Thomas Heger

3D-segmentation of traffic environments with u/v-disparity supported by radar-given masterpoints

3D-segmentation of a traffic scene with two-dimensional row- and column-disparity-histograms, namely u/v-disparities, has become more and more popular for modern stereo-camera-based driver assistance systems due to its fast computation in real-time, few memory requirements and robustness against noisy or intermittent data. In this paper, we present a novel approach to support this pure vision-based method by projecting preprocessed radar-signals directly to u-disparity-space. We called the projection result “masterpoints”. This data fusion on low feature-level improved the segmentation process and increased the obstacle detection rate significantly. No assumptions about obstacle-type or -size are needed. Furthermore, the algorithms can be parallelized easily and run in real-time.

Keynote lecture driver assistance systems - safety joins comfort

Driver assistance systems are a fast growing field in automotive industry. Functions like adaptive cruise control, lane keeping support, road sign recognition, parking and maneuvering, as well as assisted braking and automatic emergency braking are the first established steps towards the dream of autonomous driving. On that way the aspects of safety and robustness play a more and more important role in design and realization of the overall systems. It is obvious that sensors with a broad base of information and high robustness will contribute best to it. To fulfill requirements of robustness and availability, gathering information about the environment with redundant and/or orthogonal methods is necessary. As automotive industry has to keep cost and complexity as low as possible, a very powerful sensor to mention in that context is the stereo camera. The presentation describes how a stereo camera can be used to fulfill these requirements. There are challenges to face and problems to solve, but the technology has already proven that it is ready to use and opens a wide range of functionalities - ending up in the dream of autonomous driving.

Vehicle environment description and interpretation using conceptual graphs

The video-based driver assistance systems are an important part of the vehicle safety strategy. The ego vehicle environment description in several frames provided by monocular camera was used in this work to depict the relations between the ego vehicle and other objects, as well as the relations of those objects between each other. Based on this information the behavior of the objects could be predicted. The conceptual graphs application for a scene description and interpretation has been introduced and the functionality has been shown on the real traffic situation.

Estimation of Collision Risk Using Monocular On-Board Camera

Abstract The collision detection between ego vehicle and a moving object is crucial for driver assistance systems. In this paper the method for the estimation of collision risk is studied. The data needed for this is obtained from video sequences acquired by a monocular camera mounted on the ego vehicle. The algorithm incorporating the parameters (Focus of Expansion, Time to Collision and the distance to the object) is proposed.