The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Intention-aware online POMDP planning for autonomous driving in a crowd
Abstract
This paper presents an intention-aware online planning approach for autonomous driving amid many pedestrians. To drive near pedestrians safely, efficiently, and smoothly, autonomous vehicles must estimate unknown pedestrian intentions and hedge against the uncertainty in intention estimates in order to choose actions that are effective and robust. A key feature of our approach is to use the partially observable Markov decision process (POMDP) for systematic, robust decision making under uncertainty. Although there are concerns about the potentially high computational complexity of POMDP planning, experiments show that our POMDP-based planner runs in near real time, at 3 Hz, on a robot golf cart in a complex, dynamic environment. This indicates that POMDP planning is improving fast in computational efficiency and becoming increasingly practical as a tool for robot planning under uncertainty.
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
Thrun, S. (2002) · Communications of the ACM · doi:10.1145/504729.504754 · Cited by 7,994
Latombe, J.-C. (1991) · Springer eBooks · doi:10.1007/978-1-4615-4022-9 · Cited by 6,396
The dynamic window approach to collision avoidance
Fox, D., Burgard, W., & Thrun, S. (1997) · IEEE Robotics & Automation Magazine · doi:10.1109/100.580977 · Cited by 3,771
Stanley: The robot that won the DARPA Grand Challenge
Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., et al. (2006) · Journal of Field Robotics · doi:10.1002/rob.20147 · Cited by 2,237
Autonomous driving in urban environments: Boss and the Urban Challenge
Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M. N., Dolan, J., Duggins, D., Galatali, T., Geyer, C., et al. (2008) · Journal of Field Robotics · doi:10.1002/rob.20255 · Cited by 1,555
The Optimal Control of Partially Observable Markov Processes over a Finite Horizon
Smallwood, R. D., & Sondik, E. J. (1973) · Operations Research · doi:10.1287/opre.21.5.1071 · Cited by 1,479
Junior: The Stanford entry in the Urban Challenge
Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Haehnel, D., Hilden, T., Hoffmann, G., Huhnke, B., et al. (2008) · Journal of Field Robotics · doi:10.1002/rob.20258 · Cited by 1,027
Howard, R. (1966) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1966.300074 · Cited by 995
Real-Time Motion Planning With Applications to Autonomous Urban Driving
Kuwata, Y., Karaman, S., Teo, J., Frazzoli, E., How, J. P., & Fiore, G. (2009) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2008.2012116 · Cited by 889
Monte-Carlo Planning in Large POMDPs
Silver, D., & Veness, J. (2010) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 886
Cited by
Mohamed, A., Qian, K., Elhoseiny, M., & Claudel, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01443 · Cited by 989
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice
Rasouli, A., & Tsotsos, J. K. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2901817 · Cited by 855
Socially aware motion planning with deep reinforcement learning
Chen, Y. F., Everett, M., Liu, M., & How, J. P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202312 · Cited by 719
Human motion trajectory prediction: a survey
Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920917446 · Cited by 658
Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach
Hang, P., Lv, C., Xing, Y., Huang, C., & Hu, Z. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3036984 · Cited by 357
Metadata
- Authors
- Bai, H., Cai, S., Ye, N., Hsu, D., & Lee, W. S.
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 454-460
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1605929701
- Cited by
- 321
- References
- 22
- FWCI
- 25.42
- Type
- conference paper
- Language
- en
- Open access
- closed
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: Intention-aware online POMDP planning for autonomous driving in a crowd. https://app.cochat.ai/papers/10.1109/icra.2015.7139219
- The paper
- Bai, H., Cai, S., Ye, N., Hsu, D., & Lee, W. S. (2015). Intention-aware online POMDP planning for autonomous driving in a crowd. 2015 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2015.7139219
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.
