# A Survey of Deep Learning Applications to Autonomous Vehicle Control

arXiv: https://arxiv.org/abs/1912.10773 · HTML: https://mlsanity.com/paper/1912.10773/

## Key facts

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

## Built on by (most cited first, 30 of 106)

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- [Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence](https://mlsanity.com/paper/2306.02572/) (2023)
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- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Towards Robust Physical-world Backdoor Attacks on Lane Detection](https://mlsanity.com/paper/2405.05553/) (2024)
- [Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving](https://mlsanity.com/paper/2105.12713/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors](https://mlsanity.com/paper/2305.11239/) (2023)
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- [Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification](https://mlsanity.com/paper/2207.06104/) (2022)
- [Identifying Label Errors in Object Detection Datasets by Loss Inspection](https://mlsanity.com/paper/2303.06999/) (2023)
- [SIGMA: Sinkhorn-Guided Masked Video Modeling](https://mlsanity.com/paper/2407.15447/) (2024)
- [DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks](https://mlsanity.com/paper/2108.05030/) (2021)
- [Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning](https://mlsanity.com/paper/2109.08473/) (2021)
- [Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application](https://mlsanity.com/paper/2301.04829/) (2023)
- [Stochastic Coded Federated Learning with Convergence and Privacy Guarantees](https://mlsanity.com/paper/2201.10092/) (2022)
- [Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectory](https://mlsanity.com/paper/2010.10393/) (2020)
- [fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems](https://mlsanity.com/paper/2205.13807/) (2022)
- [Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles](https://mlsanity.com/paper/2206.08809/) (2022)
- [Reachability Analysis of Neural Network Control Systems](https://mlsanity.com/paper/2301.12100/) (2023)
- [Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey](https://mlsanity.com/paper/2304.10891/) (2023)
- [Squeezed Edge YOLO: Onboard Object Detection on Edge Devices](https://mlsanity.com/paper/2312.11716/) (2023)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control](https://mlsanity.com/paper/2103.03796/) (2021)

## 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.