# Data generation using simulation technology to improve perception mechanism of autonomous vehicles

arXiv: https://arxiv.org/abs/2207.00191 · HTML: https://mlsanity.com/paper/2207.00191/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 1 Jul 2022.

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

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?](https://mlsanity.com/paper/1610.01983/) (2016)
- [Virtual to Real Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1704.03952/) (2017)
- [A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving](https://mlsanity.com/paper/1804.00103/) (2018)
- [Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash](https://mlsanity.com/paper/1805.11815/) (2018)
- [Deep Semantic Classification for 3D LiDAR Data](https://mlsanity.com/paper/1706.08355/) (2017)

## Built on by (most cited first, 5 of 5)

- [Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation](https://mlsanity.com/paper/2305.10361/) (2023)
- [On the Assessment of Sensitivity of Autonomous Vehicle Perception](https://mlsanity.com/paper/2602.00314/) (2026)
- [Hybrid data driven/thermal simulation model for comfort assessment](https://mlsanity.com/paper/2309.01734/) (2023)
- [Systematic Literature Review of Vision-Based Approaches to Outdoor Livestock Monitoring with Lessons from Wildlife Studies](https://mlsanity.com/paper/2410.05041/) (2024)
- [PairedGTA: Generating Driving Datasets for Controlled Photometric Shift Analysis](https://mlsanity.com/paper/2606.01192/) (2026)

## Abstract (arXiv)

Recent advancements in computer graphics technology allow more realistic ren-dering of car driving environments. They have enabled self-driving car simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large amounts of synthetic data that can complement the existing real-world dataset in training autonomous car perception. Furthermore, since self-driving car simulators allow full control of the environment, they can generate dangerous driving scenarios that the real-world dataset lacks such as bad weather and accident scenarios. In this paper, we will demonstrate the effectiveness of combining data gathered from the real world with data generated in the simulated world to train perception systems on object detection and localization task. We will also propose a multi-level deep learning perception framework that aims to emulate a human learning experience in which a series of tasks from the simple to more difficult ones are learned in a certain domain. The autonomous car perceptron can learn from easy-to-drive scenarios to more challenging ones customized by simulation software.