Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach
Abstract
The main aim of this work is the development of a vision-based road detection system fast enough to cope with the difficult real-time constraints imposed by moving vehicle applications. The hardware platform, a special-purpose massively parallel system, has been chosen to minimize system production and operational costs. This paper presents a novel approach to expectation-driven low-level image segmentation, which can be mapped naturally onto mesh-connected massively parallel Simd architectures capable of handling hierarchical data structures. The input image is assumed to contain a distorted version of a given template; a multiresolution stretching process is used to reshape the original template in accordance with the acquired image content, minimizing a potential function. The distorted template is the process output.
Cite
Text
Broggi and Bertè. "Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach." Journal of Artificial Intelligence Research, 1995. doi:10.1613/JAIR.185Markdown
[Broggi and Bertè. "Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach." Journal of Artificial Intelligence Research, 1995.](https://mlanthology.org/jair/1995/broggi1995jair-visionbased/) doi:10.1613/JAIR.185BibTeX
@article{broggi1995jair-visionbased,
title = {{Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach}},
author = {Broggi, Alberto and Bertè, Simona},
journal = {Journal of Artificial Intelligence Research},
year = {1995},
pages = {325-348},
doi = {10.1613/JAIR.185},
volume = {3},
url = {https://mlanthology.org/jair/1995/broggi1995jair-visionbased/}
}