# Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a driver

arXiv: https://arxiv.org/abs/2204.08096 · HTML: https://mlsanity.com/paper/2204.08096/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 17 Apr 2022.

## Builds on (most cited first, 1 of 1)

- [Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis](https://mlsanity.com/paper/1802.02690/) (2018)

## Built on by (most cited first, 15 of 15)

- [The 6th AI City Challenge](https://mlsanity.com/paper/2204.10380/) (2022)
- [The 7th AI City Challenge](https://mlsanity.com/paper/2304.07500/) (2023)
- [Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition](https://mlsanity.com/paper/2304.07421/) (2023)
- [Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos](https://mlsanity.com/paper/2306.10159/) (2023)
- [The 8th AI City Challenge](https://mlsanity.com/paper/2404.09432/) (2024)
- [DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos](https://mlsanity.com/paper/2304.08261/) (2023)
- [M2DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer](https://mlsanity.com/paper/2305.08877/) (2023)
- [M3Act: Learning from Synthetic Human Group Activities](https://mlsanity.com/paper/2306.16772/) (2023)
- [MVP: Robust Multi-View Practice for Driving Action Localization](https://mlsanity.com/paper/2207.02042/) (2022)
- [PoseViNet: Distracted Driver Action Recognition Framework Using Multi-View Pose Estimation and Vision Transformer](https://mlsanity.com/paper/2312.14577/) (2023)
- [Transformer-based Fusion of 2D-pose and Spatio-temporal Embeddings for Distracted Driver Action Recognition](https://mlsanity.com/paper/2403.06577/) (2024)
- [DeepLocalization: Using change point detection for Temporal Action Localization](https://mlsanity.com/paper/2404.12258/) (2024)
- [Density-Guided Label Smoothing for Temporal Localization of Driving Actions](https://mlsanity.com/paper/2403.06616/) (2024)
- [Towards Infusing Auxiliary Knowledge for Distracted Driver Detection](https://mlsanity.com/paper/2408.16621/) (2024)
- [Spatial-Temporal Perception with Causal Inference for Naturalistic Driving Action Recognition](https://mlsanity.com/paper/2503.04078/) (2025)

## Abstract (arXiv)

This article presents a synthetic distracted driving (SynDD2 - a continuum of SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones. We collected the data in a stationary vehicle using three in-vehicle cameras positioned at locations: on the dashboard, near the rearview mirror, and on the top right-side window corner. The dataset contains two activity types: distracted activities and gaze zones for each participant, and each activity type has two sets: without appearance blocks and with appearance blocks such as wearing a hat or sunglasses. The order and duration of each activity for each participant are random. In addition, the dataset contains manual annotations for each activity, having its start and end time annotated. Researchers could use this dataset to evaluate the performance of machine learning algorithms to classify various distracting activities and gaze zones of drivers.