The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Cooperative Collision Avoidance at Intersections: Algorithms and Experiments
Open accessAbstract
In this paper, we leverage vehicle-to-vehicle (V2V) communication technology to implement computationally efficient decentralized algorithms for two-vehicle cooperative collision avoidance at intersections. Our algorithms employ formal control theoretic methods to guarantee a collision-free (safe) system, whereas overrides are only applied when necessary to prevent a crash. Model uncertainty and communication delays are explicitly accounted for by the model and by the state estimation algorithm. The main contribution of this work is to provide an experimental validation of our method on two instrumented vehicles engaged in an intersection collision avoidance scenario in a test track.
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
Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches
Simon, D. (2006) · doi:10.1002/0470045345 · Cited by 4,875
Cited by
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
“Level two is partial automation to which advanced assistance systems such as emergency braking or collision avoidance [33], [34]”
BackgroundNavigating Occluded Intersections with Autonomous Vehicles Using Deep Reinforcement Learning
Isele, D., Rahimi, R., Cosgun, A., Subramanian, K., & Fujimura, K. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8461233 · Cited by 379
Explanations in Autonomous Driving: A Survey
Omeiza, D., Webb, H., Jirotka, M., & Kunze, L. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3122865 · Cited by 355
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
Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhang, Y. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3083927 · Cited by 169
Metadata
- Authors
- Hafner, M. R., Cunningham, D., Caminiti, L., & Del Vecchio, D.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2013-09
- Volume
- 14
- Issue
- 3
- Pages
- 1162-1175
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2010725166
- Cited by
- 301
- References
- 1
- FWCI
- 30.25
- Type
- journal article
- Language
- en
- Open access
- bronze
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: Cooperative Collision Avoidance at Intersections: Algorithms and Experiments. https://app.cochat.ai/papers/10.1109/tits.2013.2252901
- The paper
- Hafner, M. R., Cunningham, D., Caminiti, L., & Del Vecchio, D. (2013). Cooperative Collision Avoidance at Intersections: Algorithms and Experiments. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2013.2252901
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.
