GTC-DAN: A graph-temporal convolutional model with dynamic adjacency for vehicle trajectory prediction

• Autorzy: Chen Hao , Wu Xuncheng , Zhang Ruoping , Guo Wenfeng , Chen Yang , Xu Jiejie , Zhang Weiwei , Yu Wangpengfei

Identyfikatory

DOI

10.24425/bpasts.2024.152610

• Języki publikacji: EN

Abstrakty

EN

Autonomous driving is currently an issue of heated debate in automotive engineering. Accurate prediction of the future trajectory of self-driving cars can significantly reduce the occurrence of traffic accidents. However, predicting the future trajectories of vehicles is a challenging task since it is influenced by the interaction behaviours of neighbouring vehicles. This paper proposes a framework that allows for parameter sharing and cross-layer independence, based on a dynamic graph convolutional spatiotemporal network, to study the interactions between vehicles and the temporal dynamics in historical trajectories. By extracting dynamic adjacency matrices from different vehicle interaction features, the model can describe dynamic spatiotemporal relationships and facilitate addressing changes in traffic scenarios. Finally, the proposed model is experimentally compared with existing mainstream trajectory prediction methods using the NGSIM dataset. The results demonstrate that our trajectory prediction model achieved excellent performance in terms of model parameters and prediction accuracy. Compared to the four mainstream models, our model improved accuracy by 35.73%. In addition, we also analyze the relationship between model complexity and efficiency.

Słowa kluczowe

EN

autonomous driving; trajectory prediction; dynamic interactions; spatiotemporal model

PL

przewidywanie trajektorii; interakcja dynamiczna; model czasoprzestrzenny; pojazd autonomiczny; prowadzenie pojazdu

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2025
• Tom: Vol. 73, nr 2
• Strony: art. no. e152610
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Chen Hao

• School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

• https://orcid.org/0009-0000-5848-2178

autor

Wu Xuncheng

• School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

autor

Zhang Ruoping

• School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

autor

Guo Wenfeng

• School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China

autor

Chen Yang

• Shanghai Smart Vehicle Cooperating Innovation Center Co., Ltd., Shanghai, 201805, China

autor

Xu Jiejie

• Shanghai Smart Vehicle Cooperating Innovation Center Co., Ltd., Shanghai, 201805, China

autor

Zhang Weiwei

• Shanghai Smart Vehicle Cooperating Innovation Center Co., Ltd., Shanghai, 201805, China

autor

Yu Wangpengfei

• Shanghai Smart Vehicle Cooperating Innovation Center Co., Ltd., Shanghai, 201805, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).