TY - GEN
T1 - Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain
AU - Neumeier, Marion
AU - Dorn, Sebastian
AU - Botsch, Michael
AU - Utschick, Wolfgang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios. Based on a spatiotemporal vehicle interaction graph, an observed traffic scenario can be transformed into the graph spectral domain by means of the multidimensional Graph Fourier Transformation. Since these spectral scenario representations have shown to successfully incorporate the complex and interactive nature of traffic scenarios, the beneficial feature representation is employed for the purpose of predicting vehicle trajectories. This work introduces GFTNNv2, a deep learning network predicting vehicle trajectories in the graph spectral domain. Evaluation of the GFTNNv2 on the publicly available datasets highD and NGSIM shows a performance gain of up to 25 % in comparison to state-of-the-art prediction approaches.
AB - This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios. Based on a spatiotemporal vehicle interaction graph, an observed traffic scenario can be transformed into the graph spectral domain by means of the multidimensional Graph Fourier Transformation. Since these spectral scenario representations have shown to successfully incorporate the complex and interactive nature of traffic scenarios, the beneficial feature representation is employed for the purpose of predicting vehicle trajectories. This work introduces GFTNNv2, a deep learning network predicting vehicle trajectories in the graph spectral domain. Evaluation of the GFTNNv2 on the publicly available datasets highD and NGSIM shows a performance gain of up to 25 % in comparison to state-of-the-art prediction approaches.
UR - http://www.scopus.com/inward/record.url?scp=85186518142&partnerID=8YFLogxK
U2 - 10.1109/ITSC57777.2023.10422530
DO - 10.1109/ITSC57777.2023.10422530
M3 - Conference contribution
AN - SCOPUS:85186518142
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1172
EP - 1179
BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
Y2 - 24 September 2023 through 28 September 2023
ER -