mlsanityData to 7 Oct 2026

Learning a Driving Simulator

arXiv 1608.01230 ↗First posted 3 Aug 2016cs.LG stat.MLcited by 33

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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.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 3; 2023: 7; 2024: 7; 2025: 5; 2026: 1.

33 papers.

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  1. 2024Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllabilitycited by 337
  2. 2024Diffusion for World Modeling: Visual Details Matter in Ataricited by 285
  3. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  4. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  5. 2024IRASim: A Fine-Grained World Model for Robot Manipulationcited by 93
  6. 2024DrivingWorld: Constructing World Model for Autonomous Driving via Video GPTcited by 66
  7. 2024Solving Motion Planning Tasks with a Scalable Generative Modelcited by 40
  8. 2025T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generationcited by 35
  9. 2025A Survey of Interactive Generative Videocited by 23
  10. 2022A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperationcited by 21
  11. 2024InfinityDrive: Breaking Time Limits in Driving World Modelscited by 21
  12. 2021End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Drivingcited by 16
21 more
  1. 2021Sim2Air - Synthetic aerial dataset for UAV monitoringcited by 16
  2. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  3. 2022Multi-modal Fusion Technology based on Vehicle Information: A Surveycited by 13
  4. 2021Behavioral Research and Practical Models of Drivers' Attentioncited by 9
  5. 2023Adversarial Attack with Raindropscited by 7
  6. 2021Diverse Complexity Measures for Dataset Curation in Self-drivingcited by 6
  7. 2021SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learningcited by 6
  8. 2025Learning to Drive from a World Modelcited by 6
  9. 2025Other Vehicle Trajectories Are Also Needed: A Driving World Model Unifies Ego-Other Vehicle Trajectories in Video Latent Spacecited by 5
  10. 2025Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectivescited by 5
  11. 2026Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithmscited by 5
  12. 2021Multi-Agent Advisor Q-Learningcited by 4
  13. 2023Confidence-based federated distillation for vision-based lane-centeringcited by 3
  14. 2023Bridging Data-Driven and Knowledge-Driven Approaches for Safety-Critical Scenario Generation in Automated Vehicle Validationcited by 3
  15. 2021Improving Robustness of Learning-based Autonomous Steering Using Adversarial Imagescited by 2
  16. 2024End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learningcited by 2
  17. 2023LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulatorcited by 1
  18. 2021Tackling Variabilities in Autonomous Drivingcited by 0
  19. 2021Brain-Inspired Deep Imitation Learning for Autonomous Driving Systemscited by 0
  20. 2021Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Datacited by 0
  21. 2022Memory based neural networks for end-to-end autonomous drivingcited by 0