mlsanityData to 7 Oct 2026

Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously

arXiv 2007.03514 ↗First posted 7 Jul 2020cs.LG cs.CV cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

In this paper, we describe our winning approach to solving the Lane Following Challenge at the AI Driving Olympics Competition through imitation learning on a mixed set of simulation and real-world data. AI Driving Olympics is a two-stage competition: at stage one, algorithms compete in a simulated environment with the best ones advancing to a real-world final. One of the main problems that participants encounter during the competition is that algorithms trained for the best performance in simulated environments do not hold up in a real-world environment and vice versa. Classic control algorithms also do not translate well between tasks since most of them have to be tuned to specific driving conditions such as lighting, road type, camera position, etc. To overcome this problem, we employed the imitation learning algorithm and trained it on a dataset collected from sources both from simulation and real-world, forcing our model to perform equally well in all environments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 13 references that are in the map. First posted on arXiv 7 Jul 2020.

Builds on

10 of 13 references.

References · 10

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  3. 2016End to End Learning for Self-Driving Carscited by 1,000
  4. 2014Efficient Object Localization Using Convolutional Networkscited by 226
  5. 2017Domain Adaptation for Visual Applications: A Comprehensive Surveycited by 127
  6. 2017Virtual to Real Reinforcement Learning for Autonomous Drivingcited by 79
  7. 2018On Offline Evaluation of Vision-based Driving Modelscited by 62
  8. 2019Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networkscited by 11
  9. 2018Real-to-Virtual Domain Unification for End-to-End Autonomous Drivingcited by 10
  10. 2019The AI Driving Olympics at NeurIPS 2018cited by 6

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  1. 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
  2. 2025Learning to Drive by Imitating Surrounding Vehicles
  3. 2022A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing
  4. 2019Urban Driving with Conditional Imitation Learning
  5. 2022Exploring the trade off between human driving imitation and safety for traffic simulation
  6. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
  7. 2022An Adaptive Human Driver Model for Realistic Race Car Simulations
  8. 2021Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning