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..
DriveGazen: Event-Based Driving Status Recognition Using Conventional Camera
Authors: Xiaoyin Yang, Xin Yang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We specifically collected the Driving Status (Drive Gaze) dataset to demonstrate the effectiveness of our approach. Additionally, we validate the superiority of the Drive Gazen on the Single-eye Event-based Emotion (SEE) dataset. To the best of our knowledge, our method is the first to utilize guide attention spiking neural networks and eye-based event frames generated from conventional cameras for driving status recognition. |
| Researcher Affiliation | Academia | Xiaoyin Yang, Xin Yang Dalian University of Technology EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model architecture and processes using mathematical equations and descriptive text, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper states: "Please refer to our project page and supplementary materials for more details." However, it does not provide a direct link to a code repository or an explicit statement confirming the immediate release of the source code for the described methodology. |
| Open Datasets | Yes | We specifically collected the Driving Status (Drive Gaze) dataset to demonstrate the effectiveness of our approach. Additionally, we validate the superiority of the Drive Gazen on the Single-eye Event-based Emotion (SEE) dataset. ... The first publicly available eye-based event-driven driving state dataset generated from conventional cameras, containing intensity frames and corresponding events, capturing data from different ages, races, genders, etc; |
| Dataset Splits | Yes | In total, Drive Gaze includes 1645 sequences/245365 frames of original events, with a total duration of 68.1 minutes(Figure 4, divided into 1316 for training and 329 for testing. |
| Hardware Specification | Yes | We trained ADSN for 150 epochs using a batch size of 128 on an NVIDIA TITAN V GPU. |
| Software Dependencies | No | ADSN is implemented in Py Torch (Paszke et al. 2019). While PyTorch is mentioned, a specific version number is not provided, nor are versions for any other key software dependencies. |
| Experiment Setup | Yes | We trained ADSN for 150 epochs using a batch size of 128 on an NVIDIA TITAN V GPU.For the SNN settings, we use a spiking threshold of 0.3 and a decay factor of 0.2 for all SNN neurons. |