Lin Che — accepted AI-conference papers

2026

UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations

Dominik J. Mühlematter, Lin Che, Ye Hong, Martin Raubal, Nina Wiedemann

ICML 2026poster

Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data. Current methods primarily utilize task-specific models, while recent generic models for spatial representations often support only limited modalities and lac…

Cited by 2 Source ↗Scholar ↗

2021

Feasible and Adaptive Multimodal Trajectory Prediction with Semantic Maneuver Fusion

Hendrik Berkemeyer, Riccardo Franceschini, Tuan Tran, Lin Che, Gordon Pipa

ICRA 2021poster

Predicting trajectories of participating vehicles is a crucial task towards full and safe autonomous driving. General unconstrained machine learning methods often report unrealistic predictions, and need to be combined with different motion constraints. Existing work either defines some shallow mane…

Cited by 12 Source ↗Scholar ↗