Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data

The Tactical Driver Behavior modeling problem requires an understanding of driver actions in complicated urban scenarios from rich multimodal signals including video, LiDAR and CAN signal data streams. However, the majority of deep learning research is focused either on learning the vehicle/environment state (sensor fusion) or the driver policy (from temporal data), but not both. Learning both tasks jointly offers the richest distillation of knowledge but presents challenges in the formulation and successful training. In this work, we propose promising first steps in this direction. Inspired by the gating mechanisms in Long Short-Term Memory units (LSTMs), we propose Gated Recurrent Fusion Units (GRFU) that learn fusion weighting and temporal weighting simultaneously. We demonstrate it's superior performance over multimodal and temporal baselines in supervised regression and classification tasks, all in the realm of autonomous navigation. On tactical driver behavior classification using Honda Driving Dataset (HDD), we report 10% improvement in mean Average Precision (mAP) score, and similarly, for steering angle regression on TORCS dataset, we note a 20% drop in Mean Squared Error (MSE) over the state-of-the-art.