EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
Authors: Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed framework is evaluated on both synthetic physics simulations and multiple real-world benchmark datasets in various areas. The experimental results illustrate that our approach achieves state-of-the-art performance in terms of prediction accuracy. |
| Researcher Affiliation | Collaboration | Jiachen Li1,2, Fan Yang2, Masayoshi Tomizuka2 Chiho Choi1 1Honda Research Institute, USA 2 University of California, Berkeley |
| Pseudocode | No | The paper describes its methodology using descriptive text and mathematical equations but does not include a formal pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | In this paper, we validated the proposed framework Evolve Graph on one synthetic dataset and three benchmark datasets for real-world applications: Honda 3D Dataset (H3D) [31], NBA Sport VU Dataset (NBA), and Stanford Drone Dataset (SDD) [33]. |
| Dataset Splits | No | The paper describes historical observation periods and future prediction horizons for its data but does not explicitly provide training, validation, and test dataset splits with specific percentages, counts, or methodologies. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | No | The paper describes a double-stage training pipeline but does not provide specific details about experimental setup, such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |