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A Survey of Deep Learning Applications to Autonomous Vehicle Control
Open accessAbstract
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.
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“This includes historical signals and inappropriately proxied measures that make ML systems vulnerable to reproducing past discrimination against under-represented groups (e.g. in contexts such as job hiring and criminal justice), or propagating abusive content [46,67].”
From A Survey of Data Quality Requirements That Matter in ML Development Pipelines (2023) · cites this paper as [46]
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“This includes historical signals and inappropriately proxied measures that make ML systems vulnerable to reproducing past discrimination against under-represented groups (e.g. in contexts such as job hiring and criminal justice), or propagating abusive content [46,67].”
From A Survey of Data Quality Requirements That Matter in ML Development Pipelines (2023) · cites this paper as [46]
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Metadata
- Authors
- Kuutti, S., Bowden, R., Jin, Y., Barber, P., & Fallah, S.
- Affiliations
- University of Surrey (GB)
- Jaguar Land Rover (United Kingdom) (GB)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2021-02
- Volume
- 22
- Issue
- 2
- Pages
- 712-733
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2995697759
- Cited by
- 96
- References
- 230
- Citations by year
- 2026: 12 · 2025: 7 · 2024: 16 · 2023: 21 · 2022: 14 · 2021: 21
- FWCI
- 5.64
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.K.-Engineering and Physical Sciences Research Council (EP/R512217/1)
- Jaguar Land Rover
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- autonomous vehicles, vehicle control, deep learning control, verification and validation, safety, goal specification, intelligent transportation systems
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