Willem Sanberg

Willem Sanberg
NXP Semiconductors · CTO Automotive System Innovations

PhD.

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10
Publications
2,397
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57
Citations

Publications

Publications (10)
Article
Full-text available
This paper presents a vision-based collision-warning system for ADAS in intelligent vehicles, with a focus on urban scenarios. In most current systems, collision warnings are based on radar, or on monocular vision using pattern recognition. Since detecting collisions is a core functionality of intelligent vehicles, redundancy is essential, so that...
Conference Paper
Full-text available
This paper explores the use of stixels in a probabilistic stereo vision-based collision-warning system that can be part of an ADAS for intelligent vehicles. In most current systems, collision warnings are based on radar or on monocular vision using pattern recognition (and ultra-sound for park assist). Since detecting collisions is such a core func...
Chapter
Smart surveillance systems become more meaningful if they both grow in reliability and robustness, while simultaneously offering a higher semantic level of understanding. To achieve a higher level of semantic scene understanding, the objects and their actions have to be interpreted in the given context, so that the extraction of contextual informat...
Article
Full-text available
Recently, vision-based Advanced Driver Assist Systems have gained broad interest. In this work, we investigate free-space detection, for which we propose to employ a Fully Convolutional Network (FCN). We show that this FCN can be trained in a self-supervised manner and achieve similar results compared to training on manually annotated data, thereby...
Conference Paper
Full-text available
This work contributes to vision processing for intelligent vehicle applications with an emphasis on Advanced Driver Assistance Systems (ADAS). A key issue for ADAS is the robust and efficient detection of free drivable space in front of the vehicle. To this end, we propose a stixel-based probabilistic color-segmentation algorithm to distinguish the...
Conference Paper
Full-text available
This work contributes to vision processing for Advanced Driver Assist Systems (ADAS) and intelligent vehicle applications. We propose a color-only stixel segmentation framework to segment traffic scenes into free, drivable space and obstacles, which has a reduced latency to improve the real-time processing capabilities. Our system learns color appe...
Chapter
Smart surveillance systems become more meaningful if they both grow in reliability and robustness, while simultaneously offering a higher semantic level of understanding. To achieve a higher level of semantic scene understanding, the objects and their actions have to be interpreted in the given context, so that the extraction of contextual informat...
Conference Paper
Full-text available
This work concentrates on vision processing for ADAS and intelligent vehicle applications. We propose a color extension to the disparity-based Stixel World method, so that the road can be robustly distinguished from obstacles with respect to erroneous disparity measurements. Our extension learns color appearance models for road and obstacle classes...
Conference Paper
Full-text available
This paper aims at improving the well-known local variance segmentation method by adding extra signal modi and specific processing steps. As a key contribution, we extend the uni-modal segmentation method to perform multi-modal analysis, such that any number of signal modi available can be incorporated in a very flexible way. We have found that the...

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