[1]LU Yifan,LI Xuanpeng,XUE Qifan.Vehicle trajectory prediction model for unseen domain scenarios[J].CAAI Transactions on Intelligent Systems,2024,19(5):1238-1247.[doi:10.11992/tis.202306046]
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Vehicle trajectory prediction model for unseen domain scenarios
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
19
Number of periods:
2024 5
Page number:
1238-1247
Column:
学术论文—智能系统
Public date:
2024-09-05
- Title:
- Vehicle trajectory prediction model for unseen domain scenarios
- Keywords:
- track prediction; domain generalization; invariant risk minimization; conditional variational auto encoder; endpoint generating; vector map; scene context; time-series networks
- CLC:
- TP391
- DOI:
- 10.11992/tis.202306046
- Abstract:
- With the rapid development of autonomous driving technology, trajectory prediction has become an essential component of smart car software systems. To address the limitations in the generalization of traditional vehicle trajectory prediction models, this study proposes a vehicle trajectory prediction method based on generalized endpoint prediction and vector maps. This method employs a conditional variational autoencoder based on invariant risk minimization to generate trajectory endpoints. The accuracy of the model in predicting unseen domains is improved by integrating map scene features encoded by a time-series network. Experiments were conducted using the interaction dataset, which includes interactive driving scenarios. Experimental results showed that, compared with the best-performing state-of-the-art (SOTA) method REx, the mADE values (where lower is better) decreased by 0%, 36.59%, and 50.68% at 1, 2, and 3 s, respectively. The accuracy of the predicted trajectories in the unseen test domain was significantly improved.
- References:
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Last Update:
2024-09-05