Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving

Nemanja Djuric, Vladan Radosavljevic, Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, NITIN SINGH, Jeff Schneider; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 2095-2104

Abstract


We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings. We introduce a deep learning-based approach that takes into account a current world state and produces raster images of each actor's vicinity. The rasters are then used as inputs to deep convolutional models to infer future movement of actors while also accounting for and capturing inherent uncertainty of the prediction task. Extensive experiments on real-world data strongly suggest benefits of the proposed approach. Moreover, following successful tests the system was deployed to a fleet of autonomous vehicles.

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[bibtex]
@InProceedings{Djuric_2020_WACV,
author = {Djuric, Nemanja and Radosavljevic, Vladan and Cui, Henggang and Nguyen, Thi and Chou, Fang-Chieh and Lin, Tsung-Han and SINGH, NITIN and Schneider, Jeff},
title = {Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}