# Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning

arXiv: https://arxiv.org/abs/2010.04767 · HTML: https://mlsanity.com/paper/2010.04767/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 9 Oct 2020.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [A Survey of End-to-End Driving: Architectures and Training Methods](https://mlsanity.com/paper/2003.06404/) (2020)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches](https://mlsanity.com/paper/1803.01164/) (2018)
- [Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car](https://mlsanity.com/paper/1704.07911/) (2017)

## Built on by (most cited first, 11 of 11)

- [AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles](https://mlsanity.com/paper/2101.06549/) (2021)
- [Zero-shot Imitation Policy via Search in Demonstration Dataset](https://mlsanity.com/paper/2401.16398/) (2024)
- [Diffusion Imitation from Observation](https://mlsanity.com/paper/2410.05429/) (2024)
- [AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education](https://mlsanity.com/paper/2212.05241/) (2022)
- [Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM Framework with Mortality Classifier and Transformer](https://mlsanity.com/paper/2403.07309/) (2024)
- [Sim2Real Diffusion: Leveraging Foundation Vision Language Models for Adaptive Automated Driving](https://mlsanity.com/paper/2507.00236/) (2025)
- [Behavioral Cloning via Search in Embedded Demonstration Dataset](https://mlsanity.com/paper/2306.09082/) (2023)
- [Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture](https://mlsanity.com/paper/2110.05437/) (2021)
- [AGPNet -- Autonomous Grading Policy Network](https://mlsanity.com/paper/2112.10877/) (2021)
- [Imitation Learning for Autonomous Driving: Insights from Real-World Testing](https://mlsanity.com/paper/2504.18847/) (2025)
- [SD2AIL: Adversarial Imitation Learning from Synthetic Demonstrations via Diffusion Models](https://mlsanity.com/paper/2512.18583/) (2025)

## Abstract (arXiv)

In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning. The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle. The training phase comprised of data collection, balancing, augmentation, preprocessing and training a neural network, following which, the trained model was deployed onto the ego vehicle to predict steering commands based on the feed from an onboard camera. A novel coupled control law was formulated to generate longitudinal control commands on-the-go based on the predicted steering angle and other parameters such as actual speed of the ego vehicle and the prescribed constraints for speed and steering. We analyzed computational efficiency of the pipeline and evaluated robustness of the trained models through exhaustive experimentation during the deployment phase. We also compared our approach against state-of-the-art implementation in order to comment on its validity.