mlsanityData to 14 Oct 2026

A Survey of Deep Learning Techniques for Autonomous Driving

arXiv 1910.07738 ↗First posted 17 Oct 2019cs.LG cs.ROcited by 403

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Cited by 43 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorithms. We investigate both the modular perception-planning-action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources and computational hardware. The comparison presented in this survey helps to gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices

Key facts. Cited by 403 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Oct 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 65; 2022: 66; 2023: 99; 2024: 78; 2025: 72; 2026: 23.

403 papers.

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  1. 2021Learning to Prompt for Continual Learningcited by 944
  2. 2023Vision-Language Models for Vision Tasks: A Surveycited by 839
  3. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 290
  4. 2023Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Drivingcited by 232
  5. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 224
  6. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
  7. 2023A Comprehensive Survey of Dataset Distillationcited by 115
  8. 2021Theory of overparametrization in quantum neural networkscited by 100
  9. 2023Deep Model Fusion: A Surveycited by 91
  10. 2022Partially Observable Markov Decision Processes in Robotics: A Surveycited by 86
  11. 2021Video Summarization Using Deep Neural Networks: A Surveycited by 85
  12. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 85
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  1. 2021RobustART: Benchmarking Robustness on Architecture Design and Training Techniquescited by 79
  2. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  3. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  4. 2022Calibrated Learning to Defer with One-vs-All Classifierscited by 69
  5. 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 66
  6. 2022Training Uncertainty-Aware Classifiers with Conformalized Deep Learningcited by 64
  7. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 61
  8. 2025A Survey of World Models for Autonomous Drivingcited by 60
  9. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  10. 2022A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibrationcited by 49
  11. 2022Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detectioncited by 46
  12. 2023LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programscited by 45
  13. 2021Offline Reinforcement Learning as Anti-Explorationcited by 44
  14. 2022ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotationscited by 44
  15. 2024DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Drivingcited by 43
  16. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  17. 2021Zero Time Waste: Recycling Predictions in Early Exit Neural Networkscited by 39
  18. 2023Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Drivingcited by 37
  19. 2021SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognitioncited by 36
  20. 2021Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overviewcited by 31
  21. 2023Fully Autonomous Programming with Large Language Modelscited by 31
  22. 2022Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Imagecited by 30
  23. 2022VeriX: Towards Verified Explainability of Deep Neural Networkscited by 30
  24. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  25. 2022The Many Faces of Adversarial Riskcited by 28
  26. 2024From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimationcited by 28
  27. 2021Reachability Is NP-Complete Even for the Simplest Neural Networkscited by 27
  28. 2021Machine learning and deep learningcited by 26
  29. 2022Towards Improving Calibration in Object Detection Under Domain Shiftcited by 26
  30. 2023In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defercited by 26
  31. 2023Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Drivingcited by 26
  32. 2024Towards Robust Physical-world Backdoor Attacks on Lane Detectioncited by 26
  33. 2021Towards Robust Neural Networks via Close-loop Controlcited by 23
  34. 2022Towards Training Reproducible Deep Learning Modelscited by 23
  35. 2022GAMa: Cross-view Video Geo-localizationcited by 23
  36. 2023Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanationscited by 22
  37. 2025Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modelingcited by 22
  38. 2021CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selectioncited by 21

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