The DOI matches its Crossref record; 218 of the 230 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Items 201–218 of 218
THEORY AND APPLICATION OF REWARD SHAPING IN REINFORCEMENT LEARNING
Laud, A. D. (2004) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 30
CAAD: Computer Architecture for Autonomous DrivingPreprint
Liu, S., Tang, J., Zhang, Z., & Gaudiot, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.01894 · Cited by 24
Parallel Architecture and Hyperparameter Search via Successive Halving and ClassificationPreprint
Kumar, M. N. A., Dahl, G. E., Vasudevan, V., & Norouzi, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10255 · Cited by 24 · cited 1×
“Other proposed approaches focus on automated hyperparameter tuning by eliminating undesirable regions of the hyperparameter search space in order to converge to optimal values [168], [169].”
BackgroundLearning decisions: robustness, uncertainty, and approximation
Bagnell, J. A., & Schneider, J. (2004) · Cited by 23
Imminent Collision Mitigation with Reinforcement Learning and Vision
Porav, H., & Newman, P. M. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569222 · Cited by 22
Wang, B., Zhao, D., Li, C., & Dai, Y. (2015) · International Conference on Information Science and Technology · doi:10.1109/icist.2015.7288976 · Cited by 19
Road-following with continuous learning
Yu, G., & Sethi, I. K. (2002) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.1995.528317 · Cited by 19 · cited 1×
“The first to suggest reinforcement learning for vehicle steering was the work carried out by Yu [85].”
BackgroundReinforcement learning of adaptive longitudinal vehicle control for dynamic collaborative driving
Ng, L., Clark, C. M., & Huissoon, J. P. (2008) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2008.4621222 · Cited by 19 · cited 1×
“Indeed, with the increasing accuracy and speed of simulation tools, simulation has become an increasingly dominant method of study in this field [196].”
BackgroundController verification in adaptive learning systems towards trusted autonomy
Zhang, X., Clark, M., Rattan, K., & Muse, J. A. (2015) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1145/2735960.2735971 · Cited by 18 · cited 1×
“It becomes even more challenging in online learning methods, since they change their policies during operation and therefore could potentially shift from safe policies to unsafe policies over time [205]–[209].”
BackgroundConstrained deep neural network architecture search for IoT devices accounting hardware calibrationPreprint
Scheidegger, F., Benini, L., Bekas, C., & Malossi, A. C. I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.10818 · Cited by 11 · cited 1×
“ware feedback into the learning signal [170]–[173].”
Learning Distributed Strategies for Traffic Control
Moriarty, D. E., Handley, S. J., & Langley, P. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/3119.003.0066 · Cited by 10 · cited 1×
“[86] developed a lane-selection strategy for a highway environment.”
BackgroundDerivative free optimization via repeated classificationPreprint
Hashimoto, T., Yadlowsky, S., & Duchi, J. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.03761 · Cited by 9 · cited 1×
“Other proposed approaches focus on automated hyperparameter tuning by eliminating undesirable regions of the hyperparameter search space in order to converge to optimal values [168], [169].”
BackgroundKendrick, A. S., Pradeep, Y. C., Chen, P. C. Y., Chen, W., & Zhao, Z. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2901757 · Cited by 9 · cited 1×
“Other avenues for future research include learning driving manoeuvrers which are still typically achieved through classical control techniques, such as overtaking [227], [228] or merging [229], [230].”
BackgroundFinal Report - Regulatory Considerations for Adaptive Systems.
Wilkinson, C., Lynch, J., & Bharadwaj, R. M. (2013) · NASA Technical Reports Server (NASA) · Cited by 6
Deep Object-Centric Policies for Autonomous DrivingPreprint
Wang, D., Devin, C., Cai, Q.-Z., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.05432 · Cited by 6 · cited 1×
“[128] demonstrated that DAgger can be used to train an object-centric policy, which uses salient objects in the image (e.”
BackgroundCooperative Adaptive Cruise Control Performance Analysis
Sun, Q. (2016) · doi:10.70675/2e5391fczf13dz44dbz8901z6a28652e8ec3 · Cited by 5 · cited 2×
“Similarly, Sun [99] proposed a CACC system based on rewards from time headway and time headway derivative in a Q-learning algorithm.”
BackgroundMethodPattern Recognition in Neural Networks
Bajaj, K. (2013) · Cited by 1
Learning Decisions: Robustness, Uncertainty, and Appoximation
Bagnell, J. A. (2018) · Figshare · doi:10.1184/r1/6555335 · Cited by 1
Research works printed but not linked: 26
- ripley, 1996, Artificial Neural Networks
- schalkoff, 1997, Artificial Neural Networks
- ng, 2004, Feature selection, L1 vs. L2 regularization, and rotational invariance, Proc 21st Int Conf Mach Learn (ICML), 78
- 10.1126/science.153.3731.34 · DOI · Paper page
- barron, 2009, Parallelizing Reinforcement Learning
- abraham, 2017, Consumer Interest in Automation Preliminary Observations Exploring a Year’s Change
- knight, 2016, An Ambitious Plan to Build a Self-Driving Borg
- 2016, Cooperative Intelligent Transportation Systems—Research Theme Analysis Report
- 2017, Consumer Acceptance of Autonomous Vehicles
- bosankic, 2017, How consumers’ Perception of Autonomous Cars Will Influence Their Adoption
- sevchuk, 2016, Hyperparameter Optimization for Neural Networks
- raffel, 2015, Neural Network Hyperparameters
- 2018, Global status report on road safety 2018
- 2019, Waymo open dataset An autonomous driving dataset
- 2018, Udacity Self-Driving Car Dataset
- 2018, Autonomous car development platform from nvidia drive px2
- williams, 1987, Reinforcement learning connectionist systems
- 2018, Cyclone V—Overview
- 2018, Evolution of the Eye
- 0, CarSim
- nguyen, 2015, Deep neural networks are easily fooled, Proc IEEE Conf Comput Vis Pattern Recognit (CVPR), 427
- 2017, 2016 road safety statistics What is behind the figures
- 2017, 2016 Fatal Motor Vehicle Crashes Overview
- maurer, 2016, Autonomous Driving
- 2017, Research on the Impacts of Connected and Autonomous Vehicles (CAVs) on Traffic Flow Summary Report
- 2019, The Open Racing Car Simulator
Cited by
Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2021.100452 · Cited by 291
“…autonomous cars where one of the key responsibilities of the AI module is continuous monitoring of the surrounding environment and the prediction of di ff erent events, which generally involves the detection and recognition of various objects such as pedestrians, vehicles, and roadside objects [38].”
BackgroundA Survey of Deep RL and IL for Autonomous Driving Policy Learning
Zhu, Z., & Zhao, H. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3134702 · Cited by 260
“References [4] addresses the deep learning techniques for AD with a focus on perception and control, while [32] addresses control only.”
BackgroundMachine learning for next-generation intelligent transportation systems: A survey
Yuan, T., Da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4427 · Cited by 179
“…works include [6-16], which focus on specific aspects of ITS, such as vehicular networks [7], vehicle detection [8-10], safety of vehicular ad-hoc network (VANET) [6, 15], VANET performance optimization [16], autonomous vehicle control [13], and Deep Reinforcement Learning (DRL) for 6G VANETs [14].”
Elallid, B. B., Benamar, N., Hafid, A. S., Rachidi, T., & Mrani, N. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.03.013 · Cited by 179
“Kuutti et al. (2020) discussed DL methods for controlling AVs and its promising performance in complex scenarios.”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
