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Conference Paper: TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
Title | TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents |
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Authors | |
Issue Date | 2019 |
Publisher | AAAI Press. The Journal's web site is located at https://aaai.org/Library/AAAI/aaai-library.php |
Citation | Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu,Hawaii, USA, 27 January – 1 February 2019, v. 33 n. 1, p. 6120-6127 How to Cite? |
Abstract | To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods. |
Description | Section: AAAI Technical Track: Multiagent Systems |
Persistent Identifier | http://hdl.handle.net/10722/294206 |
ISSN |
DC Field | Value | Language |
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dc.contributor.author | Ma, Y | - |
dc.contributor.author | Zhu, X | - |
dc.contributor.author | Zhang, S | - |
dc.contributor.author | Yang, R | - |
dc.contributor.author | Wang, WP | - |
dc.contributor.author | Manocha, D | - |
dc.date.accessioned | 2020-11-23T08:27:56Z | - |
dc.date.available | 2020-11-23T08:27:56Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu,Hawaii, USA, 27 January – 1 February 2019, v. 33 n. 1, p. 6120-6127 | - |
dc.identifier.issn | 2159-5399 | - |
dc.identifier.uri | http://hdl.handle.net/10722/294206 | - |
dc.description | Section: AAAI Technical Track: Multiagent Systems | - |
dc.description.abstract | To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods. | - |
dc.language | eng | - |
dc.publisher | AAAI Press. The Journal's web site is located at https://aaai.org/Library/AAAI/aaai-library.php | - |
dc.relation.ispartof | Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-19, IAAI-19, EAAI-20) | - |
dc.title | TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wang, WP: wenping@cs.hku.hk | - |
dc.identifier.authority | Wang, WP=rp00186 | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1609/aaai.v33i01.33016120 | - |
dc.identifier.hkuros | 319299 | - |
dc.identifier.volume | 33 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 6120 | - |
dc.identifier.epage | 6127 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2159-5399 | - |