Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets

Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV), allowing the SDV to move safely and efficiently in its environment. This is particularly important when it comes to vulnerable road users (VRUs), such as pedestrians and bicyclists. We present a deep learning method for predicting VRU movement where we rasterize high-definition maps and actor’s surroundings into bird’s-eye view image used as input to convolutional networks. In addition, we propose a fast architecture suitable for real-time inference, and present an ablation study of rasterization choices.

Fang-Chieh Chou, Tsung-Han Lin, Henggang Cui, Vladan Radosavljevic, Thi Nguyen, Tzu-Kuo Huang, Matthew Niedoba, Jeff Schneider, Nemanja Djuric

MLITS workshop @ NeurIPS 2018

‘Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets’ (PDF)

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