Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
STraj: Self-training for Bridging the Cross-Geography Gap in Trajectory Prediction
Authors: Zhanwei Zhang, Minghao Chen, Zhihong Gu, Xinkui Zhao, Zheng Yang, Binbin Lin, Deng Cai, Wenxiao Wang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiment results on various cross-geography trajectory prediction benchmarks demonstrate the effectiveness of STraj. Code https://github.com/Zhanwei-Z/STraj... Extensive experiment results validate its effectiveness and generalization ability... As shown in Table 1, our STraj surpasses all competitive predictors by convincing margins across various cross-geography tasks in most cases... We conduct several ablation studies, which are conducted on the MIA PIT task with Lane GCN (Liang et al. 2020) and evaluated with K=1. Architecture Designs. As shown in Table 2, we compare the results of using different components. |
| Researcher Affiliation | Collaboration | 1State Key Lab of CAD&CG, Zhejiang University 2Hangzhou Dianzi University 3Beijing Automobile Works 4School of Software Technology, Zhejiang University 5FABU Inc. |
| Pseudocode | Yes | Algorithm 1: Algorithm of the Pseudo Label Update Strategy |
| Open Source Code | Yes | Code https://github.com/Zhanwei-Z/STraj |
| Open Datasets | Yes | We evaluate our proposed STraj on the widely used trajectory prediction datasets Argoverse 1 (Chang et al. 2019). Argoverse 1 comprises more than 300K real-world driving sequences collected in two geographically diverse cities, i.e., Miami (MIA) and Pittsburgh (PIT). |
| Dataset Splits | Yes | We split half of the validation sets as the test sets for the convenience of separately evaluating each domain. The detailed cross-geography UDA experiments on Argoverse 1 are as follows: MIA โ PIT and PIT โ MIA. |
| Hardware Specification | Yes | In the pre-training and training process, we exploit Adam (Kingma and Ba 2014) with learning rate 1.5 ยท 10โ3 for 30 epochs, and train the model on four A6000 GPUs. |
| Software Dependencies | No | The paper mentions using Adam optimizer and building upon Lane GCN and HPNet, but does not specify software versions for programming languages, libraries, or frameworks like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | In the pre-training process, We set ฯa, r and the weight of LMSE as 1, 10 and 0.01, respectively. As for the update strategy, TU and ฯt are set as 3/2 and 2. We set Tc as a dynamic threshold, which exceeds half confidence scores of all target domain samples in the current epoch. In the trajectory-induced contrastive learning module, we set ฯc of inter-domain and intra-domain ฯc as 1 and 2, respectively. The trade-off parameter ฮท is set as 0.1. Our STraj builds upon a popular predictor Lane GCN (Liang et al. 2020) and a state-of-the-art (SOTA) predictor HPNet(Tang et al. 2024) for Argoverse 1, following their default model parameters. In the pre-training and training process, we exploit Adam (Kingma and Ba 2014) with learning rate 1.5 ยท 10โ3 for 30 epochs. |