Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of
  Traffic Agents in Risky Driving Videos
Muhammad Monjurul Karim Ruwen Qin Yinhai Wang
To ensure the safe and efficient navigation of autonomous vehicles and
advanced driving assistance systems in complex traffic scenarios, predicting
the future bounding boxes of surrounding traffic agents is crucial. However,
simultaneously predicting the future location and scale of target traffic
agents from the egocentric view poses challenges due to the vehicle's egomotion
causing considerable field-of-view changes. Moreover, in anomalous or risky
situations, tracking loss or abrupt motion changes limit the available
observation time, requiring learning of cues within a short time window.
Existing methods typically use a simple concatenation operation to combine
different cues, overlooking their dynamics over time. To address this, this
paper introduces the Fusion-Gated Recurrent Unit (Fusion-GRU) network, a novel
encoder-decoder architecture for future bounding box localization. Unlike
traditional GRUs, Fusion-GRU accounts for mutual and complex interactions among
input features. Moreover, an intermediary estimator coupled with a
self-attention aggregation layer is also introduced to learn sequential
dependencies for long range prediction. Finally, a GRU decoder is employed to
predict the future bounding boxes. The proposed method is evaluated on two
publicly available datasets, ROL and HEV-I. The experimental results showcase
the promising performance of the Fusion-GRU, demonstrating its effectiveness in
predicting future bounding boxes of traffic agents.