G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction
Authors: Zhanwei Zhang, Zishuo Hua, Minghao Chen, Wei Lu, Binbin Lin, Deng Cai, Wenxiao Wang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our G2LTraj significantly improves the performance of seven existing trajectory predictors across the ETH, UCY and nu Scenes datasets. Experimental results demonstrate its effectiveness. |
| Researcher Affiliation | Collaboration | Zhanwei Zhang1 , Zishuo Hua2 , Minghao Chen3 , Wei Lu5 , Binbin Lin2,4 , Deng Cai1 and Wenxiao Wang2 1State Key Lab of CAD&CG, Zhejiang University 2School of Software Technology, Zhejiang University 3Hangzhou Dianzi University 4Zhiyuan Research Institute 5Ningbo Beilun Third Container Terminal Co., Ltd |
| Pseudocode | Yes | Algorithm 1 Global-to-local Generation Process Input: The granularity of key steps L, the predicted key coordinates { ˆZ1+i L}N i=0, two-layer MLPs {ϕl}l {L,L/2,...,2}, the position embedding {p }1+NL =0 , and agent features A. Output: A predicted future trajectory ˆFL R(1+NL) 2 for agent a. 1: Let l L. 2: while l > 1 do 3: Calculate n ˆZ1+(i+ 1 i=0 by Eq. (6) in parallel with ϕl, ˆZ1+il, p1+il, ˆZ1+(i+1)l, p1+(i+1)l and A as input; 4: l l/2. 5: l denotes the current interval of existing adjacent predicted steps. Notably, we set L {2, 4, 8, ...} to ensure l/2 is an integer value continuously. When l = 1, it indicates that the entire trajectory coordinates of 1 + NL steps have already been generated. 6: end while |
| Open Source Code | Yes | Code will be available at https://github. com/Zhanwei-Z/G2LTraj. |
| Open Datasets | Yes | Datasets. We conducted experiments on three widely-used datasets: ETH [Pellegrini et al., 2009], UCY [Lerner et al., 2007] and nu Scenes [Caesar et al., 2020]. |
| Dataset Splits | Yes | We employ the standard leave-one-out strategy [Bae et al., 2023; Alahi et al., 2016] for training and evaluation. The task in ETH and UCY is to predict the future 12 steps (4.8 seconds) based on the observed 8 steps (3.2 seconds). nu Scenes is a large-scale trajectory dataset that includes both vehicles and pedestrians. The task in nu Scenes is to predict the future 12 steps (6 seconds) given the observed 4 steps (2 seconds) and the High-Definition (HD) maps. We evaluate the results on nu Scenes validation set with K=10, following [Girgis et al., 2022]. |
| Hardware Specification | Yes | The model is trained using a single 3090 GPU. |
| Software Dependencies | No | The paper mentions optimizers (Adam W, Adam) and neural network components (MLP), but it does not specify version numbers for any programming languages, frameworks, or libraries used in the implementation. |
| Experiment Setup | Yes | Specifically, for ETH and UCY, we employ the Adam W optimizer [Loshchilov and Hutter, 2018], set the batch size to 128, use a learning rate of 0.001, and train for 256 epochs. For nu Scenes, we utilize the Adam optimizer [Kingma and Ba, 2014], set the batch size to 64, use a learning rate of 7.5e 4, and train for 80 epochs. |