The Stixel World is a medium-level, compact representation of road scenes that abstracts millions of disparity pixels into hundreds or thousands of stixels. The goal of this work is to implement and evaluate a complete multi-stixel estimation pipeline on an embedded, energy-efficient, GPU-accelerated device. This work presents a full GPU-accelerated implementation of stixel estimation that produces reliable results at 26 frames per second (real-time) on the Tegra X1 for disparity images of 1024x440 pixels and stixel widths of 5 pixels, and achieves more than 400 frames per second on a high-end Titan X GPU card.
In summer 2013, a Mercedes S-Class drove completely autonomously for about 100 km from Mannheim to Pforzheim, Germany, using only close-to-production sensors. In this project, called Mercedes Benz Intelligent Drive, stereo vision was one of the main sensing components. For the representation of free space and obstacles we relied on the so called Stixel World, a generic 3D intermediate representation which is computed from dense disparity images. In spite of the high performance of the Stixel World in most common traffic scenes, the availability of this technique is limited. For instance under adverse weather, rain or even spray water on the windshield results in erroneous disparity images which generate false Stixel results. This can lead to undesired behavior of autonomous vehicles. Our goal is to use the Stixel World for a robust free space estimation and a reliable obstacle detection even during difficult weather conditions. In this paper, we meet this challenge and fuse the Stixels incrementally into a reference grid map. Our new approach is formulated in a Bayesian manner and is based on existence estimation methods. We evaluate our new technique on a manually labeled database with emphasis on bad weather scenarios. The number of structures which are detected mistakenly within free space areas is reduced by a factor of two whereas the detection rate of obstacles increases at the same time.