Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction
Authors: Daehee Park, Hobin Ryu, Yunseo Yang, Jegyeong Cho, Jiwon Kim, Kuk-Jin Yoon
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method on popular trajectory prediction datasets: nu Scenes and Argoverse. The results show that the proposed method brings remarkable performance gain in prediction accuracy, and achieves state-of-the-art performance in long-term prediction benchmark dataset. |
| Researcher Affiliation | Collaboration | 1Korea Advanced Institute of Science and Technology 2NAVER LABS |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks clearly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository for the described methodology. It mentions 'For implementation and computation details, please refer to the supplementary material.' but this does not guarantee public code availability. |
| Open Datasets | Yes | We train and evaluate our method on two popular real-world trajectory datasets: nu Scenes ( Caesar et al. (2020)) and Argoverse ( Chang et al. (2019)). |
| Dataset Splits | Yes | Training/validation/test sets consist of real-world driving scenes of 32,186/8,560/9,041 in nu Scenes and 205,942/39,472/78,143 in Argoverse. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It mentions 'For implementation and computation details, please refer to the supplementary material.' but no details are in the main text. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers. It mentions 'For implementation and computation details, please refer to the supplementary material.' but no details are in the main text. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values or training configurations in the main text. It refers to supplementary material for 'implementation and computation details'. |