# CARLA-BSP: a simulated dataset with pedestrians

arXiv: https://arxiv.org/abs/2305.00204 · HTML: https://mlsanity.com/paper/2305.00204/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 29 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [UniPose: Unified Human Pose Estimation in Single Images and Videos](https://mlsanity.com/paper/2001.08095/) (2020)
- [Agreeing to Cross: How Drivers and Pedestrians Communicate](https://mlsanity.com/paper/1702.03555/) (2017)
- [SRA-LSTM: Social Relationship Attention LSTM for Human Trajectory Prediction](https://mlsanity.com/paper/2103.17045/) (2021)

## Built on by (most cited first, 3 of 3)

- [Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction](https://mlsanity.com/paper/2401.06757/) (2024)
- [HABIT: Human Action Benchmark for Interactive Traffic in CARLA](https://mlsanity.com/paper/2511.19109/) (2025)
- [From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems](https://mlsanity.com/paper/2606.31589/) (2026)

## Abstract (arXiv)

We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestrian detection, autoencoding, pose estimation, and pose lifting. We also showcase baseline results. For more information, visit https://project-arcane.eu/.