# Virtual passengers for real car solutions: synthetic datasets

arXiv: https://arxiv.org/abs/2205.06556 · HTML: https://mlsanity.com/paper/2205.06556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 8 of 8)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Virtual Worlds as Proxy for Multi-Object Tracking Analysis](https://mlsanity.com/paper/1605.06457/) (2016)
- [Rendering of Eyes for Eye-Shape Registration and Gaze Estimation](https://mlsanity.com/paper/1505.05916/) (2015)
- [DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis](https://mlsanity.com/paper/2008.12085/) (2020)
- [Real-time Distracted Driver Posture Classification](https://mlsanity.com/paper/1706.09498/) (2017)
- [SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark](https://mlsanity.com/paper/2001.03483/) (2020)
- [PeopleSansPeople: A Synthetic Data Generator for Human-Centric Computer Vision](https://mlsanity.com/paper/2112.09290/) (2021)
- [nuReality: A VR environment for research of pedestrian and autonomous vehicle interactions](https://mlsanity.com/paper/2201.04742/) (2022)

## Abstract (arXiv)

Strategies that include the generation of synthetic data are beginning to be viable as obtaining real data can be logistically complicated, very expensive or slow. Not only the capture of the data can lead to complications, but also its annotation. To achieve high-fidelity data for training intelligent systems, we have built a 3D scenario and set-up to resemble reality as closely as possible. With our approach, it is possible to configure and vary parameters to add randomness to the scene and, in this way, allow variation in data, which is so important in the construction of a dataset. Besides, the annotation task is already included in the data generation exercise, rather than being a post-capture task, which can save a lot of resources. We present the process and concept of synthetic data generation in an automotive context, specifically for driver and passenger monitoring purposes, as an alternative to real data capturing.