Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TIV.2022.3167103 | IEEE Transactions on Intelligent Vehicles |
Keywords | DocType | Volume |
Autonomous driving,trajectory prediction,machine learning,deep learning,reinforcement learning | Journal | 7 |
Issue | ISSN | Citations |
3 | 2379-8858 | 1 |
PageRank | References | Authors |
0.34 | 67 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| yanjun huang | 1 | 1 | 0.68 |
| jiatong du | 2 | 1 | 0.34 |
| ziru yang | 3 | 1 | 0.34 |
| zewei zhou | 4 | 1 | 0.34 |
| Lin Zhang | 5 | 104 | 51.47 |
| Hong Chen | 6 | 280 | 56.04 |