# Agile Autonomous Driving using End-to-End Deep Imitation Learning

arXiv: https://arxiv.org/abs/1709.07174 · HTML: https://mlsanity.com/paper/1709.07174/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2017.

## Built on by (most cited first, 10 of 10)

- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning](https://mlsanity.com/paper/1805.11240/) (2018)
- [Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models](https://mlsanity.com/paper/2105.12724/) (2021)
- [Convergence of Value Aggregation for Imitation Learning](https://mlsanity.com/paper/1801.07292/) (2018)
- [Accelerating Imitation Learning with Predictive Models](https://mlsanity.com/paper/1806.04642/) (2018)
- [Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective](https://mlsanity.com/paper/2312.00054/) (2023)
- [Affordance-based Reinforcement Learning for Urban Driving](https://mlsanity.com/paper/2101.05970/) (2021)
- [WROOM: An Autonomous Driving Approach for Off-Road Navigation](https://mlsanity.com/paper/2404.08855/) (2024)
- [Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation](https://mlsanity.com/paper/1909.09721/) (2019)
- [Bridging the Gap: Regularized Reinforcement Learning for Improved Classical Motion Planning with Safety Modules](https://mlsanity.com/paper/2403.18524/) (2024)

## Abstract (arXiv)

We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost sensors. By imitating a model predictive controller equipped with advanced sensors, we train a deep neural network control policy to map raw, high-dimensional observations to continuous steering and throttle commands. Compared with recent approaches to similar tasks, our method requires neither state estimation nor on-the-fly planning to navigate the vehicle. Our approach relies on, and experimentally validates, recent imitation learning theory. Empirically, we show that policies trained with online imitation learning overcome well-known challenges related to covariate shift and generalize better than policies trained with batch imitation learning. Built on these insights, our autonomous driving system demonstrates successful high-speed off-road driving, matching the state-of-the-art performance.