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..
MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving
Authors: Haicheng Liao, Zhenning Li, Chengyue Wang, Huanming Shen, Dongping Liao, Bonan Wang, Guofa Li, Chengzhong Xu
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations on the Argoverse, NGSIM, High D, and Mo CAD datasets underscore MFTraj s robustness and adaptability, outperforming numerous benchmarks even in data-challenged scenarios without the need for additional information such as HD maps or vectorized maps. |
| Researcher Affiliation | Academia | Haicheng Liao1 , Zhenning Li1 , Chengyue Wang1 , Huanming Shen2 , Dongping Liao1 , Bonan Wang1 , Guofa Li3 , Chengzhong Xu1 1University of Macau 2University of Electronic Science and Technology of China 3Chongqing University |
| Pseudocode | No | The paper describes its methods using prose, equations, and architectural diagrams, but it does not include pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement about making its source code publicly available or include a link to a code repository. |
| Open Datasets | Yes | Datasets. We tested model s efficacy on Argoverse [Chang et al., 2019], NGSIM [Deo and Trivedi, 2018], High D [Krajewski et al., 2018], and Mo CAD [Liao et al., 2024b] datasets. |
| Dataset Splits | No | The paper describes data segmentation for observation and prediction horizons but does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | Yes | We implemented our model using Py Torch and Py Torch-lightning on an NVIDIA DGX-2 with eight V100 GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch and Py Torch-lightning' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | Using the smooth L1 loss as our loss function, the model was trained with the Adam optimizer, a batch size of 32, and learning rates of 10 3 and 10 4. |