The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles
Open accessAbstract
The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to self-driving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in this paper to give autonomous vehicles the ability to make decisions like a human. In our method, a convolutional neural network model is used to detect, recognize, and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decision-making system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method.
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
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Selective Search for Object Recognition
Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980
3D is here: Point Cloud Library (PCL)
Rusu, R. B., & Cousins, S. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980567 · Cited by 4,885
OctoMap: an efficient probabilistic 3D mapping framework based on octrees
Hornung, A., Wurm, K. M., Bennewitz, M., Stachniss, C., & Burgard, W. (2013) · Autonomous Robots · doi:10.1007/s10514-012-9321-0 · Cited by 2,866
Object scene flow for autonomous vehicles
Menze, M., & Geiger, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298925 · Cited by 2,270
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.312 · Cited by 1,876
Ji, J., Khajepour, A., Melek, W. W., & Huang, Y. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2555853 · Cited by 1,039
Cited by
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030072 · Cited by 796
Artificial intelligence applications in the development of autonomous vehicles: a survey
Ma, Y., Wang, Z., Yang, H., & Yang, L. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003021 · Cited by 639
Dimiduk, D. M., Holm, E. A., & Niezgoda, S. R. (2018) · Integrating Materials and Manufacturing Innovation · doi:10.1007/s40192-018-0117-8 · Cited by 347
BSIS: Blockchain-Based Secure Incentive Scheme for Energy Delivery in Vehicular Energy Network
Wang, Y., Su, Z., & Zhang, N. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2908497 · Cited by 218
Machine 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
Metadata
- Authors
- Li, L., Ota, K., & Dong, M.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2018-08
- Volume
- 67
- Issue
- 8
- Pages
- 6814-6823
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2795543364
- Cited by
- 282
- References
- 29
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- green
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: Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles. https://app.cochat.ai/papers/10.1109/tvt.2018.2822762
- The paper
- Li, L., Ota, K., & Dong, M. (2018). Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2018.2822762
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.
