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..
A Driving-Style-Adaptive Framework for Vehicle Trajectory Prediction
Authors: Di Wen, Yu Wang, Zhigang Wu, Zhaocheng He, Zhe Wu, Zheng Qingfang
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public real-world datasets demonstrate that the DSA framework outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Sun Yat-sen University 2Pengcheng Laboratory 3Guangdong Provincial Key Laboratory of Intelligent Transportation System EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm: Polynomial Combination |
| Open Source Code | Yes | We provide the code in supplemental material. |
| Open Datasets | Yes | Extensive experiments on public real-world datasets demonstrate that the DSA framework outperforms state-of-the-art methods. ... We evaluate our DSA framework on three real-world vehicle trajectory prediction datasets: nu Scenes [67], Argoverse [68] and Waymo [69]. |
| Dataset Splits | No | Specifically, the degree n is treated as a hyperparameter optimization problem, aimed at minimizing the loss (L) on validation data Dval and training data Dtrain. While Dtrain and Dval are mentioned, the specific split percentages or methodology for these splits are not provided in the paper. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) are provided in the paper. |
| Software Dependencies | No | To tackle this issue, we utilize SMAC3 [66] tool, which is particularly suitable for optimizing low-dimensional and continuous functions... However, no specific version number for SMAC3 or other main software dependencies are provided. |
| Experiment Setup | Yes | We utilize Loss = λ1LDis + λ2LMo E-K, with LMo E-K = wload CV (loads)2, for model training with balanced weighting parameters λ . ... Specifically, the degree n is treated as a hyperparameter optimization problem, aimed at minimizing the loss (L) on validation data Dval and training data Dtrain. |