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cs.CV2020★ 1 cited
Attentional Bottleneck: Towards an Interpretable Deep Driving Network
Jinkyu Kim, Mayank Bansal
Deep neural networks are a key component of behavior prediction and motion generation for self-driving cars. One of their main drawbacks is a lack of transparency: they should prov…
cs.CV2020★ 44 cited
VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
Jiyang Gao, Chen Sun, Hang Zhao +4
Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road compone…
cs.CV2020★ 9 cited
STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction
Zhishuai Zhang, Jiyang Gao, Junhua Mao +3
Detecting pedestrians and predicting future trajectories for them are critical tasks for numerous applications, such as autonomous driving. Previous methods either treat the detect…