The DOI matches its Crossref record; 161 of the 161 references it cites were found in a registry or an index.
A Survey on Trajectory-Prediction Methods for Autonomous Driving
Abstract
In order to drive safely in a dynamic environment, autonomous vehicles should be able to predict the future states of traffic participants nearby, especially surrounding vehicles, similar to the capability of predictive driving of human drivers. That is why researchers are devoted to the field of trajectory prediction and propose different methods. This paper is to provide a comprehensive and comparative review of trajectory-prediction methods proposed over the last two decades for autonomous driving. It starts with the problem formulation and algorithm classification. Then, the popular methods based on physics, classic machine learning, deep learning, and reinforcement learning are elaborately introduced and analyzed. Finally, this paper evaluates the performance of each kind of method and outlines potential research directions to guide readers.
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Cites
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Cited by
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
“Conversely, when humans operate vehicles, they typically observe and predict the current and future states of other traffic participants to determine their own driving strategies [108].”
Metadata
- Authors
- Huang, Y., Du, J., Yang, Z., Zhou, Z., Zhang, L., & Chen, H.
- Venue
- IEEE Transactions on Intelligent Vehicles
- Published
- 2022-09
- Volume
- 7
- Issue
- 3
- Pages
- 652-674
- ISSN
- 2379-8858 (ISSN-L)
- 2379-8904
- OpenAlex
- W4226239849
- Cited by
- 677
- References
- 161
- FWCI
- 37.33
- Type
- journal article
- Language
- en
- Open access
- closed
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