In order to achieve precise motion control of the vehicle under various road surface conditions, it is essential to identify the road surface conditions accurately and quickly. For low-cost cameras and computing platforms, this paper proposes a reliable data-level fusion identification method that is robust to scenes with shadow. Based on deep-learning models, the candidate regions of interest with confidence level for each road surface condition are obtained. Besides, the fusion identification method based on improved Dempster-Shafer Evidence Theory is designed. The results of evaluation on specific dataset with shadow and anti-lock braking system using proposed method reveal that our proposed fusion method achieves superior performance with stable and accurate predictions.
Reliable Identification of Road Surface Condition Considering Shadow Interference
2021-09-19
2728172 byte
Conference paper
Electronic Resource
English
European Patent Office | 2021
|European Patent Office | 2020
|European Patent Office | 2020
|