mlsanityData to 14 Oct 2026

A Survey of Deep Learning Applications to Autonomous Vehicle Control

arXiv 1912.10773 ↗First posted 23 Dec 2019cs.LG cs.CV cs.SYcited by 106

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Cited by 22 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.

Key facts. Cited by 106 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Dec 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 19; 2022: 17; 2023: 24; 2024: 19; 2025: 14; 2026: 12.

106 papers.

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  1. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 290
  2. 2022Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalizationcited by 97
  3. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  4. 2023Semantic Segmentation using Vision Transformers: A surveycited by 73
  5. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 61
  6. 2023Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligencecited by 56
  7. 2022LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERTcited by 50
  8. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  9. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  10. 2022Graph Neural Networks in IoT: A Surveycited by 28
  11. 2024Towards Robust Physical-world Backdoor Attacks on Lane Detectioncited by 26
  12. 2021Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Drivingcited by 25
38 more
  1. 2023Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviorscited by 25
  2. 2022Task-Free Continual Learning via Online Discrepancy Distance Learningcited by 22
  3. 2024An Information Theoretic Perspective on Conformal Predictioncited by 21
  4. 2022Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantificationcited by 20
  5. 2023Identifying Label Errors in Object Detection Datasets by Loss Inspectioncited by 16
  6. 2024SIGMA: Sinkhorn-Guided Masked Video Modelingcited by 14
  7. 2021DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networkscited by 13
  8. 2021Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learningcited by 11
  9. 2023Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Applicationcited by 11
  10. 2022Stochastic Coded Federated Learning with Convergence and Privacy Guaranteescited by 10
  11. 2020Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectorycited by 8
  12. 2022fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systemscited by 8
  13. 2022Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehiclescited by 8
  14. 2023Reachability Analysis of Neural Network Control Systemscited by 8
  15. 2023Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Surveycited by 8
  16. 2023Squeezed Edge YOLO: Onboard Object Detection on Edge Devicescited by 8
  17. 2021Machine Learning for Security in Vehicular Networks: A Comprehensive Surveycited by 7
  18. 2021Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Controlcited by 6
  19. 2023Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectoriescited by 6
  20. 2023Mind the map! Accounting for existing map information when estimating online HDMaps from sensorcited by 6
  21. 2024Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunitiescited by 6
  22. 2021The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networkscited by 5
  23. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Surveycited by 5
  24. 2021Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Datacited by 4
  25. 2022Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersectionscited by 4
  26. 2022Optimizing Trajectories for Highway Driving with Offline Reinforcement Learningcited by 4
  27. 2022Prediction Based Decision Making for Autonomous Highway Drivingcited by 4
  28. 2023Deep-Learning Framework for Optimal Selection of Soil Sampling Sitescited by 4
  29. 2023Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Controlcited by 4
  30. 2021ARC: Adversarially Robust Control Policies for Autonomous Vehiclescited by 3
  31. 2021Local Repair of Neural Networks Using Optimizationcited by 3
  32. 2021Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systemscited by 3
  33. 2022Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approachcited by 3
  34. 2023Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharingcited by 3
  35. 2024CrossCert: A Cross-Checking Detection Approach to Patch Robustness Certification for Deep Learning Modelscited by 3
  36. 2024Query-Efficient Video Adversarial Attack with Stylized Logo on Service Computingcited by 3
  37. 2024AI-Aided Kalman Filterscited by 3
  38. 2025Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunitiescited by 3

50 most cited of 106 All 106 (JSON)