Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
BCDiff: Bidirectional Consistent Diffusion for Instantaneous Trajectory Prediction
Authors: Rongqing Li, Changsheng Li, Dongchun Ren, Guangyi Chen, Ye Yuan, Guoren Wang
NeurIPS 2023 | Venue PDF | 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 EMAIL Changsheng Li Beijing Institute of Technology EMAIL Dongchun Ren ALLRIDE.AI EMAIL Guangyi Chen CMU & MBZUAI EMAIL Ye Yuan Beijing Institute of Technology EMAIL Guoren Wang Beijing Institute of Technology EMAIL |
| 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. |