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Blog 2018-08-08T13:56:04+00:00

Road Boundary Detection

This blog post provides an overview of our paper “Inferring Road Boundaries Through and Despite Traffic” by Tarlan Suleymanov, Paul Amayo and Paul Newman, which has been accepted for publication at the 21st IEEE International Conference on Intelligent Transportation Systems (ITSC) 2018. In the context of autonomous driving, road boundaries play a vital role as they legally and intentionally delimit driveable space. They provide information for navigation, path planning and mapping, and can be used as reference structure for accurate lateral vehicle positioning on a road. Additionally, road boundary detection is a crucial component of ADAS (Advanced Driving Assistance Systems) such as parking assist systems. Knowing where the road ends is always good, but the road users block the view of the road boundaries. Driving in urban scenes requires innate reasoning about unseen regions and our goal here is to trace out, in an image, the projection of road boundaries irrespective of whether or not the boundary is actually visible. In recent years, machine learning has achieved state-of- the-art performance in segmentation and object detection problems. However, many existing image segmentation or object detection methods do not explicitly infer geometry of road boundaries, rather segment the road [1]. Road boundary detection using deep learning approaches from mono images hasn’t been addressed deeply in the literature. The small width, elongated shape and no clear form in either appearance or geometry of road boundaries make detection challenging for state-of-the-art deep models. Presence of occluding [...]

By | August 17th, 2018|Categories: ORI Blog|Comments Off on Road Boundary Detection
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