TY - GEN
T1 - Adaptive self-supervised model for trajectory prediction
AU - Wei, Xinmeng
AU - Guo, Yangming
AU - Long, Jiang
AU - Liu, Mengxuan
AU - Lu, Sheng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The prediction of trajectory holds significant importance in improving vehicle safety and enhancing traffic intelligence. Since the existing models ignore the problem of trajectory bias due to possible missing sensor data in realistic scenarios, this paper presents a novel approach to enhance the accuracy of trajectory prediction utilizing a CNN-Transformer adaptive complementary self-supervised model. The model combines complementary learning of CNN local features and Transformer global features, building upon the advancements of the PishguVe model. Furthermore, the present study employs masks to simulate sensor data aberrations or data loss that may occur during real driving scenarios. Additionally, self-supervised learning is utilized to enhance the robustness and generalization of the model. Upon evaluation of the proposed model on the CHD eye-level datasets, it is observed that the Average Displacement Error (ADE) decreased to 18.02, which is 7.25% lower than that of the current leading model, the PishguVe model. When assessing the CHD high-angle datasets, the Average Displacement Error (ADE) is determined to be 16.80 pixels, and the Final Displacement Error (FDE) is calculated to be 59.67 pixels. The algorithms in question exhibit lower values compared to the currently optimal PishguVe's ADE metric of 3.28% and FDE metric of 3.74%, demonstrating the superior performance of the proposed model.
AB - The prediction of trajectory holds significant importance in improving vehicle safety and enhancing traffic intelligence. Since the existing models ignore the problem of trajectory bias due to possible missing sensor data in realistic scenarios, this paper presents a novel approach to enhance the accuracy of trajectory prediction utilizing a CNN-Transformer adaptive complementary self-supervised model. The model combines complementary learning of CNN local features and Transformer global features, building upon the advancements of the PishguVe model. Furthermore, the present study employs masks to simulate sensor data aberrations or data loss that may occur during real driving scenarios. Additionally, self-supervised learning is utilized to enhance the robustness and generalization of the model. Upon evaluation of the proposed model on the CHD eye-level datasets, it is observed that the Average Displacement Error (ADE) decreased to 18.02, which is 7.25% lower than that of the current leading model, the PishguVe model. When assessing the CHD high-angle datasets, the Average Displacement Error (ADE) is determined to be 16.80 pixels, and the Final Displacement Error (FDE) is calculated to be 59.67 pixels. The algorithms in question exhibit lower values compared to the currently optimal PishguVe's ADE metric of 3.28% and FDE metric of 3.74%, demonstrating the superior performance of the proposed model.
KW - self-supervised learning
KW - trajectory prediction
KW - Transformer
UR - http://www.scopus.com/inward/record.url?scp=85188258424&partnerID=8YFLogxK
U2 - 10.1109/AIoTSys58602.2023.00054
DO - 10.1109/AIoTSys58602.2023.00054
M3 - 会议稿件
AN - SCOPUS:85188258424
T3 - Proceedings - 2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023
SP - 221
EP - 226
BT - Proceedings - 2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023
Y2 - 19 October 2023 through 22 October 2023
ER -