Abstract
According to statistics, the incidence of traffic accidents in China is far greater than that of developed countries such as the USA and Germany. Nearly 100,000 people die in traffic accidents every year in China. The use of the assisted driving system effectively reduces the incidence of traffic accidents, while road surface object recognition is the key research object in the assisted driving system. At present, in the research on the recognition of pavement objects, there are problems such as low recognition rate and long recognition time, which cannot play the role of assisting driving. In response to these problems, this paper proposes a research on pavement object recognition based on machine learning, including analysis of key technologies of machine learning and optimization of related algorithms. In order to verify the feasibility of the proposed method in the assisted driving system, the research results are applied to the road surface object recognition device and the experiment is carried out under the actual traffic environment. The results show that in the selected experimental scenes, the recognition accuracy of the road surface object recognition device applying the research results of this paper is 100%, and the recognition time is far lower than the traditional road object recognition method. The results show that the proposed method can quickly and effectively identify the pavement object, and then assist the driver to control the vehicle, which has good applicability in the assisted driving system.
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This work was supported by Shandong Important R&D Program of China (No. 2018YFJH0306).
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Gao, H., Yuan, L. Research on key technology of pavement object recognition based on machine learning. Neural Comput & Applic 32, 5483–5493 (2020). https://doi.org/10.1007/s00521-019-04643-6
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DOI: https://doi.org/10.1007/s00521-019-04643-6