mlsanityData to 7 Oct 2026

Memory based neural networks for end-to-end autonomous driving

arXiv 2205.12124 ↗First posted 27 Apr 2022cs.ROcited by 0

On the map: End-to-end autonomous driving → End-to-end system architecture

Abstract (arXiv)

Recent works in end-to-end control for autonomous driving have investigated the use of vision-based exteroceptive perception. Inspired by such results, we propose a new end-to-end memory-based neural architecture for robot steering and throttle control. We describe and compare this architecture with previous approaches using fundamental error metrics (MAE, MSE) and several external metrics based on their performance on simulated test circuits. The presented work demonstrates the advantages of using internal memory for better generalization capabilities of the model and allowing it to drive in a broader amount of circuits/situations. We analyze the algorithm in a wide range of environments and conclude that the proposed pipeline is robust to varying camera configurations. All the present work, including datasets, network models architectures, weights, simulator, and comparison software, is open source and easy to replicate and extend.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 12 of its 21 references that are in the map. First posted on arXiv 27 Apr 2022.

Builds on

12 of 21 references.

References · 12

  1. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  2. 2016End to End Learning for Self-Driving Carscited by 1,000
  3. 2020Virtual KITTI 2cited by 345
  4. 2016End-to-end Learning of Driving Models from Large-scale Video Datasetscited by 147
  5. 2017Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Carcited by 65
  6. 2020Computing Systems for Autonomous Driving: State-of-the-Art and Challengescited by 47
  7. 2016Learning a Driving Simulatorcited by 33
  8. 2017End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependenciescited by 16
  9. 2019DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learningcited by 13
  10. 2017Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cuescited by 9
  11. 2017Feature Analysis and Selection for Training an End-to-End Autonomous Vehicle Controller Using the Deep Learning Approachcited by 4
  12. 2021Bayesian Optimization and Deep Learning forsteering wheel angle predictioncited by 2

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  7. 2023End-to-end Autonomous Driving using Deep Learning: A Systematic Review
  8. 2026Planning-Oriented End-to-End Autonomous Driving: Architectures, Evaluation, and Emerging Paradigms