A Survey of Deep Learning Applications to Autonomous Vehicle Control

By Sampo Kuutti, Richard Bowden, Yaochu Jin, Phil Barber, Saber Fallah. 2019

696 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Building and Construction, Computer Vision and Pattern Recognition

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Citations in the corpus

VEGA: Towards an End-to-End Configurable AutoML Pipeline (2020, background): “…intelligence in various tasks, such as computer vision (He et al., 2016; Ren et al., 2015), natural language processing (NLP) (Yin et al., 2017; Devlin et al., 2018), autonomous driving (Al-Qizwini et al., 2017; Kuutti et al., 2020) and recommender systems (Guo et al., 2017; Liu et al., 2020a).”
Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “tic control is large domain, and we advise readers to read the survey on this subject by authors in [17].”
Task-Free Continual Learning via Online Discrepancy Distance Learning (2022, neutral): “Such an ability can be used in many real-time applications such as robotics, health investigative systems, autonomous vehicles [20] or for agents exploring artificial (meta) universes.”
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, neutral): “prediction [73], [74], planning [75], [76], [77], [78], [79], [80], E2E [81], [82], control [83], [84], [85], [86], [87], [88], [89], system [90], [91], [92], [93], hardware [94], [95], [96], soft-”
A Survey on Multimodal Large Language Models for Autonomous Driving (2023, background): “The introduction of Deep Neural Networks (DNNs) has also played a significant role [48, 85].”
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review (2021, background): “Reference [202] highlighted that it is critical to further research the available safety validation methods and the interpretability of neural networks before deploying DL models on the road.”
Challenges in Deploying Machine Learning: A Survey of Case Studies (2020, background): “That is the case for models for autonomous vehicles control [35].”