This paper presents a new vision sensor-based road-following method for unmanned vehicles. Usually, the performance obtained with such methods is limited by several factors like image quality according to camera types (charge-coupled device CCD/CMOS), mounted camera position, stereo-/single-vision sensors, structured/unstructured environment, and image noise (illumination and shadow). Existing road-following algorithms for unmanned vehicles perform well, given a certain number of satisfied constraints, so there is a lack of flexibility in their use in real-world situations. The currently proposed local-difference-probability-based method overcomes most of these constraints assuring flexibility in real-world environments
['Pangyu Jeong', 'Sergiu Nedevschi']
Local Difference Probability (LDP)-Based Environment Adaptive Algorithm for Unmanned Ground Vehicle