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 [1].
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. |