Abstract: Obstacle detection for advanced driver assistance systems has focused on building detectors for only a few number of categories so far, such as pedestrians and cars. However, vulnerable obstacles of other categories are often dismissed, such as wheel-chairs and baby strollers. In our work, we try to tackle this limitation by presenting an approach which is able to predict the vulnerability of an arbitrary obstacle independently from its category. This allows for using models not specifically tuned for category recognition. To classify the vulnerability, we apply a generic category-free approach based on large random bag-of-visual-words representations (BoW), where we make use of both the intensity image as well as a given disparity map. In experimental results, we achieve a classification accuracy of over 80% for predicting one of four vulnerability levels for each of the 10000 obstacle hypotheses detected in a challenging dataset of real urban street scenes. Vulnerability prediction in general and our working algorithm in particular, pave the way to more advanced reasoning in autonomous driving, emergency route planning, as well as reducing the false-positive rate of obstacle warning systems.

Abstract: We present a method based on image processing to evaluate the vulnerability of objects detected in front of a vehicle by means of a stereo camera. The evaluation is part of the current cooperate research project UR:BAN SVT, which is introduced and described in this paper. The project's main objective is to further increase road safety for vulnerable road users. The detection of potentially vulnerable real world objects is performed by a car build-in stereo camera that outputs object hypotheses as medium-level object representations. Given these generic object hypotheses, our method classifies an object's vulnerability that states the expected damage of a car collision from the object's perspective. This information about obstacle hypotheses enables a better static scene understanding and thereby can be used to plan actions for accident prevention and mitigation in emergency situations. Our approach focuses on employing a model-free classification pipeline using bags-of-visual words extracted in a completely unsupervised manner. The results show that the bag-of-visual-words approach is well-suited for evaluating the vulnerability of object hypotheses.