The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Dense reinforcement learning for safety validation of autonomous vehicles
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1×
“The connections can even be added in the form of curriculum learning 46 , which can guide the information propagation gradually.”
BackgroundCongested traffic states in empirical observations and microscopic simulations
Treiber, M., Hennecke, A., & Helbing, D. (2000) · Physical Review E · doi:10.1103/physreve.62.1805 · Cited by 4,762
Microscopic Traffic Simulation using SUMO
Lopez, P. A., Wiessner, E., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flotterod, Y.-P., Hilbrich, R., Lucken, L., Rummel, J., & Wagner, P. (2018) · 2018 21st International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569938 · Cited by 3,297
ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T. A., Guibas, L. J., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2023) · doi:10.5281/zenodo.10089705 · Cited by 1,623
Research works printed but not linked: 9
- Kalra, N. & Paddock, S. M. Driving to safety: how many miles of driving would it take to demonstrate autonomous vehicle reliability? Transp. Res. A 94, 182–193 (2016).
- Paz, D., Lai, P. J., Chan, N., Jiang, Y. & Christensen, H. I. Autonomous vehicle benchmarking using unbiased metrics. In IEEE International Conference on Intelligent Robots and Systems 6223–6228 (IEEE, 2020). · doi:10.1109/iros45743.2020.9340902
- Nalic, D. et al. Scenario based testing of automated driving systems: a literature survey. In Proc. of the FISITA Web Congress 1–10 (Fisita, 2020). · doi:10.46720/f2020-acm-096
- Sinha, A., O’Kelly, M., Tedrake, R. & Duchi, J. C. Neural bridge sampling for evaluating safety-critical autonomous systems. Adv. Neural Inf. Process. Syst. 33, 6402–6416 (2020).
- Donoho, D. L. High-dimensional data analysis: the curses and blessings of dimensionality. AMS Math Challenges Lecture 1, 32 (2000).
- Cummings, M. L. Rethinking the maturity of artificial intelligence in safety-critical settings. AI Mag. 42, 6–15 (2021).
- Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, 2018).
- Sauerbier, J., Bock, J., Weber, H. & Eckstein, L. Definition of scenarios for safety validation of automated driving functions. ATZ Worldwide 121, 42–45 (2019). · doi:10.1007/s38311-018-0197-2
- Yan, X., Feng, S., Sun, H., & Liu, H. X. Distributionally consistent simulation of naturalistic driving environment for autonomous vehicle testing. Preprint at https://arxiv.org/abs/2101.02828 (2021). · arXiv
Cited by
Autonomous driving system: A comprehensive survey
Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., & Burke, A. F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122836 · Cited by 361
Huang, Z., Sheng, Z., Ma, C., & Chen, S. (2024) · Communications in Transportation Research · doi:10.1016/j.commtr.2024.100127 · Cited by 88
Understanding World or Predicting Future? A Comprehensive Survey of World Models
Ding, J., Zhang, Y., Shang, Y., Zhang, Y., Zong, Z., Feng, J., Yuan, Y., Su, H., Li, N., Sukiennik, N., et al. (2026) · ACM Computing Surveys · doi:10.1145/3746449 · Cited by 169
“This capacity is crucial for handling rare, mission-critical events (e.g., autonomous-driving corner cases [62]) and for narrowing sim-to-real gaps.”
Lin, H., Zhu, C., Shang, T., Zhu, N., Lin, K., Zhang, C., Shao, X., Wang, X., & Duan, H. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c00896 · Cited by 10
Xu, Z., Zheng, N., Fang, Y., & Vu, H. L. (2025) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2025.105367 · Cited by 6
Metadata
- Authors
- Feng, S., Sun, H., Yan, X., Zhu, H., Zou, Z., Shen, S., & Liu, H. X.
- Venue
- Nature
- Published
- 2023-03-22
- Volume
- 615
- Issue
- 7953
- Pages
- 620-627
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4353056919
- Cited by
- 587
- References
- 38
- FWCI
- 42.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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: Dense reinforcement learning for safety validation of autonomous vehicles. https://app.cochat.ai/papers/10.1038/s41586-023-05732-2
- The paper
- Feng, S., Sun, H., Yan, X., Zhu, H., Zou, Z., Shen, S., & Liu, H. X. (2023). Dense reinforcement learning for safety validation of autonomous vehicles. Nature. https://doi.org/10.1038/s41586-023-05732-2
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.
