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Henggang Cui

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0 BLOG ARTICLES 4 RESEARCH PAPERS

Research Papers

Improving Movement Prediction of Traffic Actors using Off-road Loss and Bias Mitigation

M. Niedoba, H. Cui, K. Luo, D. Hegde, F.-C. Chou, N. Djuric
In this work improves the predictions for traffic actor with two novel methods: off-road losses and action category upweighting. The off-road losses compliment the traditional L2 distance loss by penalizing the unrealistic off-road predictions. [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019

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

N. Djuric, V. Radosavljevic, H. Cui, T. Nguyen, F.-C. Chou, T.-H. Lin, N. Singh, J. Schneider
We introduce an approach that takes into account a current world state and produces rasterized representations of each traffic actor's vicinity. The raster images are then used as inputs to deep convnets to infer future movement of actors while also accounting for and capturing inherent uncertainty of the prediction task, with extensive experiments on real-world data strongly suggest benefits of the proposed approach. [PDF]
Winter Conference on Applications of Computer Vision (WACV), 2020

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

F. Chou, T.-H. Lin, H. Cui, V. Radosavljevic, T. Nguyen, T. Huang, M. Niedoba, J. Schneider, N. Djuric
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. [...] [PDF]
MLITS workshop @ Neural Information Processing Systems (NeurIPS), 2018

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

H. Cui, V. Radosavljevic, F. Chou, T.-H. Lin, T. Nguyen, T. Huang, J. Schneider, N. Djuric
Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the livelihood and life quality of many more. [...] [PDF]
International Conference on Robotics and Automation (ICRA), 2019

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