The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.
Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing
Open accessAbstract
The rising popularity of self-driving cars has led to the emergence of a new research field in recent years: Autonomous racing. Researchers are developing software and hardware for high-performance race vehicles which aim to operate autonomously on the edge of the vehicle’s limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic, and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods, and approaches used in the areas of perception, planning, control, and end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to high-performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms, emphasizing the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field, we conclude with a summary of open research challenges that will guide future researchers in this field.
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.
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
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718
A survey of iterative learning control
(2006) · IEEE Control Systems · doi:10.1109/mcs.2006.1636313 · Cited by 2,977
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Real-time loop closure in 2D LIDAR SLAM
Hess, W., Kohler, D., Rapp, H., & Andor, D. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487258 · Cited by 1,938
Perception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657
Optimization‐based autonomous racing of 1:43 scale RC cars
Liniger, A., Domahidi, A., & Morari, M. (2015) · Optimal Control Applications and Methods · doi:10.1002/oca.2123 · Cited by 537
Self-citationModel Predictive Control for Vehicle Stabilization at the Limits of Handling
Beal, C. E., & Gerdes, J. C. (2013) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2012.2200826 · Cited by 465
Learning-Based Model Predictive Control for Autonomous Racing
Kabzan, J., Hewing, L., Liniger, A., & Zeilinger, M. N. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2926677 · Cited by 462
Self-citationInformation theoretic MPC for model-based reinforcement learning
Williams, G., Wagener, N., Goldfain, B., Drews, P., Rehg, J. M., Boots, B., & Theodorou, E. A. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989202 · Cited by 460
Aggressive driving with model predictive path integral control
Williams, G., Drews, P., Goldfain, B., Rehg, J. M., & Theodorou, E. A. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487277 · Cited by 444
Cited by
Model- and Acceleration-based Pursuit Controller for High-Performance Autonomous Racing
Becker, J., Imholz, N., Schwarzenbach, L., Ghignone, E., Baumann, N., & Magno, M. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10161472 · Cited by 31
Comparing deep reinforcement learning architectures for autonomous racing
Evans, B. D., Jordaan, H. W., & Engelbrecht, H. A. (2023) · Machine Learning with Applications · doi:10.1016/j.mlwa.2023.100496 · Cited by 13
Residual Policy Learning for Vehicle Control of Autonomous Racing Cars
Trumpp, R., Hoornaert, D., & Caccamo, M. (2023) · 2023 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/iv55152.2023.10186744 · Cited by 11
TC-driver: A trajectory conditioned reinforcement learning approach to zero-shot autonomous racing
Ghignone, E., Baumann, N., & Magno, M. (2024) · IEEE Transactions on Field Robotics · doi:10.1109/tfr.2024.3499912 · Cited by 10
Trumpp, R., Javanmardi, E., Nakazato, J., Tsukada, M., & Caccamo, M. (2024) · 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801657 · Cited by 6
Metadata
- Authors
- Betz, J., Zheng, H., Liniger, A., Rosolia, U., Karle, P., Behl, M., Krovi, V., & Mangharam, R.
- Venue
- IEEE Open Journal of Intelligent Transportation Systems
- Published
- 2022
- Volume
- 3
- Pages
- 458-488
- ISSN
- 2687-7813 (ISSN-L)
- OpenAlex
- W4221141864
- Cited by
- 322
- References
- 116
- FWCI
- 19.61
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing. https://app.cochat.ai/papers/10.1109/ojits.2022.3181510
- The paper
- Betz, J., Zheng, H., Liniger, A., Rosolia, U., Karle, P., Behl, M., Krovi, V., & Mangharam, R. (2022). Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing. IEEE Open Journal of Intelligent Transportation Systems. https://doi.org/10.1109/ojits.2022.3181510
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
