mlsanityData to 14 Oct 2026

SuperDriverAI: Towards Design and Implementation for End-to-End Learning-based Autonomous Driving

arXiv 2305.10443 ↗First posted 14 May 2023cs.RO cs.AIcited by 1

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

Abstract (arXiv)

Fully autonomous driving has been widely studied and is becoming increasingly feasible. However, such autonomous driving has yet to be achieved on public roads, because of various uncertainties due to surrounding human drivers and pedestrians. In this paper, we present an end-to-end learningbased autonomous driving system named SuperDriver AI, where Deep Neural Networks (DNNs) learn the driving actions and policies from the experienced human drivers and determine the driving maneuvers to take while guaranteeing road safety. In addition, to improve robustness and interpretability, we present a slit model and a visual attention module. We build a datacollection system and emulator with real-world hardware, and we also test the SuperDriver AI system with real-world driving scenarios. Finally, we have collected 150 runs for one driving scenario in Tokyo, Japan, and have shown the demonstration of SuperDriver AI with the real-world vehicle.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 7 of its 13 references that are in the map. First posted on arXiv 14 May 2023.

Builds on

7 of 13 references.

References · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  3. 2016End to End Learning for Self-Driving Carscited by 1,009
  4. 2016End-to-end Learning of Driving Models from Large-scale Video Datasetscited by 148
  5. 2020A Survey of End-to-End Driving: Architectures and Training Methodscited by 123
  6. 2019Grounding Human-to-Vehicle Advice for Self-driving Vehiclescited by 71
  7. 2019Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safetycited by 49

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

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  1. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey
  2. 2023End-to-end Autonomous Driving using Deep Learning: A Systematic Review
  3. 2026A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform
  4. 2022Tackling Real-World Autonomous Driving using Deep Reinforcement Learning
  5. 2023Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
  6. 2023End-to-end Autonomous Driving: Challenges and Frontiers
  7. 2025Imitation Learning for Autonomous Driving: Insights from Real-World Testing
  8. 2026Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment