Motion Forecasting in Continuous Driving
Authors: Nan Song, Bozhou Zhang, Xiatian Zhu, Li Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on Argoverse series with different settings demonstrate that our Real Motion achieves state-of-the-art performance, along with the advantage of efficient real-world inference. |
| Researcher Affiliation | Academia | Nan Song1 Bozhou Zhang1 Xiatian Zhu2 Li Zhang1 1School of Data Science, Fudan University 2University of Surrey |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | https://github.com/fudan-zvg/Real Motion |
| Open Datasets | Yes | We assess the performance of our method using the Argoverse 1 [3] and Argoverse 2 [43] motion forecasting datasets in both single-agent and multi-agent settings. |
| Dataset Splits | Yes | We conduct ablation studies on the Argoverse 2 validation split for the single-agent setting to examine the effectiveness of each component in Real Motion. |
| Hardware Specification | Yes | We measure these approaches and Real Motion on the Argoverse 2 test set using an NVIDIA Ge Force RTX 3090 GPU, maintaining a batch size of 1 and following an end-to-end manner. |
| Software Dependencies | No | The paper mentions using 'Adam W Optimizer' but does not provide specific version numbers for software dependencies or libraries like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We train our models using the Adam W [24] Optimizer with a batch size of 32 per GPU for 60 epochs. Our model is trained end-to-end with a learning rate of 0.001 and a weight decay of 0.01. The latent feature dimension is set to 128. |