mlsanityData to 14 Oct 2026

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

arXiv 1812.05841 ↗First posted 14 Dec 2018cs.CV cs.ROcited by 5

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

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.

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.

Builds on

7 of 14 references.

References · 7

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  2. 2016End to End Learning for Self-Driving Carscited by 1,009
  3. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 231
  4. 2016End-to-end Learning of Driving Models from Large-scale Video Datasetscited by 148
  5. 2018End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perceptioncited by 27
  6. 2018End to End Vehicle Lateral Control Using a Single Fisheye Cameracited by 13
  7. 2016VisualBackProp: efficient visualization of CNNscited by 10

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 1.

5 papers.

  1. 2021Imitation Learning: Progress, Taxonomies and Challengescited by 58
  2. 2021Adversarial Imitation Learning with Trajectorial Augmentation and Correctioncited by 11
  3. 2021Raising context awareness in motion forecastingcited by 6
  4. 2022Data augmentation for efficient learning from parametric expertscited by 5
  5. 2024Guiding Attention in End-to-End Driving Modelscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Imitation Learning for Autonomous Driving: Insights from Real-World Testing
  2. 2022Model-Based Imitation Learning for Urban Driving
  3. 2025Learning to Drive by Imitating Surrounding Vehicles
  4. 2019Urban Driving with Conditional Imitation Learning
  5. 2019Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation
  6. 2021Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
  7. 2021Semi-Supervised Imitation Learning with Mixed Qualities of Demonstrations for Autonomous Driving
  8. 2023Long-term Microscopic Traffic Simulation with History-Masked Multi-agent Imitation Learning