A method for detecting an unstructured road boundary, comprising: obtaining a color image; converting the color image to a HIS color space; selecting (S105, S106) a candidate road region within the color image according to a road model selected within a plurality of candidate road models; identifying a seed pixel from the candidate road region; obtaining a brightness threshold and a color threshold, where the brightness threshold and the color threshold are determined according to brightness distances and color distances from pixels in the candidate road region to the seed pixel; and performing road segmentation to the candidate road region by determining whether the pixels in the candidate road region belong to a road region based on the brightness threshold and the color threshold and performing road regional growth; and determining that the selected road model matches an unstructured road when a percentage of the number of pixels in the road segmentation result belong to the candidate road region; characterized in preprocessing the obtained color image, which comprises: sampling the color image using a sampling frame which has a predetermined height and a predetermined width; calculating norms of vectors, which respectively correspond to pixels in the sampling frame, where each of the vectors represents a line pointing from an original point to a point corresponding to a pixel in a color space; identifying a first predetermined number of vectors by filtering out a first number of vectors having the maximum norms of vectors; obtaining a weighted average vector, where a m th component of the weighted average vector is obtained by calculating weighted average of m th components of the first predetermined number of vectors; calculating norms of differences between the first predetermined number of vectors and the weighted average vector; identifying, from the first predetermined number of vectors, a second predetermined number of vectors which have the minimum norms of differences with the weighted average vector; and obtaining median vectors based on the second predetermined number of vectors, to obtain a preprocessed color image, where the candidate road region is selected within the preprocessed color image. obtaining a color image; converting the color image to a HIS color space; selecting (S105, S106) a candidate road region within the color image according to a road model selected within a plurality of candidate road models; identifying a seed pixel from the candidate road region; obtaining a brightness threshold and a color threshold, where the brightness threshold and the color threshold are determined according to brightness distances and color distances from pixels in the candidate road region to the seed pixel; and performing road segmentation to the candidate road region by determining whether the pixels in the candidate road region belong to a road region based on the brightness threshold and the color threshold and performing road regional growth; and determining that the selected road model matches an unstructured road when a percentage of the number of pixels in the road segmentation result belong to the candidate road region; sampling the color image using a sampling frame which has a predetermined height and a predetermined width; calculating norms of vectors, which respectively correspond to pixels in the sampling frame, where each of the vectors represents a line pointing from an original point to a point corresponding to a pixel in a color space; identifying a first predetermined number of vectors by filtering out a first number of vectors having the maximum norms of vectors; obtaining a weighted average vector, where a m th component of the weighted average vector is obtained by calculating weighted average of m th components of the first predetermined number of vectors; calculating norms of differences between the first predetermined number of vectors and the weighted average vector; identifying, from the first predetermined number of vectors, a second predetermined number of vectors which have the minimum norms of differences with the weighted average vector; and obtaining median vectors based on the second predetermined number of vectors, to obtain a preprocessed color image, where the candidate road region is selected within the preprocessed color image.