Experiments in Autonomous Driving Through Imitation Learning
On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving
Abstract (arXiv)
This report demonstrates several methods used to make a self-driving vehicle using a supervised learning algorithm and a forward-facing RGBD camera. The project originally involved research in creating an adversarial attack on the vehicle's model, but due to difficulties with the initial training of the car, the plans were discarded in favor of completing the imitation learning portion of the project. Many approaches were explored, but due to challenges introduced by an unbalanced data set, the approaches had limited effectiveness.
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 1 of its 5 references that are in the map. First posted on arXiv 25 Nov 2020.
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1 of 5 references.
References · 1
- 2019Fooling a Real Car with Adversarial Traffic Signscited by 29
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- 2021Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments
- 2025Imitation Learning for Autonomous Driving: Insights from Real-World Testing
- 2025Learning to Drive by Imitating Surrounding Vehicles
- 2020Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning
- 2021Semi-Supervised Imitation Learning with Mixed Qualities of Demonstrations for Autonomous Driving
- 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
- 2023Human-Like Autonomous Driving on Dense Traffic
- 2021Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning