# Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving

arXiv: https://arxiv.org/abs/1902.03442 · HTML: https://mlsanity.com/paper/1902.03442/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities](https://mlsanity.com/paper/2401.08045/) (2024)
- [Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies](https://mlsanity.com/paper/2112.11937/) (2021)
- [Cluster-guided Image Synthesis with Unconditional Models](https://mlsanity.com/paper/2112.12911/) (2021)
- [Ensemble-based Semi-supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving](https://mlsanity.com/paper/2105.07930/) (2021)
- [Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird's-Eye-View](https://mlsanity.com/paper/2402.00637/) (2024)
- [Exiting the Simulation: The Road to Robust and Resilient Autonomous Vehicles at Scale](https://mlsanity.com/paper/2210.10876/) (2022)
- [ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events](https://mlsanity.com/paper/2308.14550/) (2023)

## Abstract (arXiv)

Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN are perceived as the most impactful direction of machine learning in the last decade. This paper focuses on the application of GAN in autonomous driving including topics such as advanced data augmentation, loss function learning, semi-supervised learning, etc. We formalize and review key applications of adversarial techniques and discuss challenges and open problems to be addressed.