Combinative improvement of adaptable prediction using half-model based methods

Hengyu Cao

doi:10.1117/12.2626925

2 March 2022 Combinative improvement of adaptable prediction using half-model based methods

Hengyu Cao

Proceedings Volume 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021); 1215815 (2022) https://doi.org/10.1117/12.2626925
Event: 2021 International Conference on Computer Vision and Pattern Analysis, 2021, Guangzhou, China

Abstract

This paper covers adjustment and improvement of optimized adaptable prediction using non-model based methods and neural network. The project has combined the advantages of conventional model-based adaptable prediction and the updated adaptable prediction model based on a trained neural network. While the updated adaptable prediction model can increase the prediction performance in large data slope condition, the conventional adaptable prediction model can also correct the prediction error when slope of data is small. All of the obtained results will be analyzed and compared with model-based results. Limitations of each model will also be described in the context. This paper proposes a half-model based prediction for vehicle interactions in unstructured environments.

Citation Download Citation

Hengyu Cao "Combinative improvement of adaptable prediction using half-model based methods", Proc. SPIE 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021), 1215815 (2 March 2022); https://doi.org/10.1117/12.2626925

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