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

@article{Chou2020PredictingMO,
  title={Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets},
  author={Fang-Chieh Chou and Tsung-Han Lin and Henggang Cui and Vladan Radosavljevic and Thi Nguyen and Tzu-Kuo Huang and Matthew Niedoba and Jeff G. Schneider and Nemanja Djuric},
  journal={2020 IEEE Intelligent Vehicles Symposium (IV)},
  year={2020},
  pages={1655-1662}
}
Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment. This is particularly important when it comes to vulnerable road users (VRUs), such as pedestrians and bicyclists. These actors need to be handled with special care due to an increased risk of injury, as well as the fact that their behavior is less predictable than… 
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