Abstract
Vehicle drivability and maneuverability can be improved by increasing the environment awareness via sensory inputs. In particular, off-road capable vehicles possess subsystems which are configurable to the driving conditions. In this work, a vision solution is explored as a precursor to autonomous toggling between different operating modes. The emphasis is on selecting an appropriate response to transitions from one terrain type to another. Given a forward facing camera, images are partitioned into pixel subsets known as superpixels in order to be classified. The quality of this semantic segmentation is considered for classes such as {grass, tree, sky, tarmac, dirt, gravel, shrubs}. Colour and texture are combined together to form visual cues and address this image recognition problem with good segmentation results.
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This work has been funded by the EPSRC (Engineering and Physical Sciences Research Council) in collaboration with Jaguar Land Rover.
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Gheorghe, I., Li, W., Popham, T., Burnham, K.J. (2015). Superpixel based semantic segmentation for assistance in varying terrain driving conditions. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_98
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DOI: https://doi.org/10.1007/978-3-319-08422-0_98
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