Map-aware Trajectory Prediction
Map-Aware Trajectory Prediction is a trajectory prediction framework that incorporates spatial and semantic information from high-definition maps to constrain and inform motion forecasts, distinguishing itself from generic trajectory prediction methods by explicitly leveraging lane topology, road constraints, and environmental context to improve prediction accuracy and plausibility.
17
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2.2K
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66
Authors
23
Institutions
Publications per year
2017–2026
11
Parents
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Argoverse: 3D Tracking and Forecasting With Rich Maps
Ming-Fang Chang, John Lambert, Patsorn Sangkloy et al. · 2019 · 1.4K citations
Argoverse: 3D Tracking and Forecasting with Rich Maps
Ming-Fang Chang, John Lambert, Patsorn Sangkloy et al. · arXiv (Cornell University) · 2019 · 157 citations · Full text
Egocentric Future Localization
Hyun Soo Park, Jyh-Jing Hwang, Yedong Niu et al. · 2016 · 105 citations
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Hanyang University
Seoul, South Korea
2 h-index
5 publications
147 citations
Institute of Communication and Computer Systems
Zografos, Greece
1 h-index
3 publications
45 citations
Technische Hochschule Ulm
Ulm, Germany
1 h-index
1 publications
74 citations
Ford (Germany)
Cologne, Germany
1 h-index
1 publications
15 citations
California University of Pennsylvania
California, United States
1 h-index
4 publications
420 citations
Mercedes-Benz (Germany)
Stuttgart, Germany
1 h-index
3 publications
222 citations
Centre for Artificial Intelligence and Robotics
Bengaluru, India
1 h-index
1 publications
75 citations

University of Twente
Enschede, The Netherlands
1 h-index
1 publications
44 citations
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2 publications · 52 citations
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Repository · 2 publications · 168 citations
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1 publications · 44 citations
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1 publications · 31 citations
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Repository · 1 publications · 40 citations
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