Titlebook: Road Terrain Classification Technology for Autonomous Vehicle; Shifeng Wang Book 2019 China Machine Press, Beijing and Springer Nature Sin

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Image-Based Road Terrain Classification,camera to capture images of road surfaces in an attempt to overcome the issues found using accelerometer data only. By processing and extracting features from those images, road terrain classification is expected to be improved.

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Summary,RF were employed and investigated, respectively. However, all of these sensors have their own advantages and disadvantages. To improve the classification accuracy of the LRF that works for predicting the forthcoming road terrain, an MRF multiple-sensor fusion method was then proposed.

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Introduction,e powerful sensors and computer units allows developing smarter transportation systems to ease or solve these issues. Smart cars are often envisioned to be a cornerstone of these smart transportation systems.

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Book 2019 method is extremely robust and effective in terms of classifying road terrain. The book also demonstrates numerous applications of road terrain classification for various environments and types of autonomous vehicle, and includes abundant illustrations and models to make the comparison tables and figures more accessible.