Leveraging Multi-Stream Information Fusion for Trajectory Prediction in Low-Illumination Scenarios: A Multi-Channel Graph Convolutional Approach

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. The paper proposes a novel approach for trajectory prediction in low-illumination scenarios by leveraging multi-stream information fusion, which integrates image, optical flow, and object trajectory information. This is achieved by applying Convolutional Neural Network-based (CNN) Long Short-term Memory (LSTM) networks to extract temporal information from the image channel, Spatial-Temporal Graph Convolutional Network (ST-GCN) to model relative motion between adjacent camera frames through the optical flow channel, and recognizing high-level interactions between vehicles in the trajectory channel. Further, to investigate the reliability of the model in low-illumination scenarios, epistemic uncertainty estimation is conducted by applying Monte Carlo Dropout. The proposed approach is validated on HEV-I and newly generated Dark-HEV-I datasets focusing on graph-based interaction understanding and low illumination conditions. The experimental results show improved performance compared to baselines in both standard and low-illumination scenarios. Importantly, our approach is generic and applicable to scenarios with different types of perception data. The source code is available at
https://github.com/TommyGong08/MSIF
.