BCDiff: Bidirectional Consistent Diffusion for Instantaneous Trajectory Prediction
Authors: Rongqing Li, Changsheng Li, Dongchun Ren, Guangyi Chen, Ye Yuan, Guoren Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our proposed BCDiff significantly improves the accuracy of instantaneous trajectory prediction on the ETH/UCY and Stanford Drone datasets, compared to related approaches. |
| Researcher Affiliation | Collaboration | Rongqing Li Beijing Institute of Technology lirongqing99@gmail.com Changsheng Li Beijing Institute of Technology lcs@bit.edu.cn Dongchun Ren ALLRIDE.AI Dongchun.ren@allride.ai Guangyi Chen CMU & MBZUAI guangyichen1994@gmail.com Ye Yuan Beijing Institute of Technology yuan-ye@bit.edu.cn Guoren Wang Beijing Institute of Technology wanggrbit@126.com |
| Pseudocode | No | The paper describes the model's architecture and procedures in detail, including figures like 'Structure of BCDUnit', but it does not present any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code for the proposed BCDiff framework, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Dataset. We verify the effectiveness of our proposed method on the widely used ETH/UCY [37, 26] and Stanford Drone [38] Dataset (SDD). |
| Dataset Splits | Yes | We follow the widely used leave-one-scene-out protocol, i.e., the models are trained on 4 scenes and tested on the remaining one [15, 42]. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or specific computing environments used for running the experiments. |
| Software Dependencies | No | The paper refers to various models and architectures (e.g., 'Trajectron++', 'Bi-LSTM', 'Transformer') but does not specify version numbers for any software dependencies, libraries, or programming languages used in the implementation. |
| Experiment Setup | Yes | In the instantaneous trajectory prediction setting, the observations are reduced to 2 frames, and the length of future predictions is 12 frames. Following previous works [46, 15, 31], we sample 20 future predicted trajectories, and report the final error by the minimum error over all predicted trajectories. |