For autonomously acting robots and driver assistance systems powerful optical stereo sensor systems are required. Object positions and environmental conditions have to be acquired in real-time. In this paper an algorithm based on a hardware-software co-design is applied. A depth-map is generated with a hierarchical detection method. A depth-histogram is generated by using the density distribution of the disparity in the depth-map. It is used for object detection. The object clustering can be accomplished without calculation of 3D-points, due to the almost identical mapping of the objects over the whole distance, within the histogram. A lane detection is applied by using a Hough transform. The suitability at night and the detection of small objects like bikers is proven.
Real-time vehicle and lane detection with embedded hardware
2005-01-01
993448 byte
Conference paper
Electronic Resource
English
Real-Time Vehicle and Lane Detection with Embedded Hardware
British Library Conference Proceedings | 2005
|Real-time lane detection and tracking for autonomous vehicle applications
SAGE Publications | 2019
|Real-Time Lane Detection for Autonomous Navigation
British Library Conference Proceedings | 2001
|Hardware Tracing of Embedded Multi-Core Real-Time Systems
SAE Technical Papers | 2017
|