Abstract
Trajectory prediction in urban mixed-traffic zones (a.k. a. shared spaces) is critical for many intelligent transportation systems, such as intent detection for autonomous driving. However, there are many challenges to predict the trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles) at a microscopical level. For example, an agent might be able to choose multiple plausible paths in complex interactions with other agents in varying environments. To this end, we propose an approach named Multi-Context Encoder Network (MCENET) that is trained by encoding both past and future scene context, interaction context and motion information to capture the patterns and variations of the future trajectories using a set of stochastic latent variables. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future. Through experiments on several datasets of varying scenes, our method outperforms some of the recent state-of-the-art methods for mixed traffic trajectory prediction by a large margin and more robust in a very challenging environment. The impact of each context is justified via ablation studies.
Original language | English |
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Title of host publication | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems |
Subtitle of host publication | ITSC 2020 |
Publisher | IEEE |
Pages | 1-8 |
Number of pages | 8 |
ISBN (Electronic) | 9781728141497 |
ISBN (Print) | 9781728141503 |
DOIs | |
Publication status | Published - 20 Sept 2020 |
Event | 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Virtual Conference, Greece Duration: 20 Sept 2020 → 23 Sept 2020 Conference number: 23 |
Conference
Conference | 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 |
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Abbreviated title | ITSC 2020 |
Country/Territory | Greece |
City | Virtual Conference |
Period | 20/09/20 → 23/09/20 |
Keywords
- 2021 OA procedure