mlsanityData to 7 Oct 2026

Towards End-to-End Deep Learning for Autonomous Racing: On Data Collection and a Unified Architecture for Steering and Throttle Prediction

arXiv 2105.01799 ↗First posted 4 May 2021cs.RO cs.AIcited by 7

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Abstract (arXiv)

Deep Neural Networks (DNNs) which are trained end-to-end have been successfully applied to solve complex problems that we have not been able to solve in past decades. Autonomous driving is one of the most complex problems which is yet to be completely solved and autonomous racing adds more complexity and exciting challenges to this problem. Towards the challenge of applying end-to-end learning to autonomous racing, this paper shows results on two aspects: (1) Analyzing the relationship between the driving data used for training and the maximum speed at which the DNN can be successfully applied for predicting steering angle, (2) Neural network architecture and training methodology for learning steering and throttle without any feedback or recurrent connections.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 12 of its 17 references that are in the map. First posted on arXiv 4 May 2021.

Builds on

12 of 17 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2; 2025: 1.

7 papers.

  1. 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 56
  2. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  3. 2024A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismocited by 12
  4. 2021Deep Visual Navigation under Partial Observabilitycited by 6
  5. 2024TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racingcited by 4
  6. 2025Saliency-Guided Domain Adaptation for Left-Hand Driving in Autonomous Steeringcited by 2
  7. 2023Enhancing Robot Learning through Learned Human-Attention Feature Mapscited by 0