# Learning a Driving Simulator

arXiv: https://arxiv.org/abs/1608.01230 · HTML: https://mlsanity.com/paper/1608.01230/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2016.

## Built on by (most cited first, 30 of 33)

- [Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability](https://mlsanity.com/paper/2405.17398/) (2024)
- [Diffusion for World Modeling: Visual Details Matter in Atari](https://mlsanity.com/paper/2405.12399/) (2024)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [IRASim: A Fine-Grained World Model for Robot Manipulation](https://mlsanity.com/paper/2406.14540/) (2024)
- [DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT](https://mlsanity.com/paper/2412.19505/) (2024)
- [Solving Motion Planning Tasks with a Scalable Generative Model](https://mlsanity.com/paper/2407.02797/) (2024)
- [T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generation](https://mlsanity.com/paper/2505.00337/) (2025)
- [A Survey of Interactive Generative Video](https://mlsanity.com/paper/2504.21853/) (2025)
- [A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation](https://mlsanity.com/paper/2208.10590/) (2022)
- [InfinityDrive: Breaking Time Limits in Driving World Models](https://mlsanity.com/paper/2412.01522/) (2024)
- [End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving](https://mlsanity.com/paper/2102.04738/) (2021)
- [Sim2Air - Synthetic aerial dataset for UAV monitoring](https://mlsanity.com/paper/2110.05145/) (2021)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [Multi-modal Fusion Technology based on Vehicle Information: A Survey](https://mlsanity.com/paper/2211.06080/) (2022)
- [Behavioral Research and Practical Models of Drivers' Attention](https://mlsanity.com/paper/2104.05677/) (2021)
- [Adversarial Attack with Raindrops](https://mlsanity.com/paper/2302.14267/) (2023)
- [Diverse Complexity Measures for Dataset Curation in Self-driving](https://mlsanity.com/paper/2101.06554/) (2021)
- [SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning](https://mlsanity.com/paper/2104.06506/) (2021)
- [Learning to Drive from a World Model](https://mlsanity.com/paper/2504.19077/) (2025)
- [Other Vehicle Trajectories Are Also Needed: A Driving World Model Unifies Ego-Other Vehicle Trajectories in Video Latent Space](https://mlsanity.com/paper/2503.09215/) (2025)
- [Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives](https://mlsanity.com/paper/2505.09074/) (2025)
- [Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms](https://mlsanity.com/paper/2603.28489/) (2026)
- [Multi-Agent Advisor Q-Learning](https://mlsanity.com/paper/2111.00345/) (2021)
- [Confidence-based federated distillation for vision-based lane-centering](https://mlsanity.com/paper/2306.03222/) (2023)
- [Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validation](https://mlsanity.com/paper/2311.10937/) (2023)
- [Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images](https://mlsanity.com/paper/2102.13262/) (2021)
- [End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning](https://mlsanity.com/paper/2411.16131/) (2024)
- [LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator](https://mlsanity.com/paper/2310.16103/) (2023)
- [Tackling Variabilities in Autonomous Driving](https://mlsanity.com/paper/2104.10415/) (2021)

## Abstract (arXiv)

Comma.ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road. This paper illustrates one of our research approaches for driving simulation. One where we learn to simulate. Here we investigate variational autoencoders with classical and learned cost functions using generative adversarial networks for embedding road frames. Afterwards, we learn a transition model in the embedded space using action conditioned Recurrent Neural Networks. We show that our approach can keep predicting realistic looking video for several frames despite the transition model being optimized without a cost function in the pixel space.