invalidate parts of the acquired knowledge and was not sufficiently dealt with in previous publications in the automotive field. The ability to detect the mentioned change points is essential for the necessary ongoing update of the learning map by devaluing or removing any invalidated knowledge from memory. For this purpose we propose a statistical hypothesis test and investigate its application for a prototypic ADAS: a Curve Warning Assistance System. The formulated test is able to detect change points faster and more robust than previously proposed algorithms. The ability to aggregate information in a learning map and to handle information actualization and devaluation represents a major building block for future highly automated driver assistance systems.

Abstract

This paper presents an approach on detection of largely occluded pedestrians. From a pair of synchronized cameras in the Visible Light (VL) and Far Infrared (FIR) spectrum individual detections are combined and final confidence is inferred using a small set of logic rules via a Markov Logic Network. Pedestrians not entirely contained in the image or occluded are detected based on the binary classification on subparts of the detection window. The presented method is applied to a pedestrian classification problem in urban environments. The classifier has been tested in an Intelligent Transportation System (ITS) platform as part of an Advanced Driver Assistance Systems (ADAS). This work was supported by the Spanish Government through the Cicyt projects FEDORA (GRANT TRA2010-20225-C03- 01) and Driver Distraction Detector System (GRANT TRA2011-29454-C03-02), and by the Comunidad de Madrid through the project SEGVAUTO (S2009/DPI- 1509).