The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
An Open Approach to Autonomous Vehicles
Abstract
Autonomous vehicles are an emerging application of automotive technology. They can recognize the scene, plan the path, and control the motion by themselves while interacting with drivers. Although they receive considerable attention, components of autonomous vehicles are not accessible to the public but instead are developed as proprietary assets. To facilitate the development of autonomous vehicles, this article introduces an open platform using commodity vehicles and sensors. Specifically, the authors present algorithms, software libraries, and datasets required for scene recognition, path planning, and vehicle control. This open platform allows researchers and developers to study the basis of autonomous vehicles, design new algorithms, and test their performance using the common interface.
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Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Hart, P., Nilsson, N., & Raphael, B. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300136 · Cited by 12,738
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
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
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
“Autoware [110], Apollo [290], Nvidia DriveWorks [291] and openpilot [292] are amongst the most used software-stacks capable of running an ADS platform in real world.”
MethodInfluentialSelf-citationAutonomous vehicle perception: The technology of today and tomorrow
Van Brummelen, J., O’Brien, M., Gruyer, D., & Najjaran, H. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.02.012 · Cited by 869
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
Perception Task Offloading With Collaborative Computation for Autonomous Driving
Xiao, Z., Shu, J., Jiang, H., Min, G., Chen, H., & Han, Z. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3227027 · Cited by 394
A Survey on Multimodal Large Language Models for Autonomous Driving
Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., Liao, K.-D., et al. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00106 · Cited by 369
Metadata
- Authors
- Kato, S., Takeuchi, E., Ishiguro, Y., Ninomiya, Y., Takeda, K., & Hamada, T.
- Venue
- IEEE Micro
- Published
- 2015-11
- Volume
- 35
- Issue
- 6
- Pages
- 60-68
- ISSN
- 0272-1732 (ISSN-L)
- OpenAlex
- W2206222117
- Cited by
- 564
- References
- 5
- FWCI
- 24.93
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kato, S., Takeuchi, E., Ishiguro, Y., Ninomiya, Y., Takeda, K., & Hamada, T. (2015). An Open Approach to Autonomous Vehicles. IEEE Micro. https://doi.org/10.1109/mm.2015.133
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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