# Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera

arXiv: https://arxiv.org/abs/1812.05841 · HTML: https://mlsanity.com/paper/1812.05841/

## Key facts

- Cited by 5 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 7 of its 14 references that are in the map.
- First posted on arXiv 14 Dec 2018.

## 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, 7 of 7)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception](https://mlsanity.com/paper/1801.06734/) (2018)
- [End to End Vehicle Lateral Control Using a Single Fisheye Camera](https://mlsanity.com/paper/1808.06940/) (2018)
- [VisualBackProp: efficient visualization of CNNs](https://mlsanity.com/paper/1611.05418/) (2016)

## Built on by (most cited first, 5 of 5)

- [Imitation Learning: Progress, Taxonomies and Challenges](https://mlsanity.com/paper/2106.12177/) (2021)
- [Adversarial Imitation Learning with Trajectorial Augmentation and Correction](https://mlsanity.com/paper/2103.13887/) (2021)
- [Raising context awareness in motion forecasting](https://mlsanity.com/paper/2109.08048/) (2021)
- [Data augmentation for efficient learning from parametric experts](https://mlsanity.com/paper/2205.11448/) (2022)
- [Guiding Attention in End-to-End Driving Models](https://mlsanity.com/paper/2405.00242/) (2024)

## Abstract (arXiv)

In this paper we present a complete study of an end-to-end imitation learning system for speed control of a real car, based on a neural network with a Long Short Term Memory (LSTM). To achieve robustness and generalization from expert demonstrations, we propose data augmentation and label augmentation that are relevant for imitation learning in longitudinal control context. Based on front camera image only, our system is able to correctly control the speed of a car in simulation environment, and in a real car on a challenging test track. The system also shows promising results in open road context.