  Trajectory prediction is a fundamental problem and challenge for autonomous
vehicles. Early works mainly focused on designing complicated architectures for
deep-learning-based prediction models in normal-illumination environments,
which fail in dealing with low-light conditions. This paper proposes a novel
approach for trajectory prediction in low-illumination scenarios by leveraging
multi-stream information fusion, which flexibly integrates image, optical flow,
and object trajectory information. The image channel employs Convolutional
Neural Network (CNN) and Long Short-term Memory (LSTM) networks to extract
temporal information from the camera. The optical flow channel is applied to
capture the pattern of relative motion between adjacent camera frames and
modelled by Spatial-Temporal Graph Convolutional Network (ST-GCN). The
trajectory channel is used to recognize high-level interactions between
vehicles. Finally, information from all the three channels is effectively fused
in the prediction module to generate future trajectories of surrounding
vehicles in low-illumination conditions. The proposed multi-channel graph
convolutional approach is validated on HEV-I and newly generated Dark-HEV-I,
egocentric vision datasets that primarily focus on urban intersection
scenarios. The results demonstrate that our method outperforms the baselines,
in standard and low-illumination scenarios. Additionally, our approach is
generic and applicable to scenarios with different types of perception data.
The source code of the proposed approach is available at
https://github.com/TommyGong08/MSIF}{https://github.com/TommyGong08/MSIF.
